Climate change has caused range shifts and extinctions of many species in the past. In this study, the effects of climate change on Egyptian reptiles, as a representative of the Egyptian fauna, was investigated for the first time using species distribution models, relatively new tools now used in a variety of fields from conservation planning to the assessment of species’ responses to climate change. In this study, the Maxent algorithm was used to model the current and future distributions of 75 terrestrial reptile species from Egypt. The modelled distribution for current conditions for each species was projected into the future for three time slices (2020, 2050 and 2080) using two emission scenarios (A2a and B2a) from four global circulation models (CCCma, CSIRO, HadCM3 and NIES99) and under two assumptions of dispersal ability (unlimited dispersal and no dispersal). This produced a total of 48 projections for each species. Current and future species richness patterns were determined from the results using the average response across the different global circulation models to represent a consensus view. For each species, possible changes in range were calculated and used to assess future threat status. A national Red Data listing for the Egyptian reptiles was determined to show which species require more conservation measures. Zonation software was used for conservation prioritization to show which areas require more protection under current and future climates, and to assess the effectiveness of Egypt’s Protected Areas network to conserve reptiles.
Climate change is predicted to vary in its effects spatially, with some areas characterized by increased species richness while others show declines. Future range changes are predicted to vary among species and among different future projections, from the loss of the entire range (Tarentola mindiae and Hemidactylus robustus) to large gains in range (Hemidactylus flaviviridis). No species was predicted to lose its entire currently suitable range under all scenarios. Tarentola mindiae and Hemidactylus robustus were predicted to become extinct from Egypt in the future in at least one future projection. Another eight species were predicted to lose more than 80% of their current distribution in the future. According to IUCN guidelines and criteria, under current conditions, three species were classified as nationally Endangered and 24 species as Vulnerable. Although Protected Areas have greater conservation value compared to unprotected areas, Egypt’s Protected-Areas network seems to be inadequate to conserve Egyptian reptiles. My results suggest the need to construct new Protected Areas in a variety of places across northern Egypt from between Mersa Matruh and Sallum to the Gebel El-Hallal area in northern Sinai. Some Protected Areas require stricter protection in the future to counter the threats derived from climate change.
Supervisor: Prof. Francis Gilbert, School of Life Sciences, Nottingham University, Nottingham, UK.
By: Ahmed El-Gabbas
Supervisor: Dr. Francis Gilbert
A thesis presented to the School of Biology, The University of Nottingham in partial fulllment of the requirements for the degree of Master by Research (Conservation Biology).
The University of Nottingham, School of Biology,
September, 2012 https://elgabbas.github.io/ II
Abstract
Climate change has caused range shifts and extinctions of many species in the past. In this study, the effects of climate change on Egyptian reptiles, as a representative of the Egyptian fauna, was investigated for the first time using species distribution models, relatively new tools now used in a variety of fields from conservation planning to the assessment of species’ responses to climate change. In this study, the Maxent algorithm was used to model the current and future distributions of 75 terrestrial reptile species from Egypt. The modelled distribution for current conditions for each species was projected into the future for three time slices (2020, 2050 and 2080) using two emission scenarios (A2a and B2a) from four global circulation models (CCCma, CSIRO, HadCM3 and NIES99) and under two assumptions of dispersal ability (unlimited dispersal and no dispersal). This produced a total of 48 projections for each species. Current and future species richness patterns were determined from the results using the average response across the different global circulation models to represent a consensus view. For each species, possible changes in range were calculated and used to assess future threat status. A national Red Data listing for the Egyptian reptiles was determined to show which species require more conservation measures. Zonation software was used for conservation prioritization to show which areas require more protection under current and future climates, and to assess the effectiveness of Egypt’s Protected Areas network to conserve reptiles. Climate change is predicted to vary in its effects spatially, with some areas characterized by increased species richness while others show declines. Future range changes are predicted to vary among species and among different future projections, from the loss of the entire range (Tarentola mindiae and Hemidactylus robustus) to large gains in range (Hemidactylus flaviviridis). No species was predicted to lose its entire currently suitable range under all scenarios. Tarentola mindiae and Hemidactylus robustus were predicted to become extinct from Egypt in the future in at least one future projection. Another eight species were predicted to lose more than 80% of their current distribution in the future. According to IUCN guidelines and III
criteria, under current conditions, three species were classified as nationally
Endangered and 24 species as Vulnerable.
Although Protected Areas have greater conservation value compared to
unprotected areas, Egypt’s Protected-Areas network seems to be inadequate
to conserve Egyptian reptiles. My results suggest the need to construct new
Protected Areas in a variety of places across northern Egypt from between
Mersa Matruh and Sallum to the Gebel El-Hallal area in northern Sinai. Some
Protected Areas require stricter protection in the future to counter the threats
derived from climate change.
Keywords: Climate Change; Conservation prioritization; Egyptian herpetofauna; Egyptian reptiles; Maxent; Species Distribution Modelling; Species extinctions; Species range change; Species richness; Species Turnover; Zonation.
IV
Acknowledgement
First of all, I sincerely thank Allah Almighty for giving me strength and ability to
complete this study.
I would like to express my deep gratitude to my supervisor, Dr. Francis
Gilbert, for his great support, advice and mentorship throughout this study. I
am particularly grateful to him and Prof. Samy Zalat (Suez Canal University,
Egypt) for their enthusiasm, encouragement and guidance through the past
several years since working under their supervision at the BioMAP project.
This thesis would not have been completed without their support.
Many thanks are given for: Dr. Tim Newbold (UNEP-WCMC and
Microsoft Research Fellow) for introducing GIS and Species Distribution
Modelling for me, giving me a chance to join his PhD fieldwork in Egypt and for
sharing his field records; Dr. Sherif Baha El Din for providing his valuable
database records of the Egyptian herpetofauna; Dr. Moustafa Fouda (former
director of the Nature Conservation Sector, Egyptian Environmental Affairs
Agency) for his support throughout my work with BioMAP project and Nature
Conservation Sector; Mindy Baha El Din for providing valuable information on
the current status of the Egyptian tortoise; Abdelwahab Afefe and Mahmoud
Boghdady (Nature Conservation Sector) for providing me of the coordinates of
the latest declared Protected Areas (Mount Kamel Meteor and El-Wahat El-
Bahreya); BioMAP former staff and Nature Conservation Sector team; and all
the scientists and volunteers who contributed data to the databases used.
This research study is sponsored by Chevening Scholarships. I am
indebted to British Council in Egypt and the UK Foreign and Commonwealth
Office for their generous support throughout my stay in Britain.
Finally, I would like to thank my parents and sisters for their continuous
support, encouragement and patience.
V
“Dedicated to the soul of Prof. Mohamed El Kassas, the outstanding Egyptian naturalist and doyen of wildlife conservation in Egypt, who passed away earlier this year. ”
VI
Table of Contents
Abstract …………………………………………………………………………………………. II Acknowledgement ………………………………………………………………………….. IV List of Tables…………………………………………………………………………………… VIII List of Figures ………………………………………………………………………………… IX List of Acronyms …………………………………………………………………………….. XII
Introduction …………………………………………………………………………………… 1 Issues about the data required for distribution models ……………….. 3 The choice of model algorithms ……………………………………………….. 6 Assessing model performance …………………………………………………. 7 Maximum entropy modelling (Maxent) ………………………………………. 13 Climate change and biodiversity ………………………………………………. 18 Relevance to Egypt ………………………………………………………………….. 21 Aim of study …………………………………………………………………………… 24
Methods ………………………………………………………………………………………… 25 Study area – Egypt …………………………………………………………………. 25 Study species – Egyptian reptiles …………………………………………….. 25 Environmental predictor variables …………………………………………….. 27 Species distribution modelling ………………………………………………….. 39 Comparisons across species …………………………………………………… 42 Area prioritization for conservation ……………………………………………. 46
Results ………………………………………………………………………………………….. 50 Model performance …………………………………………………………………. 50 Most influential environmental variables ……………………………………. 50 Species richness …………………………………………………………………….. 51 Species Gains and Losses ………………………………………………………. 55 Species Turnover ……………………………………………………………………. 58 Range Changes ……………………………………………………………………… 59 Reptile species classifications ………………………………………………….. 65 Reptile records in Protected Areas …………………………………………… 66 Area prioritization for conservation ……………………………………………. 67 VII
Discussion …………………………………………………………………………………….. 106 Model performance ………………………………………………………………….. 106 Variables contributing to the models …………………………………………. 107 Species richness and turnover ………………………………………………….. 109 Range Changes & important species for conservation ………………… 112 Area prioritization for conservation & Protected Areas coverage ….. 121 The limitations of projecting into the future ………………………………… 126 Conclusion ……………………………………………………………………………… 127
References ……………………………………………………………………………………… 131
Appendices …………………………………………………………………………………….. 148
VIII
List of Tables
Table 1: The confusion matrix ……………………………………………………………. 9 Table 2: Some alternative criteria commonly used to convert probability distributions to binary forms (thresholded) …………………………………. 10 Table 3: Some measures of model accuracy …………………………………………. 11 Table 4: Estimates of relative contributions of the environmental variables to an example model …………………………………………………………………… 16 Table 5: A list of species used in this study (with the number of records for each species, their classification according to IUCN (global and national status), world status, and status in Egypt) …………………………………………… 33 Table 6: A list of 19 bioclimatic variables available at worldclim website …….. 36 Table 7: List of variables used to calculate VIF values …………………………….. 37 Table 8: Different scores of different parameters used to calculate relative species weight ………………………………………………………………………………. 48 Table 9: Mean and standard deviation of AUC values for each species ……… 71 Table 10: Species classification according to future species range change; assuming unlimited dispersal …………………………………………………. 88 Table 11: Species classification according to future species range change; assuming no-dispersal ………………………………………………………….. 90 Table 12: Number of species at each species range change category (assuming unlimited dispersal) ………………………………………………………………. 92 Table 13: Number of species at each species range change category (assuming no- dispersal) …………………………………………………………………………… 92 Table 14: Number of reptile species currently recorded or predicted and the number of predicted future species loss (UD and ND) or gain (UD) in each Protected Areas ………………………………………………………………….. 100
IX
List of Figures
Figure 1: Area under curve (AUC) ………………………………………………………… 12 Figure 2: The results of the jackknife test of variable importance ………………… 17 Figure 3: Response curves: how each environmental variable affects the Maxent prediction …………………………………………………………………………… 17 Figure 4: A map showing the outline of Egypt’s political boundaries overlain with the main cities and geographical locations used in this study……………… 30 Figure 5: A map showing the outline of Egypt’s political boundaries overlain with the Protected Areas …………………………………………………………………… 31 Figure 6: The distribution of Egyptian reptiles before and after 1950 ……………. 32 Figure 7: The distribution of all Egyptian reptile records and the number of records per grid square at a scale of a ¼ of a degree …………………………….. 32 Figure 8: The distribution of the weather stations used to interpolate the bioclimatic variables…………………………………………………………………………….. 36 Figure 9: Two examples of bio-layers variables excluded before calculating VIF statistics because they do not provide enough information to the model ………………………………………………………………………………………… 36 Figure 10: Calculation of future species gain or lost areas ………………………….. 43 Figure 11: Box-and-whiskers plot for mean AUC values across 75 studied species ………………………………………………………………………………………… 73 Figure 12: Frequency distribution of species mean AUC values showing the number of species at different ranges of mean AUC values …………………….. 73 Figure 13: The correlation between species mean AUC value and its predicted area of suitable habitats ……………………………………………………………….. 73 Figure 14: The correlation between species mean AUC value and its current extent of occurrence………………………………………………………………………. 74 Figure 15: The correlation between species mean AUC value and the number of its unique recorded points used to run the models ………………………….. 74 X
Figure 16: Relative contribution of environmental variables to the final model, represented by the mean value of average permutation importance across species………………………………………………………………………………. 75 Figure 17: The number of species at which each environmental variable was considered as the most influential environmental variable …………….. 75 Figure 18: Mean predicted reptile species richness using the summation of species predicted probability distributions (current and future – assuming unlimited dispersal) …………………………………………………………………………… 76 Figure 19: Future potential species richness change as a result of anthropogenic climate change (using species probability distributions – assuming unlimited dispersal) ………………………………………………………………. 77 Figure 20: Mean predicted reptile species richness using the summation of species thresholded predicted distributions (current and future - assuming unlimited dispersal) ………………………………………………………………. 78 Figure 21: Future potential species richness change as a result of anthropogenic climate change (using predicted species thresholded distributions – assuming unlimited dispersal) ………………………………………………… 79 Figure 22: Mean predicted reptile species richness using the summation of species thresholded predicted distributions (current and future - assuming no- dispersal) ………………………………………………………………………….. 80 Figure 23: Future potential species richness change (decline) as a result of anthropogenic climate change (using predicted species thresholded distributions – assuming no-dispersal) ……………………………………… 81 Figure 24: Potential future species loss as a result of anthropogenic climate change assuming both dispersal assumptions………………………………………. 82 Figure 25: Relative potential future species loss assuming both dispersal assumptions ……………………………………………………………………….. 83 Figure 26: Potential future species gain as a result of anthropogenic climate change assuming unlimited dispersal………………………………………………….. 84 Figure 27: Relative potential future species gain assuming unlimited dispersal .. 85 Figure 28: Future species turnover assuming unlimited dispersal …………………. 86 Figure 29: Future species turnover assuming no-dispersal …………………………. 87 XI
Figure 30: Number of species at each future species range change classification across global circulation models and the mean of different global circulation models (loss – unlimited dispersal) ……………………………. 93 Figure 31: Number of species at each future species range change classification across global circulation models and the mean of different global circulation models (gain – unlimited dispersal) …………………………… 94 Figure 32: Number of species at each future species range change classification across global circulation models and the mean of different global circulation models (loss – no-dispersal) ……………………………………. 95 Figure 33: Percentage of each future specie range change classification assuming unlimited dispersal and no-dispersal ………………………………………… 96 Figure 34: The overall pattern of future mean species range change under unlimited and no-dispersal assumptions ………………………………………………… 97 Figure 35: Average future range change across different taxonomic groups assuming either unlimited dispersal or no-dispersal assumptions ………………… 98 Figure 36: The correlation between number of recorded and predicted species per Protected Area ……………………………………………………………………. 99 Figure 37: The correlation between the area of the Protected Area (in 100 km 2 ) and either of the number of recorded or predicted species per Protected Area ………………………………………………………………………………………… 99 Figure 38: Current and future conservation prioritization ranked value (using Zonation algorithm - Additive benefit function) ……………………………. 101 Figure 39: Future change in conservation prioritization value (using Zonation algorithm - Additive benefit function) ………………………………………… 102 Figure 40: Current and future conservation prioritization ranked value (using Zonation algorithm – Core-Area function)………………………………….. 103 Figure 41: Future change in conservation prioritization value (using Zonation algorithm – Core-Area function) ………………………………………………. 104 Figure 42: Mean prioritization value (± 95% confidence limits - using Additive benefit function) across Protected Areas and non-Protected areas at current and future ………………………………………………………………………………… 105 XII
Figure 43: Mean prioritization value (± 95% confidence limits - using Core-Area function) across Protected Areas and non-Protected areas at current and future ………………………………………………………………………………… 105 Figure 44: The number of recorded amphibian and non-marine reptile species per a grid of half degree, comparing the results of (Baha El Din 2006a) to the results of this study ………………………………………………………………. 109 Figure 45: Accumulated species richness map of three taxonomic groups (butterflies, mammals and reptiles), showing areas of high species richness ……. 110 Figure 46: Maps showing actual distribution and suitable distribution areas for some Egyptian reptile species ……………………………………………………….. 128 Figure 47: Average MESS (Multivariate Environmental Similarity Surfaces) maps of different global circulation models showing areas of future novel climates ………………………………………………………………………………………… 130
XIII
List of Acronyms
AOO Area of Occupancy AUC Area Under Curve BioMAP Biodiversity Monitoring and Assessing Project EOO Extent of Occurrence GCM Global Circulation Models GIS Geographical Information Systems GPS Global Positioning System IUCN International Union for Conservation of Nature VIF Variance Inflation Factor
Environmental variabes: NDVI Normalized Difference Vegetation Index Bio1 Annual Mean Temperature Bio2 Mean Diurnal Range (Mean of monthly (max temp - min temp)) Bio3 Isothermality (Bi2/Bio7) (* 100) Bio4 Temperature Seasonality (standard deviation *100) Bio5 Max Temperature of Warmest Month Bio6 Min Temperature of Coldest Month Bio7 Temperature Annual Range (Bio5-Bio6) Bio8 Mean Temperature of Wettest Quarter Bio9 Mean Temperature of Driest Quarter Bio10 Mean Temperature of Warmest Quarter Bio11 Mean Temperature of Coldest Quarter Bio12 Annual Precipitation Bio13 Precipitation of Wettest Month Bio14 Precipitation of Driest Month Bio15 Precipitation Seasonality (Coefficient of Variation) Bio16 Precipitation of Wettest Quarter Bio17 Precipitation of Driest Quarter Bio18 Precipitation of Warmest Quarter Bio19 Precipitation of Coldest Quarter
Detailed information on the ecological and geographical distributions of species is essential for conservation planning and forecasting (Elith et al. 2006) especially for species facing conservation problems (Pineda & Lobo 2009). Species distribution modelling is one of many methods used to quantify patterns of species distributions and to extrapolate distributions across space and time (Elith & Leathwick 2007; Franklin 2009), usually based on a statistical model (Franklin 2009). This is done, basically, through a class of methods that combine known species observations (occurrence or abundance at known locations) with layers of environmental variables (and/or spatial characteristics of those locations) that are thought to have an influence on habitat suitability (and so species distribution) to make a model of the environmental conditions that meet the ecological requirements of a species, thus identifying where these suitable conditions are distributed in space (Phillips et al. 2006; Pearson 2007; Elith & Leathwick 2009; Franklin 2009; Warren & Seifert 2011). The output of most models aims to provide detailed predicted distribution maps (Elith et al. 2006). The earliest attempts at modelling species distributions using correlations between the observed distribution and climate were by Johnston (1924), who predicted the invasive spread of cactus in Australia, and Hintikka (1963), who assessed the climatic determinants of the distribution of some European species based on the minimum and maximum temperatures (Pearson & Dawson 2003; Guisan & Thuiller 2005). Recent years have seen an explosion of interest in species distribution modelling, with the publishing of hundreds of studies and governmental and non-governmental reports that use them (Franklin 2009). Species distribution models have been used in the literature under many different names including bioclimatic models, climate envelopes, species niche models, ecological niche models, niche-theory models, habitat models, resource selection functions, range maps and spatial models (Elith & Leathwick 2009; Franklin 2009). They can also be referred to as habitat suitability models, since they are said to describe the suitability of the habitat for a particular species (Hirzel & Le Lay 2008; Franklin 2009).
