Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa

Jun 21, 2020·
Ahmed El-Gabbas
,
Francis Gilbert
,
Carsten F. Dormann
· 68 min read
Connectivity
Abstract

Background: Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion.
Results: Focal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt’s protected areas for conserving fauna was low.
Conclusions: For SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps.

See also:

  • Appendix 2
  • A shiny app for priotization results of the 2,560 zonation runs and interactive spatial distribution of Egyptian fauna.

 

Type
Publication
BMC Ecology (2020)  —   20, 35
Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa Running title: User decisions affect conservation planning Authors: Ahmed El-Gabbas1*, Francis Gilbert2, Carsten F. Dormann1 1 Department of Biometry and Environmental System Analysis, University of Freiburg, D-79106 Freiburg, Germany 2 School of Life Sciences, University of Nottingham, Nottingham, United Kingdom * Corresponding author – E-Mail: elgabbas@outlook.com Ahmed El-Gabbas: http://orcid.org/0000-0003-2225-088X Francis Gilbert: https://orcid.org/0000-0002-2727-4103 Carsten F. Dormann: http://orcid.org/0000-0002-9835-1794 Abstract: Background: Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion. Results: Focal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt’s protected areas for conserving fauna was low. Conclusions: For SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps. Keywords: Data-poor countries; Protected Areas; Sampling bias; Spatial Conservation Prioritisation; Species Distribution Models; Zonation. Background: Biological diversity is declining, with many species facing extinction in response to multiple interacting anthropogenic factors [1–5]. Protected areas (PAs) represent a core response strategy, currently covering about 15% of the global land area [6]. However, PAs are often bedevilled by lack of resources and trained personnel, isolation, and poor management [4, 7–9]. They are declared on expert opinion or on aesthetic value, neither of which is likely to ensure biodiversity persistence [10–12]. As a consequence of political opportunity rather than conservation assessment, many PAs are biased towards remote, uneconomic and low-cost areas, regardless of their biodiversity value [13, 14]. The Convention on Biological Diversity (CBD) agreed a Strategic Plan for Biodiversity 2011-2020, including the Aichi targets [15], two of which address threatened species (target 12) and an expansion of the global PA network to cover at least 17% of terrestrial land and 10% of coastal and marine areas by 2020 (target 11). Implementing these requires substantial effort to avoid creating PAs that exist purely as legal fictions [‘paper parks’: 16]. PAs should be ecologically representative, effectively managed, well connected, and based on sound science to include biodiversity and ecosystem services [15]. A spatially explicit suitability assessment for PAs needs to consider at least the following elements: (1) indicator taxa; (2) distribution data; and (3) connectivity of sites to facilitate movement. Ecologically coherent PAs aim to maximise representation and persistence of a wide range of habitats, ecosystems and species [3, 10, 13]. Since it is impossible to conserve all species and habitats simultaneously, limited conservation resources should be strategically and effectively prioritised [3]. Spatial conservation prioritisation (SCP) uses computational tools to prioritise areas for conservation actions [13]. It requires comprehensive, high-resolution, up-to-date data on the distribution of biodiversity, but such data are usually incomplete or do not exist at all, making it challenging to develop reliable assessments in data-poor countries [1, 8]. Such analyses are frequently performed using biodiversity surrogates, i.e. well-studied taxa representing biodiversity as a whole [13, 17]. Surrogates vary in their representativeness [3, 7, 18], and their choice is more often driven by data availability than ecological appropriateness [19]. Potential distribution maps from species distribution models (SDMs) are frequently used in conservation planning to overcome the lack of adequate surveys [20]. SDMs are statistical models that relate data on species presence (and absence) to the environment to predict potential distribution [21]. Despite their computational convenience, SDMs are subject to multiple sources of uncertainty [20, 22]: location, detectability, spatial autocorrelation, algorithm, and environmental predictors. Spatial sampling bias inherent in opportunistic presence-only data (the typical case in data-poor situations) can also lead to biased estimates of suitability [23], for example towards easily accessible areas [24]. Methods to correct for sampling bias [e.g. 23, 25] should be considered in planning [26]. Landscape connectivity influences species persistence through dispersal, source-sink dynamics, colonisation and gene flow [27]. Maintaining connectivity between PAs improves their ecological functioning and is now considered pivotal for effective planning [28–30]. Well-connected PAs facilitate the movement of widespread species and gene flow among patchily distributed populations, improving persistence under future land-use and climate change [18]. If connectivity is not included, SCP can prioritise