Issues about the data required for distribution models Species distribution models require two sorts of data input: biological data describing the current known species distribution and predictor variables describing the environmental conditions thought to affect species distribution (Pearson 2007; Phillips & Dudik 2008). These two data input components are usually in the format suitable for visualizing on a Geographic Information System (GIS). The biological data usually come in the form of geo-databases containing the georeferenced records of the occurrences of species, while the predictor variables usually come in the form of GIS raster grids. The most common predictor variables are those related to climate (e.g. temperature, precipitation), topography (e.g. elevation, aspect, slope), soil type and land cover type (Phillips et al. 2006). Some studies also use variables describing the distribution of interacting species to improve model accuracy (Newbold 2010). Predictor variables come into two formats: continuous (any value within
The choice of model algorithms A wide variety of modelling algorithms has been used recently (Guisan & Thuiller 2005; Pearson 2007). Some methods are based on statistical regression methods (e.g. generalized linear models, generalized additive models, multivariate adaptive regression splines), whilst others depend on machine-learning methods (decision trees, artificial neural networks, genetic algorithms, maximum entropy and support vector machines) (Pearson 2007; Franklin 2009). Some of these algorithms have been developed in user- friendly free software (Pearson 2007) (e.g. Maxent). Algorithms that include the possibility of interactions between predictors are considered more suitable (Elith et al. 2006; Pearson 2007). Some studies have founded that different modelling algorithms can potentially produce different predictions (Pearson 2007; Pearson et al. 2007; Wisz et al. 2008), and that the best performing model is not always the same for all species (Luoto et al. 2005; Grenouillet et al. 2011), so it is important to select the most appropriate method for the species being studied (Pearson et al. 2007).
Assessing model performance Assessing model performance is a vital step in model development (Pearson 2007). It helps to determine the suitability of the model for particular applications, and to compare different models (Pearson 2007). Nevertheless, model performance is just one aspect of model evaluation, and other criteria should be considered, such as ecological realism, spatial pattern of error, and model credibility (acceptability to the user community) (Franklin 2009). It is preferable to use new independent data in model validation (Pearson 2007; Pearson et al. 2007; Franklin 2009). Sometimes this is done by collecting new independent data from different areas (e.g. Newbold et al. 2010), spatial resolution, or time period, or by using data from other surveys
Table 1: The confusion matrix
Observed Present Absent Predicted Present True Positive (TP) False Positive (FP - Commission) Absent False Negative (FN - Omission) True Negative (TN)
It is common in distribution modelling to convert from continuous output (probability of occurrence) to categorical (presence/absence or suitable/non- suitable), conventionally done by using a threshold value (Franklin 2009). Subsequently, so-called ’threshold-dependent’ measures can be used to evaluate models, or alternatively continuous probabilistic predictions can also be evaluated, so-called ’threshold-independent’ measures (Franklin 2009). The confusion matrix, and statistics used to test model performance derived from it, requires binary predictions (presence/absence). Thus it is often necessary to convert the probabilities obtained from continuous models to binary predictions by using this threshold (Pearson 2007). Various alternative methods have been employed to select the best value for the threshold (see Table 2 for some examples). The simplest is to use a fixed arbitrary value, above which the species is deemed present; by convention this threshold is set to 0.5 (Pearson 2007; Franklin 2009). However, this method is the worst and lacks any ecological justification or reasoning (Liu et al. 2005; Pearson 2007). Liu et al. (2005) evaluated the use of different threshold criteria, reporting that five threshold criteria performed equally well and better than the others: observed prevalence, average predicted probability, the sum of sensitivity and specificity, sensitivity specificity quality approach, and the point on the ROC plot nearest the upper left corner.
Table 2: Some alternative criteria commonly used to convert probability distributions to binary forms (thresholded).
Fixed threshold: e.g. 0.5
Sensitivity= Specificity
Maximum Sensitivity plus Specificity
Maximum Kappa
Maximum percent correct classification (PCC)
Predicted prevalence = observed prevalence
observed prevalence
mean or median predicted probability
minimizing the distance between the ROC curve and the upper left corner of
the ROC plot
Weighting omission and commission errors by their costs relative to a given
application of the model
Maximum Sensitivity (Specificity) that also achieves user-defined minimum
Specificity (Sensitivity)
Of the various ways of measuring model accuracy (Table 3), one simple and easy way is to calculate the percent correct classification, by dividing the sum of the diagonals of the confusion matrix (true positives and true negatives) by the number of the observations (Pearson 2007; Franklin 2009). Although its concept is easy and logic, there are two problems with it: first, it does not distinguish between false negatives and false positives; second, it is possible to get high accuracy using a poor model when a species’ prevalence (proportion or frequency) is relatively high or low (Pearson 2007; Franklin 2009). To overcome this problem, a similar measure of accuracy has been used, called Kappa, which is considered as a measure of categorical agreement that describes the difference between the observed agreement and chance agreement with the proportion of correct predictions expected by
Table 3: Some measures of model accuracy
Measure Calculation Sensitivity (true positive fraction)
( + )
False negative rate (1 – Sensitivity) Specificity
False positive rate (1 – Specificity) Model accuracy (% correct) ( + )
Positive predictive power
+
Odds ratio ( ) ( )
Kappa [( + ) − (( + )( + ) + ( + )( + )) ⁄ )] [ − (( + )( + ) + ( + )( + ) ⁄ )]
True Skill Statistic (TSS) sensitivity + specificity - 1
When model output is continuous and a particular threshold measure is used to convert it to categorical (binary) output, the statistics derived from the confusion matrix will be sensitive to the threshold method used (Pearson 2007). It would be useful to have a single measure of model performance that is independent of the threshold choice (Phillips et al. 2006); such measures could then be used to compare modelling methods, species, candidate predictors, etc. (Franklin 2009). One of the most frequently used such statistics in species distribution modelling is the AUC - the Area Under the ROC (receiver operating characteristic) Curve (Elith et al. 2006; Elith & Leathwick 2007). The AUC is derived from the receiver operating characteristic (ROC) curve (Baldwin 2009). The ROC curve is defined by plotting the false-positive error rate (1 - specificity) on the x-axis versus the true positive rate (sensitivity)
Fig. 1: Area under curve (AUC)
Maximum entropy modelling (Maxent) Maxent is a general-purpose machine-learning method for making inferences or predictions from incomplete information (Phillips et al. 2006; Baldwin 2009). It estimates the most uniform distribution (maximum entropy) subject to a set of constraints that represent our incomplete information about the target distribution (Phillips et al. 2006). It is being applied in a variety of fields including finance and astronomy (Franklin 2009). Since 2004 it has been applied extensively to model species distributions because it shows high predictive accuracy and enjoys several additional attractive properties (Phillips
Table 4: Estimates of relative contributions of the environmental variables to an example model
Variable Percent contribution Permutation importance bio19 31.2 9.6 bio6 19.8 18.9 bio2 12.1 1.8 bio9 11.2 6.3 bio13 8 5.2 bio7 3.6 0 bio18 3.2 1.4 bio16 2.6 3.3 bio4 2 25.3 bio11 1.3 0.4 bio5 1.3 16 bio8 1.1 1.8 bio1 1 0.2 bio10 0.8 8.1 bio3 0.7 1.5 bio12 0.1 0 bio17 0.1 0 bio15 0 0 bio14 0 0
Fig. 2: The results of the jackknife test of variable importance.
Fig. 3: Response curves: how each environmental variable affects the Maxent prediction.
Relevance to Egypt Predicted impacts of climate change on Egypt include the raising of the sea level (which will affect people living in Nile Delta and coastal areas in Egypt
22 -
and also will make high percentage of Egypt flora and fauna vulnerable to
extinction), changes in the distribution of vector-borne infectious diseases, and
reduction in the productivity of major crops (Parry et al. 2007; Tolba & Saab
2009). It also predicted that Egypt will be vulnerable to water stress and
increase of water requirements, uncertainty of Nile flow, and increase in
irrigation demand under climate change (Parry et al. 2007). The water issue is
a serious problem facing Egypt in the future because the country is mostly arid
and depends on the Nile as the main source of drinking and irrigation water.
Tourism, one of the main sources of Egypt National Income, is also
predicted to be affected in the future by climate change (Tolba & Saab 2009).
The number of tourists coming to Egypt’s coasts each year is expected to
decline because of the potential impact of climate change on coral reefs.
Worldwide, almost all coral reefs have been affected by climate-change-
induced coral bleaching at one time or another (Hannah 2011). Corals depend
on a certain kind of algae (zooxanthellae), using the nutrients they produce
while providing the algae with physical support, protection and maintenance in
adequate level of sunlight for photosynthesis (Hannah 2011). When exposed
to high water temperatures (more than 1 or 2° C rise in sea surface
temperature above normal summer maximal temperatures for a period longer
than 3–5 weeks), corals expel the algae, resulting in loss of coral color
(Hannah et al. 2005; Hannah 2011). Beside its conservation implications,
declines in the number of tourists will have great impact on Egypt National
Income and a relatively high proportion of Egyptians working in tourism will
suffer.
Climate change will potentially affect biodiversity and species
composition of Egypt ecosystems (Tolba & Saab 2009), although not enough
studies or data are available on this (see below). It is predicted that Egypt will
suffer from global changes in the distribution of flowering plants and pollinating
insects due to climate change, which are predicted to cause dramatic declines
in the ecosystem services they provide. We do have an estimate of the
potential costs of such declines: the annual cost of losing Egyptian pollinators
to the Egyptian National Income would be approximately L.E. 13.5 billion ($2.4
billion, 3.3% of the 2003 GDP) (Brading et al. 2009).
23 - Only a handful of studies have been conducted with the use of species distribution modelling techniques to predict the potential distribution of Egyptian fauna and the potential impacts of climate chance on them: there are no such published studies on Egyptian flora. This may be because that the models are relatively new and the availability of biodiversity data records of Egyptian fauna and flora are not well organized and sparse. Gilbert & Zalat (2008); Basuony et al. (2010), the main two publication of the BioMAP project (see: http://biomapegypt.org), discussed the distribution of each Egyptian butterfly and mammal species respectively. In these two books, data on each species were collated from available sources and an actual and predicted distribution map for each species were provided (using Maxent); each species was also assessed according to the IUCN guidelines and criteria. In another study, El Alqamy et al. (2010) used Maxent to predict the potential distribution of the Nubian Ibex (Capra nubiana) in South Sinai, and showed that the presence of water was the environmental factor most influential in their distribution in South Sinai. Three studies have discussed the effect of climate change on Egyptian species. Hoyle & James (2005) used an occupancy model (a type of population viability analysis) to assess the potential impacts of global warming on the world’s smallest butterfly, the Sinai Baton Blue (Pseudophilotes sinaicus). Just two recent studies have used species distribution models to assess the effect of climate change on Egypt biodiversity. Soultan (2011) used Maxent to test the potential impact of climate change on the distribution of Egyptian antelopes (Barbary sheep Ammotragus lervia, Nubian ibex Capra nubiana, Dorcas gazelle Gazella dorcas, and Slender-horned gazelle Gazella leptoceros) using the A2 and B2 emission scenarios of a Global Circulation Models. Leach (2011) discussed the effect of climate change on Egyptian butterflies and mammals (using Maxent algorithm and A2 and B2 emission scenarios of a different Global Circulation Models), and considered the effectiveness of the Protected Area network in Egypt in conserving Egypt biodiversity under current and future climates (using Zonation software). No studies have been published on the use of species distribution models to predict the potential distribution of Egyptian reptiles - the topic of this study.
24 - Aim of study
The main aims of this study are to: prepare a distribution map of the records for each reptile species; predict their potential distribution under the current climate; predict their future potential distribution under climate change using four global circulation models, two emission scenarios, three time slices and two assumptions about dispersal ability; estimate the pattern of species richness under current and future climates; estimate areas predicted to have highest future gains / losses / turnover of species; analyse the responses to climate change and assess future threat status for each species; assess current status according to IUCN guidelines and criteria; assess the priority areas for conservation of Egyptian reptiles; perform a gap analysis to show the effectiveness of Egypt’s Protected Areas network to conserve reptiles.