isolated, fragmented, and small sites insufficient for long-term persistence in fragmented landscapes. Applying any SCP software involves decisions on settings, which inevitably affect the results [3, 26, 31, 32]. We assess the sensitivity of SCP in data-poor situations (represented by Egypt) to common sources of uncertainty. We quantify variability in the results of Zonation to distributional data quality and user-defined Zonation parameters, including: 1) biodiversity surrogate (butterflies, reptiles, mammals, or all three groups together); 2) SDM algorithm (Maxent vs elastic net); 3) with vs without weighting by prediction uncertainty; 4) with vs without bias correction; 5) different options for integrating connectivity; 6) weighting species by Red-List status; 7) different rules of priority; and 8) accounting for existing PAs vs a clean-slate situation. Although changing any of these factors will change the results, we assess here which dominate. We then evaluate the effectiveness of the current PA network in the conservation of the Egyptian fauna, and then map important areas for future PA expansions in Egypt. Methods: Study species and data: The main source of the presence data was the database collated by the BioMAP project [Biodiversity Monitoring and Assessment Project, 2004-2008; Butterflies: 33, Reptiles: 34, Mammals: 35], with revisions and additions from subsequent fieldwork and literature (see Appendix S1). We excluded 76 species with few records (<8 unique pixels and <5 spatial blocks), as they make spatial-block cross-validation (see below) impossible. The final species list comprises a total of 178 species (32 butterfly, 75 reptile, and 71 mammal species; Table S1). SDMs & sampling bias: We used two SDM algorithms to model potential distributions: Maxent [ver. 3.3.3k: 36] and elastic net [37; implementing a down-weighted Poisson regression], both under the point-process modelling framework [38]. As the focus of this study is the evaluation of Zonation, we only give an overview of the SDM-approach here [details in 39]. Models were calibrated using five-fold spatial-block cross-validation, resulting in a mean prediction for each species. Uncertainty in the prediction (predictive consistency) was computed as the average spatial congruence [Schoener’s D; 40] for all ten possible pairs of cross-validated predictions; this served as a weight in Zonation (see below). We assessed how much correcting for sampling bias changed the results by supplying Zonation with two sets of prediction maps, without correction (models fitted using only environmental variables), and with correction [using model-based bias correction: 25]. In the latter, SDMs were fitted including a bias layer of accessibility variables (distances to closest roads, cities, and PAs) and adjusting predictions to a constant accessibility of zero distance [for more details see: 25, 39]. SCP & potential sources of uncertainty: We used Zonation [version 4.0; 2, 41] to prioritise the Egyptian landscape for conservation. The Zonation outputs include a priority rank map and performance curves. The former represents a nested hierarchy of conservation value (top 5% cells are within the top 10%, etc.), showing the priority areas [41]. Zonation starts with the full landscape, and then iteratively removes the cells with the smallest marginal loss of conservation value (subject to species-specific weights, cost, connectivity, etc.). Performance curves demonstrate the quality of the solution by quantifying the fraction of the original distribution of each species (or their average) retains in response to successive removals of cells from the landscape [42; see Fig. S1 for a performance curve example]. Zonation uses different rules to calculate the marginal loss of conservation value and therefore the order of cell removal: additive-benefit function (ABF) and core-area zonation (CAZ) emphasise different but complementary aspects of biodiversity [41]. ABF emphasises species richness and is more appropriate for surrogates; CAZ emphasises species rarity by maintaining high-quality locations regardless of the local richness, and is more appropriate for the actual conservation targets [42]. To be more realistic, conservation planning should include the cost of implementation [43, 44], but obtaining cost data is frequently challenging [3]. No land prices (acquisition cost) are available across Egypt, and hence we constrained Zonation always to give low priority to agricultural and urban areas (Fig. S2), heavily modified and settled since Pharaonic times [45]. As different species do not have equal conservation importance, some species can be given a higher weight, influencing the order of cell removal [42]. We used two weighting methods: Red-List status and predictive consistency. We assigned weights between 1 and 5 according to national Red List assessments [Butterflies: 33, Reptiles: 34, Mammals: 35; Table S1]. We compared equal-weighted vs Red List-weighted solutions. We used predictive consistency as described above as a second weighting option. Mean spatial congruence ranges from zero (very high predictive uncertainty) to one (identical predictions, no uncertainty). We used both weighting schemes factorially, yielding four options of species weighting. Zonation offers different ways of integrating connectivity, such as boundary length penalty [28], distribution smoothing [2], and boundary-quality penalty [BQP; 30]. We used BQP, which requires a user-defined species-specific response to fragmentation (habitat loss) in the neighbour cells, represented by two parameters (radius and response curve). The radius defines the number of effective neighbour cells (~ distance) whose degradation or loss will affect the local cell value. The response curve describes the degree to which habitat loss in neighbour cells affects the local population [30]. Values are typically determined from habitat models [41] or expert ecological knowledge [e.g. 30, 46, 47]. Since both parameters are hard to obtain accurately, we used combinations of three response curves (low, moderate, and high effect; Fig. S3) and three radii (1-3 cells) plus no connectivity, making ten options in total. Finally, we allowed for two possibilities with the current PA network. First, we ignored it, allowing Zonation to prioritise any area of Egypt. Second, we forced Zonation always to retain the cells of the current PA network as the highest priority cells in Egypt. In total, we conducted 2,560 Zonation analyses: four surrogate groups (butterflies, reptiles, mammals and all three together) × two modelling algorithms (Maxent, elastic net) × two biases (without, with sampling-bias correction) × two cell-removal rules (ABF, CAZ) × four weighting schemes (none, Red-List, predictive uncertainty, and both) × ten connectivity options × two PA-integration options (without, with PA masking). An example reproducible R-code used to generate the input files for zonation can be found in this link: https://github.com/elgabbas/Conservation-Prioritisation-Sensitivity. To quantify the main drivers of uncertainty in Zonation’s output, we used the mean species representation (= average proportion of remaining distribution of species) in the top 17% priority cells as the response variable, estimated from performance curves. This value denotes the target area to be protected in Egypt by 2020. A randomForest analysis quantified the permutation importance of these factors. We also estimated the most important first-order interactions among factors using the mean sum of squares from a generalised linear model. Gap analysis and potential areas for PA expansion: To assess the effectiveness of Egypt’s PA network, we concentrated on Maxent SDM because it fitted slightly better on cross-validation than elastic net, using species weights from Red-List status and predictive consistency so as to give high weight to threatened species with high predictive consistency. To restrict the number of possibilities, we used an average connectivity (line 3 in Fig S3) with the radius set at two neighbour cells and the CAZ cell-removal rule to retain high-quality core areas for conservation. Where necessary, we make comparisons between the outputs for Maxent vs elastic net and CAZ vs ABF. To conduct the gap analysis, we first allowed Zonation to prioritise the Egyptian landscape without considering the positions of the existing PAs. We then used three ways to assess the current PAs: first, we measured the point-biserial correlation between the (continuous) priority rank and the binary allocation of each pixel either to a PA or to non-PA land; second, we quantified the spatial overlap between current PAs and top-priority sites of equal area using Jaccard’s similarity index; and third, by calculating Kendall’s correlation between the priority rank and the proportion of cells protected at each 1% rank intervals [following 48]. We expect PAs with good spatial allocation to have high values for biserial correlation, Jaccard’s index, and Kendall’s correlation. We identified top-priority areas outside PAs (candidate locations for PA expansion) by forcing PAs to have the highest priority and then determining the best locations required to expand to 17% of the Egypt’s area. Results: The main factor affecting the sensitivity of prioritisation (Fig. 1) was the choice of surrogate group, followed by correction for sampling bias, the strength of connectivity (response curve), and the choice of SDM algorithm. Correction for sampling bias led to higher species representation. The spatial pattern of the top-priority cells depended on how strong the dependence of local populations in each cell was on habitat loss in the neighbourhood. The steeper the response curve (high dependency), the more clumped and the less scattered the solution. This effect was more pronounced for CAZ than for ABF (compare Figs. 2 and S4). Maxent models led to higher species representation than elastic net. The different cell-removal rules, the radius of effective neighbour cells (connectivity), and weighting species by Red-List status or predictive consistency did not affect sensitivity much (Fig. 1); however, using ABF or weighting species by their Red-List status led to relatively lower species representation. The use of different landscape threshold percentages (10%, 17%, 25%) did not affect the overall relative importance; however, the choice of the surrogate group had lower importance when the top 10% priority of the landscape was used (Fig. S5). The most important interactions were as follows. Between sampling-bias correction and SDM algorithm, correction for sampling bias led to higher improvement for elastic net. Between the surrogate group and sampling-bias correction, correction led