Study area – Egypt Egypt is located at the north eastern part of the continent of Africa; it is situated between latitudes 22° to 32° N and longitudes 24° to 37° E. It occupies an area of a little more than 1,000 kilometers square (1 million km 2 ), constituting about 3 per cent of Africa (Zahran & Willis 2009 - See Fig. 4 for the political boundaries of Egypt showing the main locations discussed in this study). Geographically, it can be divided into four units: the Nile Valley and Delta, Western Desert, Eastern Desert, and Sinai. Most of Egypt’s human population lives around the Nile and Delta because of the restricted availability of water and food elsewhere, which leaves a large proportion of Egypt’s land as desolate desert. Relatively small settlements are sparsely distributed in Western Desert oases, the Red Sea coast, and Sinai. Egypt is one of if not the most arid country in the world; average annual rainfall across Egypt is just 10 mm with a maximum of 200 mm at the Mediterranean coast (Zahran & Willis 2009). There are 30 Protected Areas in Egypt, compromising about 15% of the total area of Egypt (Fig. 5).
Study species – Egyptian reptiles According to (Baha El Din 2006a), the most recent comprehensive publication on the Egyptian herpetofauna, the contemporary Egyptian reptiles include at least 109 species: 61 lizards, 39 snakes, 1 crocodile, 7 turtles and a tortoise; in addition there is a recently separated species, Acanthodactylus aegyptius (Baha El Din 2007). Data on species distribution were compiled from various different sources. The main source was records extracted from the BioMAP project database [Biodiversity Monitoring and Assessment Project 2004-2008; see: http://www.biomapegypt.org/]; one of BioMAP’s main aims was to develop a comprehensive database of existing Egyptian biodiversity records derived mainly from international museum records, recent literature, and personal collections. For Egyptian reptiles, the collections were mainly from two well-known Egyptian herpetologists, Dr. Sherif Baha El Din and Dr.
Environmental predictor variables
Climate data for the near past (1950-2000) was downloaded from WorldClim
Global Climate Data v1.4 (release 3 - see:
http://www.worldclim.org) (Hijmans
et al. 2005). These data were considered to indicate the current climate
conditions. The Worldclim website provides a set of global environmental
layers (at different resolutions) in the form of 19 climate variables (bio-layers:
see Table 6) derived from precipitation and temperature records. Bio-layers
were clipped to the boundaries of Egypt using Clip tool in ArcMap 10 (ESRI,
USA). The resolution of 2.5 arc-minutes (~5 km square) was chosen to run the
distribution models because it is appropriate to the level of uncertainty that
accompanies the museum records (which form the majority of the records),
and because the climate data for Egypt were interpolated using relatively few
weather stations largely concentrated in the Nile Valley and Delta (Newbold
29 -
http://free.vgt.vito.be/). They are provided as 10-day synthetic maps at a
resolution of 1 km, and a total of 252 maps were obtained representing data
from 2004 to 2010. The maximum and minimum value of each pixel was
calculated across the full set of maps. Two layers were derived from and used
as predictors in the models: maximum NDVI value (indicating how much
vegetation there is per pixel), and the difference between maximum and
minimum NDVI value (indicating the degree of fluctuation in vegetation per
pixel). The layers were clipped to the boundaries of Egypt, and re-scaled to
2.5 arc-minutes resolution.
In order to reduce the negative effect of having high correlations
between predictor variables on model performance and the over-fitting
resulting from using many variables, the Variance Inflation Factor (VIF)
statistic was calculated among the continuous variables as a measure of
multicollinearity. The VIF statistic has been widely used (with other statistical
alternatives) as a measure of collinearity between continuous variables in
order to prune predictors before use (Farren et al. 2010; Fernández-Moya et
al. 2010; Bombi & D’Amen 2011; Bombi et al. 2012).
Four bio-layers were excluded prior to VIF calculations because they
show only minor variability in their values across Egypt (see Fig. 9) and hence
do not provide valuable information to the models. The excluded layers were:
Bio7 (temperature annual range), Bio14 (precipitation of driest month), Bio17
(precipitation of driest quarter), and Bio18 (precipitation of warmest quarter). In
variable Bio14, for example, almost all pixels were shown to have 0 value
except just 5 pixels that have a value of 1 (as shown in Fig. 9). This explicitly
reflects how arid Egypt is. Using these variables will not provide useful
information to the models and may just add extra noise, so they were excluded
before calculating the VIF statistic.
30 -
1 The Mediterranean Sea 25 Assiut 49 Ras El-Hekma
2 The Suez Gulf 26 Hurghada 50 Port-Said
3 The Aqaba Gulf 27 Ras Mohamed 51 Rafah
4 The Red Sea 28 Sharm El-Sheikh 52 Mersa Matruh
5 The Nile Delta 29 El-Minia 53 Rosetta
6 Lake Nasser 30 El-Tur 54 Damietta
7 Lake Brullus 31 Ras Gharib 55 Sallum
8 Lake Bardawil 32 Dahab 56 Sidi Barrani
9 Lake Manzala 33 Saint-Katherine 57 Kharga oasis
10 Lake Idku 34 Nuweiba 58 Dakhla oasis
11 Lake Mariut 35 Abu Zneima 59 Farafra oasis
12 Lake Qarun 36 Beni Suef 60 Bahariya oasis
13 Halayeb 37 Ras Zaafarana 61 Siwa oasis
14 Abu Ramad 38 Fayoum 62 Gebel Elba area
15 Al-Shalatein 39 Taba 63 El-Gilf El-Kebir
16 Berenice 40 Ain Sukhna 64 Gebel Abraq area
17 Aswan 41 Suez 65 Gebel El-Gallala El-Qibliya
18 Edfu 42 The greater Cairo 66 Gebel El-Gallala El-Bahariya
19 Mersa Alam 43 Wadi El-Natrun 67 Qattara Depression
20 Luxor 44 Ismailia 68 Gebel Yillaq
21 El-Quseir 45 El-Alamein 69 El-Hassana
22 Qena 46 El-Dabaa 70 Gebel El-Hallal
23 Sohag 47 El-Arish 71 Gebel El-Maghara
24 Safaga 48 Alexandria 72 Tiran & Sanafir islands
Fig. 4: A map showing the outline of Egypt’s political boundaries overlain with the main cities and geographical locations used in this study.
No. Protectorate Name Declaration Date Area Km² Governorate 1 Ras Mohamed National Park 1983 850 South Sinai 2 Zaranik Protectorate 1985 230 North Sinai 3 Ahrash Protectorate 1985 8 North Sinai 4 El-Omayed Protectorate 1986 700 Matrouh 5 Elba National Park 1986 35600 Red Sea 6 Saluga and Ghazal Protectorate 1986 0.5 Aswan 7 St. Katherine National Park 1988 4250 South Sinai 8 Ashtum El-Gamil Protectorate 1988 180 Port Said 9 Lake Qarun Protectorate 1989 250 El Fayoum 10 Wadi El-Rayan Protectorate 1989 1225 El Fayoum 11 Wadi Allaqi Protectorate 1989 30000 Aswan 12 Wadi El-Assuti Protectorate 1989 35 Assuit 13 El Hassana Dome Protectorate 1989 1 Giza 14 Petrified Forest Protectorate 1989 7 Cairo 15 Sannur Cave Protectorate 1992 12 Beni Suef 16 Nabq Protectorate 1992 600 South Sinai 17 Abu Galum Protectorate 1992 500 South Sinai 18 Taba Protectorate 1998 3595 South Sinai 19 Lake Burullus Protectorate 1998 460 Kafr El Sheikh 20 Nile Islands Protectorates * 1998 160 All Governorates on the Nile 21 Wadi Degla Protectorate 1999 60 Cairo 22 Siwa 2002 7800 Matrouh 23 White Desert 2002 3010 Matrouh 24 Wadi El-Gemal/Hamata 2003 7450 Red Sea 25 Red Sea Northern Islands * 2006 1991 Red Sea 26 El-Gilf El-Kebir 2007 48523 New Valley 27 El-Dababya 2007 1 Qena 28 El-Salum Gulf 2010 383 Matrouh 29 El-Wahat El-Bahreya 2010 109 6th October 30 Mount Kamel Meteor Protectorate 2012 1 New Valley
Fig. 5: A map showing the outline of Egypt’s political boundaries overlain with the Protected Areas. Protected Areas with * symbols are not shown in the map.
Fig. 6: The distribution of Egyptian reptiles before 1950 (left) and after 1950 (right).
(a) (b) Fig. 7: The distribution of all Egyptian reptile records (a) and the number of records per grid square at a scale of a ¼ of a degree (b).
N Species # records Global IUCN Egypt National IUCN World status Egypt Status 1 Cyrtopodion scabrum 37 LC LC Narrow Widespread 2 Hemidactylus flaviviridis 31 NA VU (D2) Narrow Narrow 3 Hemidactylus robustus 40 NA VU (D2) Narrow Narrow 4 Hemidactylus turcicus 217 LC LC Widespread Widespread 5 Pristurus flavipunctatus 80 NA VU (D2) Narrow Narrow 6 Ptyodactylus guttatus 115 NA LC Narrow Narrow 7 Ptyodactylus hasselquistii 225 NA LC Narrow Widespread 8 Ptyodactylus siphonorhina 180 NA LC Restricted Widespread 9 Stenodactylus mauritanicus 35 NA VU (D2) Restricted localized 10 Stenodactylus petrii 60 NA LC Narrow Widespread 11 Stenodactylus sthenodactylus 268 NA LC Narrow Widespread 12 Tarentola annularis 324 NA LC Narrow Widespread 13 Tarentola mauritanica 342 LC LC Widespread Narrow 14 Tarentola mindiae 43 LC VU (D2) Near- Endemic Narrow 15 Tropiocolotes bisharicus 20 NA VU (D2) Endemic Narrow 16 Tropiocolotes nattereri 34 NA LC Narrow Narrow 17 Tropiocolotes steudneri 197 NA LC Narrow Widespread 18 Tropiocolotes tripolitanus 24 LC LC Narrow Narrow 19 Agama spinosa 113 LC LC Narrow Narrow 20 Laudakia stellio 458 NA LC Narrow Narrow 21 Pseudotrapelus sinaitus 117 NA LC Narrow Widespread 22 Trapelus mutabilis 299 NA LC Narrow Widespread 23 Trapelus pallidus 173 NA LC Narrow Widespread 24 Trapelus savignii 86 VU (A2abcd) VU (D2) Near- Endemic Narrow
34 - N Species # records Global IUCN Egypt National IUCN World status Egypt Status 25 Uromastyx aegyptia 82 NA LC Narrow Widespread 26 Uromastyx ocellata 56 LC EN (B2 a,b i) Narrow Narrow 27 Uromastyx ornata 16 NA VU (D2) Restricted localized 28 Chamaeleo africanus 72 NA EN (B2 a,b i,iv) Narrow Narrow 29 Chamaeleo chamaeleon 262 NA LC Widespread Narrow 30 Acanthodactylus aegyptius 167 NA LC Near- Endemic Widespread 31 Acanthodactylus boskianus 1414 NA LC Narrow Widespread 32 Acanthodactylus longipes 50 NA VU (D2) Narrow Widespread 33 Acanthodactylus pardalis 191 VU - (A2c; B1ab (i,ii,iii)) VU (D2) Restricted Narrow 34 Acanthodactylus scutellatus 406 NA LC Narrow Widespread 35 Mesalina bahaeldini 98 LC VU (D2) Endemic localized 36 Mesalina guttulata 216 NA LC Narrow Widespread 37 Mesalina olivieri 172 NA LC Narrow Widespread 38 Mesalina pasteuri 19 NA VU (D2) Narrow localized 39 Mesalina rubropunctata 129 NA LC Narrow Widespread 40 Ophisops occidentalis 28 LC VU (D2) Restricted localized 41 Varanus griseus 141 NA LC Narrow Widespread 42 Varanus niloticus 24 NA VU (D2) narrow localized 43 Chalcides cf. humilis 37 NA VU (D2) Narrow Widespread 44 Chalcides ocellatus 596 NA LC Widespread Widespread 45 Eumeces schneiderii 188 NA LC Narrow Narrow 46 Scincus scincus 376 NA LC Narrow Widespread 47 Sphenops sepsoides 367 LC LC Restricted Widespread 48 Trachylepis quinquetaeniata 318 NA LC Narrow Widespread 49 Trachylepis vittata 17 LC VU (D2) Narrow Narrow 50 Leptotyphlops cairi 218 NA EN (B2 a,b i) Narrow Narrow
35 - N Species # records Global IUCN Egypt National IUCN World status Egypt Status 51 Leptotyphlops macrorhynchus 17 NA VU (D2) Narrow Narrow 52 Eryx colubrinus 51 NA VU (D2) Narrow Narrow 53 Eryx jaculus 44 NA LC Widespread Narrow 54 Eirenis coronella 22 NA VU (D2) Narrow localized 55 Lytorhynchus diadema 144 NA LC Narrow Widespread 56 Macroprotodon cucullatus 53 LC VU (D2) Widespread Narrow 57 Malpolon moilensis 51 NA LC Narrow Widespread 58 Malpolon monspessulanus 170 LC LC Widespread Narrow 59 Natrix tessellata 96 LC VU (D2) Widespread localized 60 Platyceps florulentus 127 LC LC Narrow localized 61 Platyceps rogersi 45 NA LC Narrow Widespread 62 Platyceps saharicus 34 NA LC Narrow Narrow 63 Psammophis aegyptius 183 NA LC Narrow Widespread 64 Psammophis schokari 371 NA LC Narrow Widespread 65 Psammophis sibilans 283 LC LC Narrow Narrow 66 Spalerosophis diadema 216 NA LC Widespread Widespread 67 Telescopus dhara 56 NA LC Narrow Narrow 68 Naja haje 60 NA LC Narrow Narrow 69 Naja nubiae 14 NA VU (D2) Restricted Narrow 70 Walterinnesia aegyptia 17 NA VU (D2) Restricted Narrow 71 Cerastes cerastes 233 NA LC Narrow Widespread 72 Cerastes vipera 551 NA LC Narrow Widespread 73 Echis coloratus 50 NA LC Narrow Widespread 74 Echis pyramidum 59 NA LC Narrow Widespread 75 Testudo kleinmanni 63 CE (A2 abcd + 3d) VU (D2) Near- Endemic Narrow
36 - Table 6: A list of 19 bioclimatic variables available from the worldclim website
Bio1 Annual mean temperature Bio2 Mean diurnal range (mean of monthly (max temp - min temp)) Bio3 Isothermality (Bio2/Bio7) (* 100) Bio4 Temperature seasonality (standard deviation *100) Bio5 Maximum temperature of the warmest month Bio6 Minimum temperature of coldest month Bio7 Temperature annual range (Bio5-Bio6) Bio8 Mean temperature of the wettest quarter Bio9 Mean temperature of the driest quarter Bio10 Mean temperature of the warmest quarter Bio11 Mean temperature of the coldest quarter Bio12 Annual precipitation Bio13 Precipitation of the wettest month Bio14 Precipitation of the driest month Bio15 Precipitation seasonality (coefficient of variation) Bio16 Precipitation of the wettest quarter Bio17 Precipitation of the driest quarter Bio18 Precipitation of the warmest quarter Bio19 Precipitation of the coldest quarter
Fig. 8: The distribution of the weather stations used to interpolate the bioclimatic variables: the number of weather stations in Egypt is clearly relatively few.
Fig. 9: Two examples of bio-layers variables excluded before calculating VIF statistics because they do not provide enough information to the model.
Table 7: List of variables used to calculate VIF values; rows shaded with grey show variables with VIF values less than 10 and so used to run the models.
Altitude Altitude NDVI_Max NDVI maximum value NDVI_Difference Absolute difference between the highest and lowest NDVI values Bio1 Annual mean temperature Bio2 Mean diurnal range (mean of monthly (max temp - min temp)) Bio3 Isothermality (Bio2/Bio7) (* 100) Bio4 Temperature seasonality (standard deviation *100) Bio5 Maximum temperature of the warmest month Bio6 Minimum temperature of the coldest month Bio8 Mean temperature of the wettest quarter Bio9 Mean temperature of the driest quarter Bio10 Mean temperature of the warmest quarter Bio11 Mean temperature of the coldest quarter Bio12 Annual precipitation Bio13 Precipitation of the wettest month Bio15 Precipitation seasonality (coefficient of Variation) Bio16 Precipitation of the wettest quarter Bio19 Precipitation of the coldest quarter
To estimate the potential impact of climate change on Egypt biodiversity (represented here by reptiles), current distribution models were projected into the future. This was done for three time slices (2020, 2050, and 2080). Future climate data (IPCC 4th assessment data - IPCC 2007) were downloaded from the International Centre for Tropical Agriculture website (see http://www.ccafs- climate.org/). In this study, four Global Circulation Models (GCMs) were used at each time slice to minimise the effect of model type and model calculation method on the final results, giving an average overall trend of the potential impacts of climate change (i.e. some elements of ensemble modelling). The four GCMs used were the Hadley Centre Coupled Model, version 3 (HadCM3), produced by the Hadley Centre for Climate Prediction and Research (UK), the second-generation coupled global climate model produced by the Canadian Centre for Climate Modelling and Analysis (CGCM2-
Species distribution modelling
In this study, Maxent software v3.3.3k (Phillips et al. 2004; Phillips et al. 2006 -
see:
http://www.cs.princeton.edu/~schapire/maxent/) was chosen to run the
models, for reasons explained above. Ten replicated runs with cross-validation
were made for each species (except species distributed in less than 10 unique
pixels ‘at the resolution of 2.5 arc minutes’: in these cases the number of
replicates was set automatically to the number of unique points). This was
done by randomly dividing the records into 10% sections, and then each run
used 90% of the data (training), leaving 10% for testing. This was repeated ten
times, each with a different 10% of the data for testing. This method gets the
best use of all the data for validation, especially if there are not many records
(Phillips & AT&T Research 2011). This also obtains a more stable model
performance to estimate the potential distribution, and minimises the effect of
possible errors and bias in species records. The habitat map (categorical) and
all non-collinear continuous variables (grey-shaded rows in Table 7) were
used to run the models. The mean (± SD) AUC values of the ten replicated
runs are given in Table 9.
Default Maxent settings have been shown to have the potential of
achieving a performance as good as if they were tuned on the evaluation data
itself (Phillips & Dudik 2008). Default settings were used to run the models,
with just one exception: the maximum number of iterations was set to 1000, to
be sure of allowing the algorithm enough time to converge (Phillips et al.
2006). The logistic output format was chosen, i.e. the probability of
occurrence, ranging from 0 to 1. From the cross-validation runs, ten raster
ASCII files were produced for each species, together with the overall mean
probability distribution. For each species, the most influential variable was
determined as the variable with the highest mean permutation importance
across model runs. Mean contribution value of each variable across all
Comparisons across species Current and future predicted species richness maps were calculated in two different ways: first, by simply adding together the average probability distribution maps of all species (assuming future unlimited dispersal); and second, by adding together the thresholded distribution maps (assuming future unlimited- and no-dispersal assumptions). For current species richness, two maps were created using either probability or thresholded distributions. Future species richness maps were calculated for each of the 24 possible future projections, using either probability (unlimited dispersal) or thresholded (unlimited and no dispersal) distributions. The overall means of future species richness maps across the four GCMs were also calculated using either probability (unlimited dispersal) or thresholded (unlimited and no-dispersal) distributions. Future potential changes in species richness were also calculated by subtracting current species-richness from future species-richness maps. This does not take into consideration the potential future changes in species composition, but just gives an indication of which areas will have an altered (increased or decreased) numbers of species. This resulted in having 24 species richness changes maps either using either probability distributions (unlimited dispersal) or thresholded distributions (either unlimited or no- dispersal). I calculated the average future changes in species richness across the four GCMs. Gains and losses in future species distributions were calculated using current and future thresholded distributions. The current thresholded
(a) Current distribution (b) Future distribution
Fig. 10: Calculation of future species gain or lost areas
Species turnover, an index of dissimilarity between current and future species composition (Thuiller 2004) was calculated. It is defined as the net
44 -
change in the number of species in a particular area (Broennimann et al.
2006). Species turnover is often considered a good measure of community
composition change, ecosystem disturbance and the potential impacts of
climate change from regional to continental scales (Thuiller 2004;
Broennimann et al. 2006). Areas with small turnover values (close to 0)
indicate that the species assemblage at these areas is predicted to remain
unchanged in the future (no species loss or gain); while areas with a high
turnover values (close to 100) indicate that the species assemblage at these
areas is predicted to be completely different in the future (i.e. all species
occupying the area are predicted to be lost/replaced in the future) (Thuiller
2004; Broennimann et al. 2006). For each future projection, and assuming
both dispersal assumptions, species turnover was calculated as the following
(following: Peterson et al. 2002; Thuiller et al. 2005; Broennimann et al. 2006):
(unlimited dispersal) = 100 ∗
+
+
(no dispersal) = 100 ∗
Where SL is the number of potentially lost species, SG is the number of potentially gained species, and SR is the current species richness.
In order to assess the future extinction risk of Egyptian reptiles as a result of climate change, and to determine which species may require more protection in the future, species range changes were calculated: the percentage loss (or gain) in suitable habitats (unlimited- and no-dispersal assumptions). This was calculated by counting the number of suitable pixels for each species in current and future maps, and calculating the average percentage of loss (or gain) in the number of pixels. Each species was then classified into one of the following categories (at each of 24 possible future projections and a mean classification among different GCMs): Extinct (loss of the entire suitable habitat - 100%), Critically Endangered (loss >80%), Endangered (loss 50-80%), Vulnerable (loss 30-50%), Least Concern (loss <30%), gain 1 (gain <30%), gain 2 (gain 30-50%), gain 3 (gain 50-80%), gain
Area prioritization for conservation Recently, several algorithms have been available to prioritize areas for conservation and conservation planning; these include Zonation (Moilanen et al. 2005; Moilanen 2007; Moilanen et al. 2012), Marxan (Game & Grantham 2008; Watts et al. 2009), ConsNet (Ciarleglio et al. 2008; Ciarleglio et al. 2009), MultCSync (Moffett et al. 2005), WorldMap (Williams 2001), and ResNet (Sarkar et al. 2002). Using the predictions from the species distribution models, Zonation v3.1 (Moilanen et al. 2012) was used in this study to create a nested spatial conservation prioritization to evaluate the effectiveness and performance of current Protected Area network in Egypt and to prioritize other sites for conservation (i.e. potential areas to expand the current PA network).
47 -
Zonation is a framework for conservation prioritization and spatial
conservation planning (Moilanen et al. 2012). It is based on hierarchically
prioritizing the conservation value of the landscape based on the conservation
value of sites (cells) (Moilanen 2007; Moilanen et al. 2012). The software
starts with the full extent of the landscape, and then generates rankings of the
cells by iteratively removing the least valuable cells from the edges of the
landscape according to set rules (e.g. minimizing marginal loss of the
conservation value and maintaining high habitat connectivity). The last to be
removed are the most important areas (Moilanen et al. 2005; Moilanen et al.
2012). The order of the cell removal is recorded and can be used later to
select any given top or bottom percentage of the landscape (e.g. highest or
lowest 10%) (Moilanen 2007). In Zonation, there are four different cell-removal
rule sets that specify how conservation value is aggregated across features
and space, determining which cell leads to smallest marginal loss of
biodiversity value: Basic core-area Zonation, Additive benefit function, Target-
based planning, and Generalized benefit function (Moilanen et al. 2012). Two
of these were used in this study (Basic core-area Zonation and Additive
benefit function).
Basic core-area Zonation was used to identify important (or poor)
locations where a single or a few species have important occurrences, while
Additive benefit function was used to give more weight to locations with high
species richness (Moilanen et al. 2012). The resulting maps of conservation
importance do not agree completely, which means that there can be species-
poor areas with some rare species. The manual suggests running Zonation
with both removal options and comparing the results, since this may reveal
some interesting information (Moilanen et al. 2012). In the calculations each
species is given a weight, allowing some species to be more important to
conserve than others. Unequal weights were assigned to all the reptile species
using the Global IUCN assessment, species world distribution, distribution
patterns within Egypt, and national IUCN species classification. For each
element, a score was given (Table 8) to indicate relative importance, and then
the sum of the scores gave the relative conservation weight of each species
(Table 9).
48 - Table 8: Different scores of different parameters used to calculate relative species weight.
Global IUCN status score National IUCN status score Not assessed 1 Least Concern 1 Least concern 1 Vulnerable - D2 2 Vulnerable 2 Endangered 3 Critically Endangered 3
Species world distribution score Distribution patterns within Egypt score Widespread 1 Widespread 1 Narrow 2 Narrow 2 Restricted 3 localized 3 Endemic/Near Endemic 4
2 [ ]
2 ∗ [4.6388] [ ( )] ∗ [0.0416]
The dispersal ability of almost all terrestrial reptile species is very limited (Cadby et al. 2010; Edgar et al. 2010), which makes them more vulnerable to rapid environmental changes (Araujo & Pearson 2005). In this study, the dispersal distance of all reptiles was set to be equal to 1 km, entailing one α-value for all species (=223). Zonation was run using the maps of occurrence probabilities for all the species using both Basic core-area Zonation and Additive benefit function cell removal rules. The number of cells removed at each iteration was set to 10 (=
0.7 were considered to be of high conservation importance. These important areas were then overlaid with the current Protected Area system in Egypt (Fig.
Model performance The performance of current distribution models, in terms of mean AUC values, was good. The mean AUC value of 10 repeat model runs for each species ranged from 0.78 to 0.99 with an overall mean of 0.93 ± 0.05. The lowest and highest mean AUC values were for Ptyodactylus siphonorhina (0.78 ± 0.09) and Mesalina pasteuri (0.99 ± 0.01), respectively (Fig. 11). Only one species (Ptyodactylus siphonorhina) had a mean AUC of less than 0.8; there were five species between 0.8 and 0.85, 14 species between 0.85 and 0.9, 19 species between 0.9 and 0.95 and 36 species with mean AUC values greater than 0.95 (Fig. 12). AUC variability among replicate runs was low, with standard deviations of less than 0.1 in 67 species, between 0.1 and 0.2 in six species, and between 0.2 and 0.3 in two species (Naja nubiae and Hemidactylus robustus) (Table 9). Mean AUC values for highly weighted species (weight value ≥ 12) range from 0.887 and 0.993, with a mean of 0.97 ± 0.03. As almost all species have a mean AUC value > 0.8, all models were accepted and processed for further analyses (see Table 9 for a list of mean AUC value, s.d., and the weighting value of each species). A highly significant strong negative correlation was found between mean AUC values and both of the predicted area occupied by the species (in terms of the number of currently predicted suitable pixels; n=75, r s = - 0.85, p<0.005 – Fig. 13) and species current extent of occurrence (EOO - calculated as the minimum convex polygon containing species distribution points; n=75, r s = - 0.77, p<0.005 – Fig. 14); and a significant weak negative correlation between mean AUC values and the number of species unique recorded points used to run the models (n=75, r s = - 0.433, p<0.005 – Fig. 15).
Most influential environmental variables Variables with highest mean permutation importance across all modelled species, and hence potentially the highest contribution to final models, were altitude, Bio4 (temperature seasonality) and Bio13 (precipitation of wettest
Species richness Current species richness Using probability distributions, the areas with the highest current species richness are located at the greater Cairo area, the Suez Canal area extending from Suez to Port-Said, around Damietta and Alexandria and scattered small patches on Suez and Aqaba gulfs in Sinai and the Eastern Desert; other such areas are Wadi El-Natrun, Fayoum and scattered locations on the Mediterranean coast (Fig. 18). Using thresholded distributions, the areas with the highest species richness are the Suez Canal area, the greater Cairo southwards towards Fayoum and Beni Suef and eastwards towards Suez and Ismailia, the northern coast of the Nile Delta from Damietta to Rashid, Wadi El- Natrun, around Alexandria, coastal areas of the Suez and Aqaba Gulfs in Sinai and the Eastern Desert, and the north coast and coastal areas in North Sinai (Fig. 20).
Future species richness changes Using probability distributions, under the A2a scenario it is predicted that species richness will increase by 2020 in coastal areas between Safaga and El-Quseir, Bir Abraq area, patchy areas on the north coast from Alexandria to west of Mersa Matruh, both sides of the Suez Canal near El-Salam lake, east of Fayoum in the Nile valley, near Ras Mohamed in South Sinai and scattered areas from east and central Sinai northwards towards the Mediterranean
52 - coast. Species richness is predicted to decline around the Siwa oasis, inland wadis between El-Quseir and Mersa Alam, and small patches south of Abu Zneima in the Suez gulf (Figs. 18, 19). By 2050, species richness is predicted to increase on the northern coast from west Alexandria to Sidi Barrani across the northern edge of the Qattara Depression, in the Nile Valley (around El- Minia and Fayoum, and from Sohag to Edfu), Red Sea coastal areas from Mersa Alam to north of Hurghada, Western Desert oases (Kharga, Dakhla, and Farafra), South Sinai (around Ras Mohamed), and an area from central Sinai northwards to the Gebel El-Hallal area. It is predicted to decline in small areas around Gebel Elba, southwest of El-Quseir, Siwa oasis, Wadi El-Natrun, the greater Cairo, north of Suez and west of Ismailia (Figs. 18, 19). By 2080, species richness is predicted to increase more extensively along the north Mediterranean coast as far south as the northern part of the Qattara Depression, along the Red Sea coast from Mersa Alam to north of Hurghada, in the Nile Valley from Fayoum southwards, Western Desert oases, the Gebel El-Gallala area, on both sides of the northern parts of the Suez Canal, the coasts of South Sinai and a major part of central Sinai northwards to the Gebel El-Hallal area. It is predicted to decline in small areas of Gebel Elba, Wadi El- Natrun, the greater Cairo and scattered sites along the Suez Canal near Ismailia (Figs. 18, 19). Under the B2a scenario, the predicted patterns of change in species richness do not differ much from those of the A2a scenario (see Figs. 18, 19). By 2020, species richness is predicted to increase in Red Sea coastal areas near Berenice and El-Quseir and in small areas near El-Burullus Lake, with a greater area of increased species richness at the Mediterranean coast and in central to east Sinai. No increase is predicted at Bir Abraq and the Suez Canal area, and a greater decline in inland wadis near El-Quseir and at small coastal areas of the Suez and Aqaba gulfs near Zaafarana and north of Dahab. By 2050, there is predicted to be less increase in species richness, compared to the A2a scenario, along the Red Sea coast from south of El-Quseir to north of Hurghada, in South Sinai, and in the Western Desert oases; greater increase in central to east Sinai and in inland wadis west of El-Quseir; and no decline at all in Siwa and the Suez Canal area. By 2080, the overall pattern of species
53 - richness increase is the same as for the A2a scenario, but the magnitudes and areas of increased species richness are lower. Using thresholded distributions there are two assumptions to consider, unlimited dispersal or the complete absence of dispersal. For unlimited dispersal under the A2a scenario, it is predicted that species richness will increase by 2020 in the Bir Abraq area, inland areas of the north coast southwards to northern parts of the Qattara Depression, central Sinai northwards to the Gebel El-Hallal area, west of Cairo, coastal areas between Safaga to south of El-Quseir, and on the western side of the lower Nile Valley (the Tushka area). Declines are predicted in inland wadis from near Berenice northwards to Gebel El-Gallala, the area between Suez, Ismailia and Cairo, coastal areas on the Suez and Aqaba gulfs in Sinai and the Eastern Desert, the Gebel Elba area, Siwa oasis, and northwest of Wadi El-Natrun (Figs. 20, 21). By 2050, species richness is predicted to increase in the Qattara Depression and inland areas of the north coast, the Abraq area, the Gebel El- Gallala area, central Sinai northwards to the Gebel El-Hallal area, the northern part of El-Qaa plain on the eastern side of the Suez Gulf in Sinai, the lower Nile Valley westwards to the Tushka area, and Western Desert oases. Declines are predicted in the Suez Canal area, greater Cairo eastwards towards Suez and Ismailia, Wadi El-Natrun northwards, coastal areas of both the Suez and Aqaba gulfs, inland wadis near the Red Sea coast between Berenice and north of Hurghada, the Gebel Elba area and scattered areas near the North Sinai coast (Figs. 20, 21). By 2080, species richness is predicted to increase over extensive areas covering a large percentage of Egypt, including more than half of the Western Desert (the Qattara Depression, Western Desert oases and Tushka), Gebel El-Gallala, Bir Abraq, coastal areas north of El-Quseir and an area extending from central Sinai northwards to Gebel El-Hallal. Declines are predicted in the Wadi El-Natrun area, greater Cairo eastwards towards Suez and Ismailia, both sides of the Suez Canal, the Suez and Aqaba Gulfs, Gebel Elba, North Sinai north of Gebel El-Hallal and inland wadis from Mersa Alam to Hurghada (Figs. 20, 21). For unlimited dispersal under the B2a scenario, the overall pattern of predicted changes in species richness does not differ much from the A2a scenario (Figs. 20, 21). By 2020, there is predicted to be less increase in the
54 - Abraq area and greater declines in Wadi El-Natrun and the South Sinai coasts. By 2050, there will be less decline in Wadi El-Natrun and northern parts of the Suez Canal, and greater declines in the inland wadis of the Red Sea (west of Mersa Alam northwards) and on South Sinai coasts. By 2080, although the overall pattern of species richness increase does not differ from that of the A2a scenario, the magnitude of the increase is greater in central and southern parts of the Western Desert, Gebel El-Gallala, the northern part of the Nile Delta and coastal areas of the Red Sea; and less in coastal areas of North Sinai. Under the no-dispersal assumption and the A2a scenario, it is predicted that species richness will decline by 2020 in coastal areas of the Suez and Aqaba gulfs, the Suez Canal area, Wadi El-Natrun, the area between Cairo, Suez and Ismailia, Siwa oasis and the Qattara Depression, Gebel Elba, around Berenice, and inland wadis between Mersa Alam and north of Hurghada (Figs. 22, 23). By 2050, species richness is predicted to decline further in the area between Cairo, Ismailia and Suez, coastal areas of the Suez and Aqaba gulfs, the Nile Valley between Fayoum and Assiut, Red Sea inland wadis between Mersa Alam and north of Hurghada, scattered areas around Wadi El-Natrun, northern and central Sinai, and the Gebel Elba area (Figs. 22, 23). By 2080 the greatest decline in species richness is predicted to be from Suez southwards on both sides of the northern part of the Suez Gulf, Wadi El-Natrun northwards, and the area between Cairo, Ismailia and Suez; smaller declines are predicted in coastal areas around the Suez and Aqaba gulfs, the Suez Canal, Eastern Desert inland wadis, the Qattara Depression, Gebel Elba, Siwa oasis and Gebel El-Hallal northwards to the North Sinai coast (Fig. 22, 23). With no-dispersal and under the B2a scenario, the overall pattern of declines in future species richness does not differ much from the A2a scenario, but greater declines are predicted in Wadi El-Natrun, the area between Cairo, Suez and Ismailia, and on coastal areas on both sides of the Suez Gulf (Figs. 22, 23).