to greater improvement for mammals than for reptiles. Between the surrogate group and SDM algorithm, Maxent had higher species representation for butterflies and mammals (Fig. S6). We expected (by design) that incorporating the current PAs would lead to different spatial allocation of important sites than solutions that did not account for PAs. Indeed, PA incorporation was one of the most important factors affecting prioritisation (Fig. 1). The interactions between incorporating PAs and other important factors (sampling bias correction and the choice of SDM algorithm and surrogate group) were also significant (Fig. S6). When the Egyptian PAs were not incorporated, the spatial allocation of the top-priority cells varied depending on the choice of taxon, with the majority of cells outside current PAs (78-90% under CAZ, similar for ABF: Figs. 3 and S7). In other words, the overlap of priority cells with current PAs (Jaccard’s index) was very low (4.8-9.8%) and independent of the choice of the surrogate taxon, sampling-bias correction and connectivity. The correlation between current PAs and the overall priority ranking in Egypt was very weak (-0.11 to 0.14). There was a fair to good positive Kendall’s correlation between cell ranking and the fraction of cells protected at each 1% rank interval for mammals (τ = 0.72), butterflies (0.42), and all-species solutions (0.31), but a negative correlation for reptiles (−0.38; all for CAZ; see Fig. 4). When ABF was used, negative correlations occurred for all taxa (from −0.57 to −0.29) except butterflies (0.4; see Fig. S8). Similar results were found for elastic net, with non-significant correlations for reptiles and mammals for CAZ (Fig. S9). Performance curves showed that the top-priority sites equivalent in area to the current PAs retained an average species representation of about 22% (all-species analyses; Fig. 5, left panel), compared with only about 15% retained within the current PAs (Fig. 5, right panel). Results are similar for CAZ and ABF, with slightly higher values for elastic net (Figs. 5 and S10-11). For the separate taxa, top-priority sites again maintained a higher mean species representation (40% for butterflies; 35% for reptiles, and 33% for mammals). Urban and agricultural areas retained an average species representation of about 13% of all study species (Figs. 5 and S10-11), suggesting that although these areas are not crucial for species protection, they still have conservation importance for those species that depend on humans, either directly or via agriculture (e.g. rodents and some butterflies). When PAs were forced into the solution, expanding current PAs to 17% of Egypt almost doubled the average species representation (all-species: Figs. 5 and S10-11), with relatively higher values for elastic net. The pattern of top-priority sites varied slightly among species groups (Fig. S12), which together require more than 17% of the Egyptian landscape to conserve all three species groups simultaneously. We summarised the overall pattern of the top-priority sites by adding together the rankings of the top 17% cells of the four surrogate options (Figs. 6 and S13). Potential areas for PAs expansion include the Qattara Depression westwards to include the surroundings of the Siwa Oasis, the Mediterranean Coast west of El-Omayed, the Wadi El-Natrun area southwards, a coastal strip of North Sinai, the majority of central Sinai, coastal areas on both sides of the Suez Canal and the Gulf of Suez (including El-Galala mountains), inland wadis west of Hurghada, and the periphery of Wadi El-Gimal PAs (Figs. 6, S13 and S15). Discussion: Drivers of conservation prioritisation sensitivity: Target areas for conservation should maximise species representation and their long-term persistence for best use of limited resources. SCP is a method of identifying priority areas and assessing the effectiveness of reserves. However, its efficient use is hampered by uncertainties and the paucity of good-quality data. Many procedural decisions influence the robustness of the assessments, and therefore conservation practitioners should account for possible sources of uncertainty during SCP analyses [3, 26, 31, 32]. In this study, we quantified the sensitivity of prioritisation by varying user decisions experimentally. The main sources of uncertainty were the choice of surrogate group, sampling bias, the strength of the BQP response curve (i.e. the connectivity), and the choice of SDM algorithm, which collectively reflect our incomplete knowledge of species ecology and distribution. Conservation planning is sensitive to the choice of taxon used as surrogate for biodiversity [19]. Here, we used four surrogates for the Egyptian fauna. Although their priority rankings are (fairly) positively correlated (Table S2), choosing one strongly affects the mean species representation in the solution (Fig. 1), implying that they are not adequate to represent each other [7, 17]. As a consequence, even the collective use of all three will not adequately represent Egyptian biodiversity as a whole. The vertebrate groups only partially cover for butterflies, implying that data on other taxa is essential for comprehensive conservation planning in Egypt. Such data are currently not available for the majority of taxa, and hence improving sampling and data-sharing in data-poor countries are prerequisites