55 -
Species Gains and Losses
Under both dispersal assumptions and under the A2a scenario, the highest
predicted species loss by 2020 is predicted to be in coastal areas on both
sides of the Suez Gulf, coastal areas in the lower parts of the Aqaba Gulf, the
area between the greater Cairo, Ismailia and Suez, Siwa oasis, the base of the
Qattara Depression, inland wadis between Hurghada and Mersa Alam, coastal
areas between north of Berenice and Abu Ramad, and Wadi El-Natrun
northwards (Fig. 24). By 2050, the pattern of species loss is predicted to be
the same as in 2020, with greater loss around the lower part of Suez Gulf,
Siwa oasis, inland wadis between Hurghada and El-Quseir, northwestern parts
of Sinai, around Farafra oasis, Wadi El-Natrun and in the Nile Valley from
Fayoum to Minia; losses are predicted to decrease near Berenice (Fig. 24). By
2080, there is predicted to be greater species loss in the area between the
greater Cairo, Ismailia and Suez, south of Suez on both sides of the Suez
Gulf, and Wadi El-Natrun northwards; somewhat less in the area extending
from central to north Sinai, northern and eastern parts of the Qattara
Depression, the Berenice area and scattered locations on the north coast.
Inland wadis between Hurghada and El-Quseir show smaller losses compared
to 2050 (Fig. 24).
Under the B2a scenario, the overall pattern of species loss is much
greater than predicted under the A2a scenario (Fig. 24). By 2020, there will be
large losses on both sides of the Suez Gulf, Wadi El-Natrun northwards, the
area between the greater Cairo, Ismailia, and Suez, inland wadis between
Hurghada and El-Quseir, and coastal areas of the southern part of the Aqaba
Gulf; these are somewhat less in the Berenice area, Siwa oasis, northern and
eastern parts of the Qattara Depression and the Gebel Elba area. By 2050,
the same pattern is predicted, with greater losses in Wadi El-Natrun
northwards, the area between the greater Cairo, Ismailia and Suez, coastal
areas on both sides of the Suez Gulf, Siwa oasis and the Qattara Depression;
these are a bit lower in coastal areas of the Mediterranean from Sallum to
Sinai, inland wadis near Gebel El-Hallal westwards, around the Farafra and
Bahariya oases, coastal areas of the northern part of the Aqaba Gulf, around
Fayoum and the Gebel Elba area. By 2080, the overall pattern of species loss
does change from that of 2050, with smaller numbers of species being lost on
56 -
the Mediterranean coast, around Bahariya oasis and Fayoum, northern Sinai,
the Qattara Depression and northern coastal areas of the Aqaba gulf.
In terms of species loss relative to current (thresholded) species
richness, under the A2a scenario the highest relative loss by 2020 is predicted
to be in the Western Desert oases (Bahariya, Dakhla, and Kharga oases, and
Siwa oasis south-eastwards), El-Gilf El-Kebir, both sides of the Nile Valley
between Fayoum and Aswan, and inland wadis between Mersa Alam and
Gebel El-Gallala (Fig. 25). This pattern is predicted not to change by 2050, but
with relative greater declines in the Western Desert oases (except Bahariya)
and relatively smaller declines in El-Gilf El-Kebir and inland and coastal areas
of the northern half of the Eastern Desert, followed by Suez Gulf coasts in
Sinai and the North Coast (Fig. 25). By 2080, Western Desert oases are
predicted to undergo greater relative losses (except Bahariya where the areas
of high relative loss shrink), followed by the Mediterranean coast (especially
south-east of Sallum), the northern part of the Suez Gulf in Sinai, the area east
of greater Cairo and northern and central parts of the Nile Delta. Areas of
earlier high relative species loss in the northern part of the Eastern Desert are
predicted to shrink in area by 2080 (Fig. 25).
Under B2a scenario, the overall pattern of relative species loss is
similar to that of A2a scenario, with greater relative losses in inland wadis of
the northern half of the Eastern Desert, around Western Desert oases, Wadi
El-Natrun and the Mediterranean coast. By 2050, there is predicted to be
greater relative loss in the area between Gebel Elba and Wadi El-Allaqi (Fig.
25).
Under unlimited dispersal and the A2a scenario, the highest gains in
species by 2020 are predicted to be on the Mediterranean coast west of
Alexandria, central and south-eastern parts of the Qattara Depression, eastern
and central Sinai northwards towards Gebel El-Hallal, Bir Abraq, and coastal
areas north and south of El-Quseir (Fig. 26). By 2050, the predictions have the
same overall pattern of species gain, with an expansion in area on the
Mediterranean coast, the Qattara Depression and central to north Sinai; and
with smaller gains at Bir Abraq (Fig. 26). By 2080, there is predicted to be
continuing increases in gains on the Mediterranean coast, the Qattara
57 -
Depression, north of Siwa, scattered areas on both sides of the Nile Valley
from Fayoum southwards, Gebel El-Maghrabi east of Edfu, Gebel El-Gallala
and coastal areas between Hurghada and Mersa Alam; somewhat smaller in
areas on both sides of the northern part of the Suez Canal and the Western
Desert oases (Fig. 26).
Under the B2a scenario and unlimited dispersal, there is predicted to be
an overall increase in gains across Egypt relative to the A2a scenario. By
2020, the highest increase in species gains is predicted to be on the
Mediterranean coast, the Qattara Depression, areas around the Nile Valley
from north of Fayoum to Edfu, the Western Desert oases, Bir Abraq, the
coastal area from north of Hurghada to south of El-Quseir, Gebel El-Gallala,
the area extending from eastern and central Sinai northwards to Gebel El-
Hallal, south of Rafah, and areas on both sides of the northern part of the
Suez Canal (Fig. 26). By 2050, a large proportion of the northern half of the
Western Desert is predicted to gain many more species (including the
Mediterranean coast and Siwa oasis, with the highest gains in the Qattara
Depression). Areas with high predicted gains also include the Western Desert
oases, the Tushka area northwards, areas on both sides of the Nile Valley
from north of Fayoum southwards, Bir Abraq, the northern part of the Nile
Delta, Gebel El-Gallala, coastal areas from north of Hurghada to Mersa Alam,
an area extending from central to northwestern Sinai and south of Rafah (Fig.
26). By 2080, the pattern of areas of highest predicted gains is similar to that
of 2050, with contractions in some areas including around Western Desert
oases, Tushka, around the Nile Valley, the northern Nile Delta, Gebel El-
Gallala, south of Rafah and the Red Sea coast from north of Hurghada to
Mersa Alam (Fig. 26).
Predicted gains in species relative to current (thresholded) species
richness under the A2a scenario by 2020 are highest on both sides of the
lower Nile Valley from Qena southwards, Tushka area, west of Fayoum
towards the west and south of the Bahariya oasis, and north and south of
Siwa; these are somewhat smaller west and north-west of Farafra, the Qattara
Depression northwards towards inland areas of the Mediterranean coast,
scattered areas in central Sinai, Kharga southwards and westwards, and a
small area south of Damietta (Fig. 27). By 2050, the overall predicted pattern
58 -
is similar, with increases in the centre of the Qattara Depression northwards
and an area extending from west of Fayoum towards west and south of
Bahariya (Fig. 27). By 2080, there are predicted to be even greater increases
in both area and magnitudes in the Qattara Depression northwards towards
inland areas of the Mediterranean coast, north and south of Siwa, an area
extending from west of Fayoum towards west and south of Bahariya oasis,
around Dakhla and Kharga, and on both sides of the lower Nile Valley
especially mountainous areas of the Eastern Desert (Fig. 27).
Under the B2a scenario, the overall pattern of relative gain shows much
higher magnitudes compared to the A2a scenario. By 2020, the predicted
pattern of relative gains resembles that of 2080 for the A2a scenario, and by
2050 and 2080, there is predicted to be large areas with relatively high gains,
mainly in the Qattara Depression northwards, around Western Desert oases,
west and north of Fayoum and around the lower part of the Nile Valley
especially in mountainous areas of the Eastern Desert (Fig. 27).
Species Turnover Under unlimited dispersal and the A2a scenario, by 2020 the highest predicted turnover in species composition is predicted to be in the Western Desert (south-east of Bahariya westwards to the Libyan borders, around Dakhla, around Kharga down to the Sudanese borders, and north of El-Gilf El-Kebir) and around the Nile Valley from Assiut southwards; values are somewhat lower west and south of Fayoum, north and north-west of the Qattara Depression, and in the Bir Abraq area, as well as scattered inland wadis in the Eastern Desert (Fig. 28). By 2050, a similar pattern is predicted, with slight declines in turnover west of Farafra near the Libyan borders (Fig. 28). By 2080, the pattern again does not change much, with greater expansion in the areas of high turnover around the southern part of the Nile Valley and the Western Desert (south and west of Kharga and Dakhla); and slight increases in turnover in the greater Cairo area, Wadi El-Natrun westwards to Libya, the central Nile Delta and in central to northern Sinai (Fig. 28). Under the B2a scenario, the predicted pattern of species turnover resembles that of the A2a scenario, but with an overall increase in the areas of high turnover, and
Range Changes
With unlimited dispersal, no species is predicted to become extinct in the
future under all or the average of the global circulation models. There are a
couple of species predicted to become extinct by losing their entire area of
suitable habitat in at least one of the future projections: Tarentola mindiae is
predicted to become extinct by 2080 under both emission scenarios of the
CSIRO and NIES99 models, and under the A2a scenario of the HadCM3
model; and Hemidactylus robustus is predicted to become extinct by 2080
under both emission scenarios of the CSIRO model, and under the A2a
scenario of the HadCM3 model (Table 10 - Fig. 30).
Using the average gain or loss of suitable habitat across the four
different global circulation models (Table 10), only one species is predicted to
be classified as Critically Endangered (i.e. predicted to lose more than 80% of
suitable habitat) by 2020: this species is Hemidactylus robustus under the A2a
scenario. By 2050, two species are predicted to be classified as Critically
Reptile species classifications According to the current IUCN Red List website ( http://www.iucnredlist.org/), 19 Egyptian reptiles have been assessed globally: one is classified as Critically Endangered (Testudo kleinmanni), two as Vulnerable (Acanthodactylus pardalis and Trapelus savignii) and 16 as Least Concern. Fifty-six species have not been classified globally yet. Among the reptile species excluded from this study, two near-endemic species are in the IUCN global Red List: Philochortus zolii is Critically Endangered [B1 ab(iii)] and Telescopus hoogstraali is Endangered [B1ab(iii)] (Böhme & Baha El Din 2006b; Disi et al. 2006). According to Egypt’s national Red List assessment, three species are classified as Endangered (Leptotyphlops cairi, Chamaeleo africanus and Uromastyx ocellata), 24 as Vulnerable and 48 as Least Concern (for the global and national IUCN classification of the Egyptian reptiles, see Table 5). According to their world distribution, 6 species are classified as Endemic or Near-Endemic (Acanthodactylus aegyptius, Mesalina bahaeldini, Tarentola mindiae, Testudo kleinmanni, Trapelus savignii and Tropiocolotes bisharicus), eight as Restricted (Acanthodactylus pardalis, Naja nubiae, Ophisops occidentalis, Ptyodactylus siphonorhina, Sphenops sepsoides, Stenodactylus mauritanicus, Uromastyx ornata and Walterinnesia aegyptia),
Reptile records in Protected Areas Using database records, the highest number of recorded species per Protected Area was found at St Katherine, followed by Gebel Elba, Wadi El- Gemal and Lake Qarun (Table 14). No records were available from eight Protected Areas (Saluga & Ghazal, Ashtum El-Gamil, Sanur Cave, Nile Islands, Red Sea northern islands, El-Dababya, El-Sallum Gulf and Mt Kamel Meteor). Using the thresholded predicted distributions, the number of climatically suitable species per Protected Area was found to be highest at Lake Burullus, Nabq, Gebel Elba, Ashtum El-Gamil, Taba and Wadi El-Gemal (Table 14). There is a non-significant correlation between the number of recorded and climatically suitable species across Protected Areas (n=23, r s =0.49, p=0.12 one-tailed – Fig. 36). The highest difference between the number of predicted and recorded species was for Lake Burullus, Nabq, Siwa, Abu Galum, Ras Mohamed, Taba and Al-Ahrash. There is a significant positive correlation between the area of the Protected Areas and both the number of recorded species (n=23, r s =0.64, p=0.001 – Fig. 37) and the number of predicted climatically suitable species (thresholded) (n=23, r s =0.46, p=0.03 – Fig. 37).
Area prioritization for conservation Using the assumption of ‘additive benefit function’, the areas with the highest current prioritization value were located on the Mediterranean coast from Rafah to Sallum, high-elevation areas of South Sinai, the Suez Canal area, greater Cairo north- and eastwards towards Suez, Ismailia and Sharqia and
69 -
between Fayoum and Edfu, central Sinai, and around the Farafra, Dakhla and
Kharga oases (Figs. 38, 39).
The mean prioritization value in all models was found to be higher in
Protected Area than outside them, with overall a slight increase in prioritization
value in the future. The difference in prioritization value between inside and
outside Protected Areas seems to decline in the future, especially by 2050 and
2080 under the B2a scenario (Fig. 42).
Using the ‘core-area function’ assumption, areas with the highest
current prioritization value were located in high-elevation wadis in South Sinai,
Gebel Elba, Siwa oasis, the northern part of the Qattara Depression, the Suez
Canal area, coastal areas of the Aqaba Gulf, Red Sea coastal areas from
south of Safaga to south of Halayeb, the Mediterranean coast from Sallum to
Rafah and the Nile Valley from Qena to the Sudanese border. Slightly lower
priority was given to non-coastal areas in North Sinai (Gebel El-Hallal); the
greater Cairo area eastwards to Ismailia and Suez, westwards towards Wadi
El-Natrun and southwards in the Nile Valley towards Fayoum and Qena; and
coastal areas on both sides of the Suez Gulf (Fig. 40).
The overall pattern of prioritization value does not seem to change very
much in the future, but there are more changes than when additive benefit
function was used. Under the A2a scenario, prioritization value is predicted to
decline by 2020 in El-Gilf El-Kebir, around Farafra oasis and south of Siwa;
while increase north of Siwa, south of the Qattara Depression, around
Bahariya, south-east and south-west of Suez, east of El-Gilf El-Kebir (on the
Sudanese border) and near Hurghada (Figs. 40, 41). By 2050, the pattern of
prioritization value decline resembles that of 2020, but with greater decline
around Farafra oasis, and increases from around Fayoum westwards to the
southern part of the Qattara Depression, north of the Qattara Depression,
north of Siwa, east of El-Gilf El-Kebir eastwards to the Nile Valley, the Red
Sea coast between west of El-Quseir to Gebel El-Gallala, western parts of
central Sinai and Bir Abraq southwards to small areas of Gebel Elba (Figs. 40,
41). By 2080, the overall pattern of prioritization value change does not
change much from 2050: there are greater declines around Siwa, and smaller
declines (and shrinking areas) around Farafra; but increased prioritization
70 - value in both area and amount in some areas, especially west of Fayoum towards the Qattara Depression and east of El-Gilf El-Kebir (Figs. 40, 41). As under the B2a scenario, prioritization value is predicted to decline by 2020 south of Siwa, around Farafra, El-Gilf El-Kebir, east of Cairo and west of Hurghada; while increase north of Siwa, south of the Qattara Depression, east of Bahariya, around Fayoum, along the Red Sea coast from Gebel El-Gallala to south-west of Safaga, east of El-Gilf El-Kebir on the Sudanese border, south-east of Aswan and eastern and western parts of central Sinai (Figs. 40, 41). By 2050, prioritization value is predicted to decline in El-Gilf El-Kebir, south of Siwa, around Farafra and in between the greater Cairo and Suez; while increase between Fayoum and south of Fayoum westwards to the Libya border, the southern part of the Western Desert near Sudan, north of El-Gilf El-Kebir, Bir Abraq southwards to Gebel Elba, Eastern Desert inland wadis from Gebel El-Gallala to Safaga and scattered locations in central Sinai (Figs. 40, 41). By 2080, prioritization value is predicted to decline in El-Gilf El-Kebir, south of Siwa, around Farafra and east of Cairo; while increase in an area extending from Fayoum southwards to El-Minia and westwards to the Libyan border, Red Sea inland wadis from Gebel El-Gallala southwards to El-Quseir, Bir Abraq southwards to Gebel Elba, around the Nile valley from Edfu southwards and westwards (including Tushka), and scattered locations in central Sinai (Figs. 40, 41). Mean prioritization value in all cases were found to be higher inside Protected Area than outside, with overall a moderate increase predicted in the future (except by 2020 under the A2a scenario). The difference in prioritization value between inside and outside Protected Areas seems to be higher using the ‘core area’ rather than the ‘additive benefit’ function, with slightly smaller differences in the future (Figs. 42, 43).
71 - Table 9: Mean and standard deviation of AUC values for each species
Weighting score 1 Cyrtopodion scabrum 0.951 0.097 2 2 Hemidactylus flaviviridis 0.961 0.105 8 3 Hemidactylus robustus 0.899 0.289 8 4 Hemidactylus turcicus 0.955 0.021 1 5 Pristurus flavipunctatus 0.991 0.004 8 6 Ptyodactylus guttatus 0.953 0.026 4 7 Ptyodactylus hasselquistii 0.932 0.031 2 8 Ptyodactylus siphonorhina 0.781 0.09 3 9 Stenodactylus mauritanicus 0.989 0.012 18 10 Stenodactylus petrii 0.939 0.034 2 11 Stenodactylus sthenodactylus 0.85 0.041 2 12 Tarentola annularis 0.906 0.046 2 13 Tarentola mauritanica 0.978 0.014 2 14 Tarentola mindiae 0.97 0.019 16 15 Tropiocolotes bisharicus 0.986 0.017 16 16 Tropiocolotes nattereri 0.975 0.015 4 17 Tropiocolotes steudneri 0.879 0.037 2 18 Tropiocolotes tripolitanus 0.95 0.036 4 19 Agama spinosa 0.964 0.019 4 20 Laudakia stellio 0.983 0.008 4 21 Pseudotrapelus sinaitus 0.881 0.08 2 22 Trapelus mutabilis 0.947 0.026 2 23 Trapelus pallidus 0.943 0.036 2 24 Trapelus savignii 0.982 0.007 32 25 Uromastyx aegyptia 0.945 0.022 2 26 Uromastyx ocellata 0.923 0.077 12 27 Uromastyx ornata 0.99 0.005 18 28 Chamaeleo africanus 0.963 0.043 12 29 Chamaeleo chamaeleon 0.976 0.013 2 30 Acanthodactylus aegyptius 0.936 0.05 4 31 Acanthodactylus boskianus 0.889 0.02 2 32 Acanthodactylus longipes 0.962 0.022 4 33 Acanthodactylus pardalis 0.983 0.017 24 34 Acanthodactylus scutellatus 0.859 0.03 2 35 Mesalina bahaeldini 0.978 0.023 24 36 Mesalina guttulata 0.845 0.055 2 37 Mesalina olivieri 0.951 0.042 2
Weighting score 38 Mesalina pasteuri 0.993 0.007 12 39 Mesalina rubropunctata 0.839 0.071 2 40 Ophisops occidentalis 0.992 0.006 18 41 Varanus griseus 0.892 0.04 2 42 Varanus niloticus 0.985 0.021 12 43 Chalcides cf. humilis 0.87 0.155 4 44 Chalcides ocellatus 0.952 0.015 1 45 Eumeces schneiderii 0.961 0.043 4 46 Scincus scincus 0.87 0.08 2 47 Sphenops sepsoides 0.92 0.027 3 48 Trachylepis quinquetaeniata 0.956 0.021 2 49 Trachylepis vittata 0.988 0.012 8 50 Leptotyphlops cairi 0.948 0.058 12 51 Leptotyphlops macrorhynchus 0.926 0.113 8 52 Eryx colubrinus 0.91 0.113 8 53 Eryx jaculus 0.982 0.012 2 54 Eirenis coronella 0.985 0.018 12 55 Lytorhynchus diadema 0.894 0.047 2 56 Macroprotodon cucullatus 0.985 0.023 4 57 Malpolon moilensis 0.874 0.076 2 58 Malpolon monspessulanus 0.98 0.017 2 59 Natrix tessellata 0.978 0.012 6 60 Platyceps florulentus 0.957 0.027 6 61 Platyceps rogersi 0.943 0.071 2 62 Platyceps saharicus 0.882 0.097 4 63 Psammophis aegyptius 0.84 0.055 2 64 Psammophis schokari 0.934 0.03 2 65 Psammophis sibilans 0.979 0.009 4 66 Spalerosophis diadema 0.935 0.036 1 67 Telescopus dhara 0.936 0.074 4 68 Naja haje 0.961 0.026 4 69 Naja nubiae 0.897 0.214 12 70 Walterinnesia aegyptia 0.887 0.075 12 71 Cerastes cerastes 0.838 0.044 2 72 Cerastes vipera 0.922 0.038 2 73 Echis coloratus 0.878 0.11 2 74 Echis pyramidum 0.917 0.128 2 75 Testudo kleinmanni 0.969 0.026 48
Fig. 11: Box-and-whiskers plot for mean AUC values across 75 studied species. The horizontal dark line in the middle of the box indicates the median; the box indicating the 1 st and 3 rd quartiles; and the Whiskers indicate minimum and maximum mean AUC values.
Fig. 12: Frequency distribution of species mean AUC values showing the number of species at different ranges of mean AUC values.
Fig. 13: The correlation between species mean AUC value and its predicted area of suitable habitats (in terms of the number of currently predicted suitable pixels of the thresholded distribution) (n=75, r s =-0.85, p<0.005).
Fig. 14: The correlation between species mean AUC value and its current extent of occurrence (calculated as the minimum convex polygon containing species distribution points) in 1000 Km 2 (n=75, r s =-0.77, p<0.005).
Fig. 15: The correlation between species mean AUC value and the number of its unique recorded points used to run the models (n=75, r s =-0.433, p- value<0.005).
Fig. 16: Relative contribution of environmental variables to the final model, represented by the mean value of average permutation importance across species (± 95% confidence limit).
Fig. 17: The number of species at which each environmental variable was considered as the most influential environmental variable. Two variables are not shown (the difference between maximum and minimum NDVI and Bio9), as they have not been shown as the most influential environmental variable for any species.
Fig. 18: Mean predicted reptile species richness using the summation of species predicted probability distributions (current and future – assuming unlimited dispersal). Colour gradient ranked from grey (low species richness) to red (high species richness).y
A2a
2020 2050 2080
B2a
2020 2050 2080
77 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 19: Future potential species richness change as a result of anthropogenic climate change (using species probability distributions – assuming unlimited dispersal); calculated as the difference between each of future species richness maps and current species richness (shown in Fig. 17). Colour gradient indicates how much future species richness change is: grey indicates no much change, dark green indicates high future species richness increase, and dark red indicates high future species richness decline.
Fig. 20: Mean predicted reptile species richness using the summation of species thresholded predicted distributions (current and future - assuming unlimited dispersal). Colour gradient ranked from green (low species richness) to red (high species richness).
A2a
2020 2050 2080
B2a
2020 2050 2080
79 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 21: Future potential species richness change as a result of anthropogenic climate change (using predicted species thresholded distributions – assuming unlimited dispersal); calculated as the difference between each of future species richness maps and current species richness (shown in Fig. 19). Colour gradient indicates how much future species richness change is: grey indicates no much change, dark green indicates high future species richness increase, and dark red indicates high future species richness decline.
Fig. 22: Mean predicted reptile species richness using the summation of species thresholded predicted distributions (current and future - assuming no- dispersal). Colour gradient ranked from green (low species richness) to red (high species richness).
A2a
2020 2050 2080
B2a
2020 2050 2080
81 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 23: Future potential species richness change (decline) as a result of anthropogenic climate change (using predicted species thresholded distributions – assuming no-dispersal); calculated as the difference between each of future species richness maps and current species richness (shown in Fig. 21). Colour gradient indicates how much future species richness decline is: grey indicates (no much change) to dark red (high future species richness decline).
82 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 24: Potential future species loss as a result of anthropogenic climate change (assuming both dispersal assumptions: unlimited and no- dispersal).
For each species possible future projection, currently suitable pixels predicted to loss its suitability in the future are exported to separate files, then average loss across different global circulation models was calculated. The summation of mean future loss across species was then performed to show which area will potentially loss much species in the future. Colour gradient indicate how much species will be lost in the future: grey indicates no species loss, dark green indicates low species loss, and dark red indicates high species loss.
83 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 25: Relative potential future species loss (assuming both dispersal assumptions: unlimited and no-dispersal); calculated as the percentage between the number of species predicted to be lost (Fig. 23) and current thresholded species richness (Fig. 19) for each pixel. Grey indicated low relative species loss and dark red indicated high relative species loss.
84 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 26: Potential future species gain as a result of anthropogenic climate change (assuming unlimited dispersal).
For each species possible future projection, currently non-suitable pixels predicted to gain suitability in the future (assuming unlimited dispersal) are exported to separate files, then average gain across different global circulation models was calculated. The summation of mean future gain across species was then performed to show which area will potentially gain much species in the future. Colour gradient indicate how much species will be gained in the future: grey indicates no species gain, dark green indicates low species gain, and dark red indicates high species gain.
85 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 27: Relative potential future species gain (unlimited dispersal assumption); calculated as the percentage between the number of gained species (unlimited dispersal - Fig. 25) and current thresholded species richness (Fig. 19) for each pixel. Grey colour indicates low relative species gain and dark red indicates high relative species gain.
86 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 28: Future species turnover (a measure of dissimilarity between current and future species composition) assuming unlimited dispersal. Colour range from grey (low species turnover – small species composition change in the future) to dark red (high species turnover – high species composition change in the future).
A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 29: Future species turnover (a measure of dissimilarity between current and future species composition) assuming no-dispersal. Colour range from grey (low species turnover – small species composition change in the future) to dark red (high species turnover – high species composition change in the future).
Abbreviations used: Critically Endangered “CR”: loss>80%; Endangered “EN”: loss 50- 80%; Vulnerable “VU”: loss 30-50%; Least Concern “LC”: loss<30 %; Gain 1: gain <30%; Gain 2: gain 30-50%; Gain 3: gain 50-80%; Gain 4: gain 80-100%; Gain 5: gain
100%.