to making SCP analyses more representative [19]. The issue of data quality also becomes apparent in the effect of sampling bias on SDM predictive performance. We used model-based bias correction to improve predictions for presence-only SDMs [25, 39, 49], and sampling bias was the second most important factor affecting prioritisation. Correction led to a different spatial allocation of important sites and greater representation of species. Accordingly, we used 'bias-free' predictions in the final assessments. The use of biased data for conservation planning identifies suboptimal conservation sites, which are weakly correlated with ideal reserves, leading to the requirement of more area for conserving fewer species [50]. Sampling-bias correction should be used for conservation planning in data-poor situations or when there are clear signs of bias in the available data. However, correction for sampling bias is not always successful [23, 39], and hence the improvement of data quality should be a priority. Maintaining connectivity among conservation sites is important for long-term conservation [9]. However, the effective integration of connectivity into SCP requires knowledge of home range, dispersal ability and sensitivity to habitat fragmentation of each species. This information is onerous to estimate correctly [31] and is unavailable for most species, especially for cryptic species and in data-poor countries. Connectivity methods that do not require information on species ecology are possible. One example is the boundary-length penalty [28], but this is a generic (not ecology-driven) method that maintains site aggregation via assigning a high penalty to solutions that have a high edge-to-area ratio, and hence does not account for habitat quality near the border [41]. Connectivity response curves strongly affected the spatial distribution of the top-priority sites (Figs. 2 and S4), and hence connectivity demands careful attention in SCP analyses. Our use of the low-connectivity response curve (line 2 in Fig. S3) did not lead to a different outcome compared to analyses which did not consider connectivity at all (line 1 in Fig. S3). In contrast, the use of medium or high response curves (lines 3 and 4 in Fig. S3) led to more aggregated top-priority sites (Figs. 2 and S4) and a lower mean species representation [46]. Maintaining connectivity between important sites leads to the inclusion of sites of low importance in the solution, and hence involves trade-offs [29, 31]. On the other hand, the radius of effective neighbour cells was not of much importance. As aggregated important sites are a safer investment than fragmented or isolated solutions [31], we conditioned our final analyses on a medium level of connectivity (line 3 in Fig. S3). However, this is not the ideal approach of integrating connectivity into Zonation when species differ in their sensitivity to habitat loss. The lack of relevant information for the Egyptian species is a common situation in data-poor countries. Although Maxent and elastic net had very similar performances when used under the point-process modelling framework [39, 49], their use here contributes to the sensitivity of prioritisation. Predictions from Maxent led to a higher mean species representation than elastic net. Similarly, Lentini & Wintle [19] found that the spatial allocation of priority sites was highly dependent on the SDM algorithm used. More than one SDM algorithm should probably be used, comparing the spatial pattern of their priority sites. However, the effect of modelling algorithm had little effect on the proposed sites for PA expansion (Figs. 6 and S13). The use of CAZ or ABF cell removal rules led to some variation in prioritisation. Each rule identified different top-priority sites, especially for reptiles and mammals (Fig. 3 vs S7). This effect was expected, because the calculation of the marginal loss of conservation value is different in both rules: CAZ emphasises species rarity, while ABF emphasises species richness [for details, see 41]. Their differences here can be attributed partially to the effect of connectivity, which affects CAZ more than ABF [Fig. 2 vs S4; 51]. The areas proposed for PA expansion were not too different whichever cell removal rule was used (Figs. 6 and S12-13). Weighting species by Red-List status was less important than the other factors described above, but it led to smaller mean species representation. Weighting species includes trade-offs, because species assigned higher weights will have a higher representation in the solution at the expense of other species [47]. In contrast, weighting by predictive consistency was not important, probably because spatial-block cross-validation produced high and not very variable values (median = 0.8; Fig. S14). The effectiveness of current Egyptian PAs and areas for potential expansion: Since 1983, thirty Egyptian PAs have been declared, covering approximately 14.6% of the total area of Egypt (Fig. S15), gazetted after fieldwork, literature reviews and recommendations of experts [45, 52]. Although the surveyed sites show good spatial coverage (Fig S16), they are inevitably incomplete and are taxonomically biased towards charismatic or well-known taxa. Ideally the majority of top-priority sites would be located inside PAs, but the high permutation importance of PA incorporation indicates the