A2 2020 A2 2050 A2 2080 B2 2020 B2 2050 B2 2080 1 Cyrtopodion scabrum Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 2 Hemidactylus flaviviridis Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 3 Hemidactylus robustus CR CR EN EN CR CR 4 Hemidactylus turcicus Gain 2 Gain 3 Gain 3 Gain 2 Gain 3 Gain 3 5 Pristurus flavipunctatus EN EN EN VU EN EN 6 Ptyodactylus guttatus VU EN CR EN EN EN 7 Ptyodactylus hasselquistii Gain 1 Gain 3 Gain 5 Gain 1 Gain 4 Gain 5 8 Ptyodactylus siphonorhina LC VU EN LC VU EN 9 Stenodactylus mauritanicus Gain 1 Gain 1 Gain 1 Gain 1 Gain 1 Gain 1 10 Stenodactylus petrii LC LC VU Gain 1 LC LC 11 Stenodactylus sthenodactylus Gain 4 Gain 5 Gain 5 Gain 4 Gain 5 Gain 5 12 Tarentola annularis Gain 4 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 13 Tarentola mauritanica Gain 1 VU EN LC VU EN 14 Tarentola mindiae EN CR CR EN CR CR 15 Tropiocolotes bisharicus LC Gain 1 LC Gain 1 LC LC 16 Tropiocolotes nattereri Gain 3 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 17 Tropiocolotes steudneri Gain 1 Gain 1 Gain 1 Gain 1 Gain 1 Gain 1 18 Tropiocolotes tripolitanus LC LC VU LC VU VU 19 Agama spinosa LC LC EN LC VU EN 20 Laudakia stellio Gain 1 LC EN Gain 3 Gain 1 LC 21 Pseudotrapelus sinaitus Gain 1 Gain 3 Gain 4 Gain 1 Gain 3 Gain 3 22 Trapelus mutabilis VU EN CR VU EN CR 23 Trapelus pallidus Gain 2 Gain 3 Gain 4 Gain 1 Gain 3 Gain 3 24 Trapelus savignii Gain 1 Gain 4 Gain 5 Gain 1 Gain 3 Gain 5 25 Uromastyx aegyptia LC VU EN VU VU VU 26 Uromastyx ocellata LC LC VU LC LC VU 27 Uromastyx ornata Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 28 Chamaeleo africanus Gain 2 Gain 3 Gain 5 Gain 1 Gain 1 Gain 3 29 Chamaeleo chamaeleon Gain 4 Gain 5 Gain 5 Gain 4 Gain 5 Gain 5 30 Acanthodactylus aegyptius LC LC VU LC LC VU 31 Acanthodactylus boskianus Gain 1 Gain 1 Gain 2 Gain 1 Gain 1 Gain 1 32 Acanthodactylus longipes LC VU EN Gain 1 VU EN 33 Acanthodactylus pardalis Gain 2 Gain 3 Gain 1 Gain 3 Gain 3 Gain 2 34 Acanthodactylus scutellatus Gain 2 Gain 3 Gain 3 Gain 2 Gain 3 Gain 3 35 Mesalina bahaeldini Gain 2 Gain 1 Gain 1 Gain 1 Gain 2 Gain 1 36 Mesalina guttulata Gain 2 Gain 3 Gain 5 Gain 2 Gain 3 Gain 5
A2 2020 A2 2050 A2 2080 B2 2020 B2 2050 B2 2080 37 Mesalina olivieri Gain 4 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 38 Mesalina pasteuri LC Gain 3 Gain 5 LC Gain 3 Gain 5 39 Mesalina rubropunctata Gain 3 Gain 5 Gain 5 Gain 3 Gain 5 Gain 5 40 Ophisops occidentalis Gain 1 LC LC Gain 1 Gain 1 LC 41 Varanus griseus Gain 1 Gain 3 Gain 4 Gain 1 Gain 3 Gain 3 42 Varanus niloticus Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 43 Chalcides cf. humilis Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 Gain 5 44 Chalcides ocellatus Gain 1 Gain 1 Gain 1 Gain 1 Gain 1 Gain 1 45 Eumeces schneiderii LC EN CR LC EN CR 46 Scincus scincus LC LC VU Gain 1 LC VU 47 Sphenops sepsoides Gain 1 Gain 1 EN Gain 1 Gain 1 LC 48 Trachylepis quinquetaeniata Gain 3 Gain 5 Gain 5 Gain 3 Gain 5 Gain 5 49 Trachylepis vittata Gain 3 Gain 5 Gain 5 Gain 3 Gain 5 Gain 5 50 Leptotyphlops cairi Gain 1 Gain 3 Gain 5 Gain 1 Gain 2 Gain 4 51 Leptotyphlops macrorhynchus LC VU EN LC VU EN 52 Eryx colubrinus Gain 1 Gain 1 Gain 3 Gain 1 Gain 1 Gain 2 53 Eryx jaculus VU EN CR VU EN EN 54 Eirenis coronella Gain 2 Gain 1 LC Gain 3 Gain 2 Gain 1 55 Lytorhynchus diadema Gain 1 LC LC Gain 1 Gain 1 LC 56 Macroprotodon cucullatus Gain 2 Gain 3 Gain 5 Gain 2 Gain 3 Gain 3 57 Malpolon moilensis VU EN CR EN EN CR 58 Malpolon monspessulanus Gain 1 LC VU Gain 1 LC VU 59 Natrix tessellata Gain 1 Gain 1 Gain 2 Gain 1 Gain 1 Gain 2 60 Platyceps florulentus Gain 1 Gain 1 Gain 2 Gain 1 Gain 2 Gain 1 61 Platyceps rogersi Gain 3 Gain 5 Gain 5 Gain 2 Gain 4 Gain 5 62 Platyceps saharicus LC Gain 1 Gain 2 LC Gain 1 Gain 1 63 Psammophis aegyptius Gain 2 Gain 1 Gain 1 Gain 1 Gain 2 Gain 1 64 Psammophis schokari Gain 1 LC EN Gain 1 Gain 1 VU 65 Psammophis sibilans LC LC Gain 1 LC LC LC 66 Spalerosophis diadema Gain 1 Gain 2 Gain 2 Gain 1 Gain 2 Gain 2 67 Telescopus dhara LC LC VU LC VU VU 68 Naja haje LC VU EN LC VU EN 69 Naja nubiae Gain 3 Gain 5 Gain 3 Gain 4 Gain 3 Gain 5 70 Walterinnesia aegyptia LC VU EN VU VU VU 71 Cerastes cerastes Gain 3 Gain 4 Gain 5 Gain 2 Gain 4 Gain 4 72 Cerastes vipera Gain 1 VU CR LC LC EN 73 Echis coloratus Gain 1 Gain 1 Gain 1 LC LC Gain 1 74 Echis pyramidum LC LC LC LC LC LC 75 Testudo kleinmanni Gain 1 Gain 2 Gain 2 Gain 1 Gain 1 Gain 1
Abbreviations used: Critically Endangered “CR”: loss>80%; Endangered “EN”: loss 50-80%; Vulnerable “VU”: loss 30-50%; Least Concern “LC”: loss<30 %.
A2 2020 A2 2050 A2 2080 B2 2020 B2 2050 B2 2080 1 Cyrtopodion scabrum LC LC LC LC LC LC 2 Hemidactylus flaviviridis LC LC LC LC LC LC 3 Hemidactylus robustus CR CR EN EN CR CR 4 Hemidactylus turcicus LC LC LC LC LC LC 5 Pristurus flavipunctatus EN EN EN EN EN EN 6 Ptyodactylus guttatus EN EN CR EN EN CR 7 Ptyodactylus hasselquistii LC LC LC LC LC LC 8 Ptyodactylus siphonorhina VU EN CR VU EN EN 9 Stenodactylus mauritanicus LC LC LC LC LC LC 10 Stenodactylus petrii LC LC VU LC LC LC 11 Stenodactylus sthenodactylus LC LC LC LC LC LC 12 Tarentola annularis LC LC LC LC LC LC 13 Tarentola mauritanica LC VU EN LC VU EN 14 Tarentola mindiae EN CR CR EN CR CR 15 Tropiocolotes bisharicus LC LC VU LC LC LC 16 Tropiocolotes nattereri LC LC LC LC LC LC 17 Tropiocolotes steudneri LC VU EN VU VU EN 18 Tropiocolotes tripolitanus LC VU VU LC VU VU 19 Agama spinosa LC VU EN VU VU EN 20 Laudakia stellio LC VU EN LC LC VU 21 Pseudotrapelus sinaitus LC LC LC LC LC LC 22 Trapelus mutabilis VU CR CR EN EN CR 23 Trapelus pallidus LC LC LC LC LC LC 24 Trapelus savignii LC LC LC LC LC LC 25 Uromastyx aegyptia EN EN EN EN EN EN 26 Uromastyx ocellata LC LC EN LC LC VU 27 Uromastyx ornata LC LC LC LC LC LC 28 Chamaeleo africanus LC LC LC LC LC LC 29 Chamaeleo chamaeleon LC LC LC LC LC LC 30 Acanthodactylus aegyptius LC VU EN LC VU VU 31 Acanthodactylus boskianus LC LC LC LC LC LC 32 Acanthodactylus longipes VU EN CR VU EN EN 33 Acanthodactylus pardalis LC LC LC LC LC LC 34 Acanthodactylus scutellatus LC LC LC LC LC LC 35 Mesalina bahaeldini LC LC VU LC LC LC 36 Mesalina guttulata LC LC LC LC LC LC 37 Mesalina olivieri LC LC LC LC LC LC
A2 2020 A2 2050 A2 2080 B2 2020 B2 2050 B2 2080 38 Mesalina pasteuri VU LC LC VU VU LC 39 Mesalina rubropunctata LC LC LC LC LC LC 40 Ophisops occidentalis LC LC VU LC LC LC 41 Varanus griseus LC LC LC LC LC LC 42 Varanus niloticus LC LC LC LC LC LC 43 Chalcides cf. humilis LC LC LC LC LC LC 44 Chalcides ocellatus LC LC VU LC LC VU 45 Eumeces schneiderii LC EN CR LC EN CR 46 Scincus scincus LC VU EN LC LC VU 47 Sphenops sepsoides LC VU EN LC VU EN 48 Trachylepis quinquetaeniata LC LC LC LC LC LC 49 Trachylepis vittata LC LC LC LC LC LC 50 Leptotyphlops cairi LC LC LC LC LC LC 51 Leptotyphlops macrorhynchus VU VU EN VU VU EN 52 Eryx colubrinus LC LC LC LC LC LC 53 Eryx jaculus VU EN CR VU EN EN 54 Eirenis coronella LC LC VU LC LC LC 55 Lytorhynchus diadema LC LC LC LC LC LC 56 Macroprotodon cucullatus LC LC LC LC LC LC 57 Malpolon moilensis EN EN CR EN EN CR 58 Malpolon monspessulanus LC LC VU LC LC VU 59 Natrix tessellata LC LC LC LC LC LC 60 Platyceps florulentus LC LC VU LC LC VU 61 Platyceps rogersi LC LC LC LC LC LC 62 Platyceps saharicus VU VU VU VU VU EN 63 Psammophis aegyptius LC LC LC LC LC LC 64 Psammophis schokari LC VU EN LC VU VU 65 Psammophis sibilans VU VU VU VU VU EN 66 Spalerosophis diadema LC VU VU LC VU VU 67 Telescopus dhara VU EN EN VU EN EN 68 Naja haje LC VU EN LC VU EN 69 Naja nubiae LC LC VU LC LC LC 70 Walterinnesia aegyptia LC VU EN VU VU VU 71 Cerastes cerastes LC LC LC LC LC LC 72 Cerastes vipera LC EN CR LC VU EN 73 Echis coloratus LC LC LC LC LC LC 74 Echis pyramidum LC LC VU VU LC VU 75 Testudo kleinmanni LC LC LC LC LC LC
Category
CCCma CSIRO HadCM3 NIES99 Overall mean A2a B2a A2a B2a A2a B2a A2a B2a A2a B2a 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 Extinct 0 0 0 0 0 0 0 0 2 0 0 2 0 0 2 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 Critically Endangered 0 1 6 1 1 4 1 4 6 2 3 5 2 2 9 1 2 4 2 8 19 3 7 11 1 2 7 0 2 5 Endangered 2 7 8 4 4 8 1 5 9 2 5 7 2 7 7 1 7 8 4 8 8 5 10 10 2 6 13 4 6 10 Vulnerable 5 6 11 8 9 9 5 11 11 9 8 9 5 2 10 8 5 8 9 4 2 9 5 6 4 8 7 5 10 9 Least Concern 26 17 4 29 20 16 17 13 3 14 14 9 16 15 4 17 16 10 12 12 6 8 6 4 19 14 5 16 10 8 Gain 1 21 19 11 19 19 12 23 16 7 21 14 8 26 10 9 25 14 11 26 8 4 12 10 9 23 14 8 27 14 11 Gain 2 11 4 9 5 8 7 11 7 8 13 5 6 9 6 5 9 8 5 7 4 1 10 5 5 10 2 6 6 6 4 Gain 3 7 7 6 3 3 3 5 6 4 6 10 3 7 15 5 6 7 7 3 8 6 8 8 3 7 12 4 6 11 7 Gain 4 1 3 6 2 4 2 4 2 3 3 2 6 3 4 2 2 1 5 2 2 4 3 4 5 4 2 3 3 3 2 Gain 5 2 11 14 4 7 14 8 11 22 5 14 20 5 14 22 6 15 17 10 21 24 17 20 21 5 15 22 8 13 19
Table 13: Number of species at each species range change category (assuming no-dispersal)
Category
CCCma CSIRO HadCM3 NIES99 Overall mean A2a B2a A2a B2a A2a B2a A2a B2a A2a B2a 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 2020 2050 2080 Extinct 0 0 0 0 0 0 0 1 2 0 0 2 0 0 2 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 Critically Endangered 0 2 6 2 1 4 1 4 8 2 3 5 2 2 9 1 2 4 2 9 20 4 9 12 1 3 9 0 2 6 Endangered 4 10 14 7 6 12 4 10 15 5 8 12 4 10 15 5 10 10 8 13 11 8 14 14 5 10 16 7 10 15 Vulnerable 11 18 12 14 14 17 9 15 10 14 15 14 8 6 10 10 10 16 10 11 7 10 9 10 9 15 14 12 15 12 Least Concern 60 45 43 52 54 42 61 45 40 54 49 42 61 57 39 59 53 45 55 42 36 53 43 38 60 47 36 56 48 42
(a) Average
(b) CCCma (c) CSIRO
(d) HadCM3 (e) NIES99
Fig. 30: Number of species at each future species range change classification across global circulation models and the mean of different global circulation models (loss – unlimited dispersal).
Abbreviations used: EX: Extinct; CR: Critically Endangered; EN: Endangered; VU: Vulnerable; and LC: Least Concern.
(a) Average
(b) CCCma (c) CSIRO
(d) HadCM3 (e) NIES99
Fig. 31: Number of species at each future species range change classification across global circulation models and the mean of different global circulation models (gain – unlimited dispersal).
(a) Average
(b) CCCma (c) CSIRO
(d) HadCM3 (e) NIES99
Fig. 32: Number of species at each future species range change classification across global circulation models and the mean of different global circulation models (loss – no-dispersal).
Abbreviations used: EX: Extinct; CR: Critically Endangered; EN: Endangered; VU: Vulnerable; and LC: Least Concern.
(a) A2a (unlimited dispersal) (b) B2a (unlimited dispersal)
(c) A2a (no-dispersal) (d) B2a (no-dispersal)
Fig. 33: Percentage of each future specie range change classification assuming unlimited dispersal (a and b) and no-dispersal (c and d).
(unlimited dispersal)
(no-dispersal)
Fig. 34: The overall pattern of future mean species range change (mean percentage of gain or loss) under unlimited and no-dispersal assumptions.
(unlimited dispersal)
(no-dispersal)
Fig. 35: Average future range change across different taxonomic groups assuming either unlimited dispersal or no-dispersal assumptions.
The taxa contain variable numbers of species; Snakes (25 species), Gekonidae (18), Lacertidae (11), Agamidae (9), Scincidae (7), Chamaeleonidae (2), Varanidae (2) and Turtles (1).
Fig. 36: The correlation between number of recorded and predicted species per Protected Area (n=23, r s = 0.49, p=0.12). Seven Protected Areas were not involved in the analysis as they do not have any recorded or predicted reptile species.
Fig. 37: The correlation between the area of the Protected Area (in 100 km 2 ) and either of the number of recorded or predicted species per Protected Area. In both cases, seven Protected Areas were not involved in the analysis as they do not have any recorded or predicted reptile species (For recorded species, n=23, r s =0.638, P<0.005 – For predicted species: n=23, r s =0.46, P<0.05).
For the name and location of the Protected Areas: see Fig. 5. Only Protected Areas with current or future species existence are listed in the table.
Protected Areas
1 2 3 4 5 7 9 10 11 12 13 14 16 17 18 19 21 22 23 24 26 27 29
1
49 42 47 51 23 43 22 45 11 18 4
(UD)
A2a-2020
6 2 1 2 4 4 5 3 3 5
3 1 4 2 5 2 5 1 2 1
A2a-2050 4 6 3 1 3 6 3 5 6 5
4 4 4 2 7 3 5 1 2 2 1 A2a-2080 12 11 5 7 6 7 7 5 8 6
1 6 5 5 12 4 8 7 2 4 2 B2a-2020 7 4 2 2 3 5 5 3 2 6
4 1 3 2 6 2 6 3 2 4 1 B2a-2050 8 4 3 1 3 3 4 5 4 5
4 2 3 3 9 3 8 2 2 4 1 B2a-2080 9 8 3 2 5 5 6 4 4 9
6 3 4 3 12 3 8 4 2 3 2
(UD)
A2a-2020
2
3
1 2
2
1
1
3 2 2 1 4
A2a-2050 3 2 5 2 1 1 6 2 1 6
2 1 3 4
8 5 3 3 5 2 A2a-2080 4 5 6 7 4 5 7 5 3 1
3 2 8 4 1 9 7 3 3 5 4 B2a-2020 3 1 1 3
2 3 1 1 3
1 1 2 1
4 4 1 2 3 1 B2a-2050 4 1 3 3
4 5 3
7
1
4 3 1 6 6 4 2 5 3 B2a-2080 4 4 4 8 3 5 6 4 2 5
2 1 6 4 2 7 7 4 3 6 3
(ND)
A2a-2020
5 2 1 2 4 4 4 2 5 4 4 1 4 2 6 2 5 1 4 2 1 A2a-2050 5 7 3 1 4 5 4 5 7 5 5 5 4 2 8 3 5 1 3 3 2 A2a-2080 12 11 5 7 8 7 8 5 9 6 10 6 6 5 12 4 8 7 3 4 1 B2a-2020 8 3 2 2 3 6 5 3 4 6 4 1 4 2 7 2 6 3 4 3 2 B2a-2050 10 4 3 1 4 3 5 5 6 5 4 3 4 3 9 3 7 2 4 4 1 B2a-2080 10 8 3 2 5 6 7 4 7 9 7 5 5 3 12 3 8 5 3 3 2
Fig. 38: Current and future conservation prioritization ranked value (using Zonation algorithm - Additive benefit function). Colours range from grey (low conservation value) to dark red (high conservation value).
A2a
2020 2050 2080
B2a
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102 - A2a
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B2a
2020 2050 2080
Fig. 39: Future change in conservation prioritization value (using Zonation algorithm - Additive benefit function); calculated as the difference between future and current conservation prioritization value (Fig. 36). Grey colour indicates no change in prioritization value; dark red indicates high future decline, and dark green indicates high future increase.
Fig. 40: Current and future conservation prioritization ranked value (using Zonation algorithm – Core-Area function). Colours range from grey (low conservation value) to dark red (high conservation value).
A2a
2020 2050 2080
B2a
2020 2050 2080
104 - A2a
2020 2050 2080
B2a
2020 2050 2080
Fig. 41: Future change in conservation prioritization value (using Zonation algorithm – Core-Area function); calculated as the difference between future and current conservation prioritization value (Fig. 38). Grey colour indicates no change in prioritization value; dark red indicates high future decline, and dark green indicates high future increase.
Fig. 42: Mean prioritization value (± 95% confidence limits - using Additive benefit function) across Protected Areas (PAs – grey coloured) and non-Protected areas (non-PAs – white coloured) at current and future.
Fig. 43: Mean prioritization value (± 95% confidence limits - using Core-Area function) across Protected Areas (PAs – grey coloured) and non- Protected areas (non-PAs – white coloured) at current and future.
Model performance From the AUC values, the models of all species discriminated much better than random; with all species having a mean AUC value greater than 0.7, this indicated at least an overall moderate discrimination ability. About 73% of the species had a mean AUC value greater than 0.9, indicating high discrimination ability (Franklin 2009). The negative correlations found between AUC and the area occupied, and with the extent of occurrence, concur with findings of other studies (e.g. Brotons et al. 2004; Elith et al. 2006; Hernandez et al. 2006). Species ecological characteristics have been shown to affect model accuracy, with more localized or rarer species being easier to model with higher accuracy than widespread species, regardless of sample size. This is probably because rare species are usually habitat specialists, show low environmental tolerance, and are environmentally or geographically restricted compared to widespread species (Stockwell & Peterson 2002; Brotons et al. 2004; Elith et al. 2006; Hernandez et al. 2006; Jiménez-Valverde et al. 2008; Franklin et al. 2009; Newbold et al. 2009b). Widespread species are more likely to be generalists occupying a wide range of habitats and climates, making it difficult to distinguish between suitable and unsuitable habitats (Franklin et al. 2009). This result may require further scrutiny (Elith et al. 2006), as it is very easy to get high AUC scores when modelling species distributions with low relative occurrence area (the proportion between the extent of species records and the extent of the study area – i.e. high extrapolation) (Jiménez-Valverde et al. 2008; Lobo et al. 2008). The smaller is the relative occurrence area (localized, rare, or endemic species), the greater is the number of available absences outside the limits of species records and better the model describes the data; this may be an inevitable result for species with small relative occurrence areas (Jiménez-Valverde et al. 2008; Lobo et al. 2008). Accordingly, some have advised against using AUC to compare model performances of species having different relative occurrence areas (Lobo et al. 2008). Given that most of the Egyptian herpetofauna (about 60%) are narrowly distributed, occupying less than 10% of Egypt’s area (Baha El Din 2006a), this may be the reason for
Variables contributing to the models Choosing the most appropriate variables that limit species distributions is a challenge in many species distribution modelling studies. They should be selected on the basis that they are ecologically meaningful and have high explanatory power (Beaumont et al. 2008). Environmental variables are substitutes, in general, for those variables that affects species distribution directly through physiological mechanisms, so poor selection of the variables used in the model (or the unavailability of data for variables thought to have direct effects on species distributions) may affect the association between the species and the climate (Araújo & Peterson 2012). In this study, I used available environmental layers thought to have an ecological meaning. Altitude was found to be the most effective variable for many species. The use of the two NDVI variables in the modelling of Egyptian reptiles proved to be not very useful, and probably future studies done on the same species and scale should not use them. Maximum NDVI was the most important variable only for Echis coloratus (although this information should be interpreted with caution), with an average permutation importance across species of 5.2 ± 5.9. The variable recording the difference between maximum and minimum NDVI was not the most important variable for any species, with an average permutation importance across species of 2.6 ± 4.1. NDVI has
108 - been used in many species distribution modelling studies done on a variety of species groups; including mammals (Torres et al. 2010; Hu & Jiang 2011; Soultan 2011), reptiles (Zabalaga 2009; Costa et al. 2010; Kgosiesele 2010; Taheri 2010; Carvalho et al. 2011; de Pous et al. 2011; Huang et al. 2011), amphibian (Tarkhnishvili et al. 2009; Beukema et al. 2010), birds (Niamir 2009), and even fungi (Flory et al. 2012). Some studies show a high importance of NDVI in species distribution modelling applications (e.g. Egbert et al. 2002; Anderson et al. 2006; Kgosiesele 2010; Taheri 2010), while others show only minor contributions (e.g. Torres et al. 2010; Soultan 2011). In Soultan’s study on the effect of climate change on Egyptian antelopes, NDVI had only a minor contribution to the models (Soultan 2011). As most of Egypt area is almost bare, except the Nile Valley and the Nile Delta, a large proportion of Egypt has very small NDVI values, making their use in modelling Egyptian fauna questionable. The habitat map produced by the BioMAP project seems to have made only a low contribution to the reptile models (with an average permutation importance across species of 3.9 ± 5.1), the most important variable for just one species (Leptotyphlops macrorhynchus). This may be because of the series of conversions and rescaling processes that converted it from vector to a relatively coarse raster format, or because many of the habitat categories are correlated with other variables, such as altitude. Neither slope nor aspect were used to run the models, although they are thought to affect the reptile distributions. They have been used in two other species distribution modelling studies done on Egyptian antelopes and a relatively high contributions to the final models were found (El Alqamy et al. 2010; Soultan 2011). Having an average indication of either slope or aspect across Egypt at the coarse resolution used here (~ 5 km) seems not to be that helpful. Giving just one value for either of them on a grid of 25-km 2 squares does not provide accurate information (especially with the moderate degree of uncertainty associated with some species records). Involving them in further studies at smaller scales may be more useful; these would need high- resolution variables and records with only minor levels of positional uncertainty.
109 - Species richness and turnover The pattern of species richness of the Egyptian herpetofauna from this study is consistent with the findings of (Baha El Din 2006a - see Fig. 44). The main pattern of herpetofauna hotspots in Egypt was found to be around the greater Cairo (including Wadi El-Natrun and Fayoum area), North Sinai, Gebel Elba area, and margins of Nile Delta eastwards to the Suez Canal area. Despite the high degree of concordance, this study shows fewer numbers of species in the high mountains of South Sinai, the Gebel Elba area and the western parts of the Mediterranean coast.
Fig. 44: The number of recorded amphibian and non-marine reptile species per a grid of half degree, comparing the results of (Baha El Din 2006a) (left) to the results of this study (right).
The current predicted pattern of species richness (using either probability or thresholded distributions) is consistent with the recorded species richness, but gives more emphasis to the Suez Canal area, western Mediterranean coast, both sides of the Suez Gulf and a narrow strip across the northern part of the Nile Delta. Gathering information from other studies discussing the species richness pattern of different taxonomic groups in Egypt is very helpful to show which areas share highest biodiversity or require more protection. Although produced with different methodologies, adding up species richness maps of this study to those of butterflies and mammals (Gilbert & Zalat 2008; Basuony et al. 2010) indicates the most important biodiversity areas. Using the accumulated pattern of species richness of these three groups, the hotspots of biodiversity were at the periphery of greater Cairo, in the high mountain areas of South Sinai, the coastal areas of the Aqaba and
Fig. 45: Accumulated species richness map of three taxonomic groups (butterflies, mammals, and reptiles), showing areas of high species richness. Species richness maps for butterflies and mammals were obtained from the results of (Gilbert & Zalat 2008; Basuony et al. 2010)
There are no published studies known to me that discuss the effect of climate change on Egyptian reptiles (see above), obviating any comparison of main findings of this study to others. Information on the reptiles in adjacent countries (e.g. Sudan and Libya) is very limited, making it not possible to compare patterns of species distributions, or make assumptions about possible compensations or migrations of Egyptian reptiles as a result of climate change. Areas predicted to lose a high number of species in the future (and so potentially require more attention; assuming either dispersal abilities) are the Suez Canal area, coastal areas of both Suez and Aqaba gulfs, Wadi El-Natrun, around the greater Cairo, Siwa oasis and small inland wadis of the Red Sea. Other areas are predicted to benefit from climate change, increasing their number of species (assuming unlimited dispersal); these include the area between the west Mediterranean coast to the Qattara Depression, middle to north Sinai, Red Sea coastal areas, Western Desert oases, and southern parts of the Nile Valley (for more details, see above). This indicates that some areas
Range Changes & important species for conservation Two species are predicted to loss their entire suitable habitats in at least one future projection: Tarentola mindiae and Hemidactylus robustus. Tarentola mindiae is a near endemic species recorded just from northwest Egypt and northern Cyrenaica (eastern Libya); its distribution in Egypt is restricted to Siwa oasis, the Qattara Depression and their periphery (Baha El Din 2006a -
Area prioritization for conservation & Protected Areas coverage In my study, Protected Areas had higher mean prioritization value (currently, and in the future) compared to outside the protectorates using both additive benefit and core area functions of the Zonation algorithm. Using additive benefit function, areas with current high prioritization value are the Suez Canal area, the Nile Valley and its Delta, the Qattara Depression, high elevation wadis in South Sinai, and the coastal areas of the Red Sea, the Mediterranean Sea and the Aqaba & Suez Gulfs. These areas also show high species richness, reflecting the nature of additive benefit function which gives more weight to locations with high species richness (Moilanen et al. 2012). Using core-area function, areas with current high prioritization values are high elevation wadis in South Sinai, Gebel Elba, Siwa oasis, the Suez Canal area, the Red Sea coast, the Mediterranean coast and the Nile Valley. There is not much difference between the overall patterns of areas of high prioritization value using either function, with overall higher prioritization value using additive benefit function (especially along the north Mediterranean coast from Sallum to Rafah, Wadi El-Natrun, Suez Canal area and around Cairo). This
The limitations of projecting into the future Using species distribution models for extrapolations is risky and must be treated carefully (Elith et al. 2010; Araújo & Peterson 2012). Possible uncertainties resulting from extrapolation beyond the limits of training data can be assessed from the calculation of the ‘Multivariate Environmental Similarity Surfaces’ (MESS), available in recent versions of Maxent (Elith et al. 2010). MESS is a measure of the similarity of any given pixel to a reference set of pixels of chosen predictor variables; it is used to determine novel climates by giving negative values for dissimilar pixels where at least one variable has a value outside the training range (Elith et al. 2010; Elith et al. 2011). Novel climates can then be used as a mask to inhibit the use of certain areas by the models, or as a measure of prediction uncertainty (Elith et al. 2010). In my study, MaxEnt produced MESS maps at each possible combination of global circulation models, emission scenarios and time slices. The mean MESS value across different global circulation models in Egypt is shown in Fig. 47; depicting which areas are predicted to have novel climate conditions in the future at each possible combination of emission scenarios and time slices. MESS output maps are continuous maps with values ranging from -100 (red) to +100 (blue). Positive values indicate similar future climate conditions compared to the current (the darker the blue, the higher is the similarity), while the negative values indicate locations of dissimilar climate compared to the current (the darker is the red, the higher dissimilarity). Under both emission scenarios at 2020 and 2050 and under the B2a scenario at 2080, areas with predicted non-analogue climates (i.e. the most dissimilar novel climates) are located at east of El-Gilf El-Kebir, the Red Sea coast from Mersa Alam southwards, south of Wadi El-Allaqi near the Sudanese borders, near Qena,
Conclusion My results suggest that about 10% of the Egyptian reptiles will suffer greatly in the future due to climate change. Eight species are predicted to lose up to 80% of their suitable habitats in the future. The current network of Egyptian Protected Areas appears to be inadequate to conserve Egyptian reptiles; new Protected Areas are probably needed at Wadi El-Natrun, the Qattara Depression, eastern side of Suez Canal, Gebel El-Gallal area, coastal areas of Aqaba Gulf, western Mediterranean coasts between Mersa Matruh and Sallum, and Gebel El-Hallal area in North Sinai. More strict protection is required in the Protected Areas of St Katherine, Siwa oasis and Gebel Elba.
(a) Tarentola mindiae (b) Hemidactylus robustus
(c) Eumeces schneiderii (d) Malpolon moilensis
(e) Trapelus mutabilis (f) Ceraster vipera
Fig. 46: Maps showing actual distribution and suitable distribution areas for some Egyptian reptile species.
(g) Eryx jaculus (h) Ptyodactylus guttatus
(i) Acanthodactylus longipes (j) Ptyodactylus siphonorhina
(k) Testudo kleinmanni
Fig. 46 cont. Maps showing actual distribution and suitable distribution areas for some Egyptian reptile species.
130 - A2a
2020 2050 2080 B2a
2020 2050 2080
Fig. 47: Average MESS (Multivariate Environmental Similarity Surfaces) maps of different global circulation models showing areas of future novel climates.
Colours ranges from blue (Positive values - similar future climate conditions compared to the current; the darker the blue, the higher is the similarity) to Red (negative values - locations of dissimilar climates compared to the current; the darker is the red, the higher dissimilarity). Results at the areas marked with dark red colour (non- analogous climates) should be interpreted with caution.
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