suboptimality of current Egyptian PAs. The performance curve for the top-priority sites irrespective of PAs is more concave (Fig. 5 left) than for current PAs (Fig. 5 right), meaning that the cells with the highest priority across Egypt have higher species representation than sites within PAs. Current PAs are reasonably correlated with the important sites for butterflies and when all species were used together, but only very weakly or even negatively for reptiles and mammals. The correlation between priority rankings across Egypt and the PAs was similarly very weak in all cases, as was the intersection between current PAs and top-priority sites (Jaccard’s index) for all taxa, cell-removal rules, and SDMs. Thus modelling shows that though slightly better for butterflies, the PA network does not cover species-rich areas for mammals well, and is also inadequate for reptiles in all scenarios. Previous studies evaluating the effectiveness of the Egyptian PAs have shown contrasting results. Newbold et al. [45] suggested that they are more effective for butterflies and mammals than random sites, containing higher than average species richness, and similar results were found for medicinal plants [53, 54]. However, simple species richness as an indicator is sensitive to sampling bias [55], fails to consider complementarity, and ignores important threatened species living in less-rich areas [56] and unique assemblages [5]. Therefore the use of species richness alone for conservation planning may create solutions that are too optimistic. In contrast, Zonation is a complementarity-based method, designed to ensure that important sites maximise species representation. Effective PAs should be resilient to climate change [57]. We did not consider climate change here so as to simplify the analysis of prioritisation sensitivity. Using Egyptian reptiles, the current PA network has a significantly higher Zonation ranking than unprotected areas, but the difference declines under climate change [34]. To prevent species loss under climate change, new PAs in Egypt are probably required for effective conservation in the future [34, 54, 58]. For example, the Suez Gulf coast, Qattara Depression and Red Sea Coast are important for medicinal plants under climate change [54]. These areas, along with the Gebel El-Galala area, Mediterranean Coast, and Central to North Sinai, are important for reptiles under climate change [34]. These assessments suggest areas similar to those proposed for PA expansion here (Figs. 6 and S13). It is interesting that, despite the large uncertainty explored in this study via the 2,560 SCP analyses, there is surprising congruence in the spatial distribution of the priority sites chosen by Zonation across all options. The frequency with which cells were chosen to be within the highest or lowest set of priority sites across all options is shown in Fig. S17. This encourages us to feel the final recommendations may be rather robust to the modelled uncertainties. The proper evaluation of Egypt’s PAs is in its infancy, and our analysis is inevitably incomplete. Our results should not be considered definitive, because of the unknown level of error and uncertainty accompanying species distribution data, distribution modelling and the prioritisation process. Conservation planning is sensitive to commission errors in predicted distribution maps [false presences - conserving areas lacking the species of interest; 24]. Although we attempted to minimise this error using spatial-block cross-validation, predicted distributions still have an unknown level of error. The use of only a single type of species response to habitat loss can also be criticised. We cannot guarantee that conserving the top-priority areas identified here will ensure long-term species persistence, because this requires explicit population modelling, such as population viability analysis [20, 28]. Some priority areas outside PAs are under great pressure from human uses (e.g. tourism on the Mediterranean and the Red-Sea coasts; mining in the Qattara Depression). The actual conservation value of the top-priority sites requires fine-scale field validation [3] to verify their suitability. Such validation must be undertaken by the Egyptian Nature Conservation Sector when planning for PA expansion. Likewise, our evidence for the low conservation efficiency of the current PA network should not encourage decision-makers in Egypt to alter current PAs until intensive good-quality data on a range of taxa become available. This will require much time and effort. Our aspirations for an expansion of the PA network should not distract from the urgent need for improving management within current PAs. Data quality greatly affects conservation planning [50]. Despite the endeavours of Egypt’s Nature Conservation Sector, many more initiatives are required to improve inventories within and outside PAs, and also effective ecosystem management within PAs. A national scheme for biodiversity data collection and sharing would greatly facilitate future conservation planning. Due to the limitations of currently available data, the effectiveness of the PAs will need to be re-evaluated using improved inventory data on a wider range of taxa (e.g. birds, amphibians, plants, invertebrates, etc.), and include climate change. Data on additional species would make the SCP analysis more robust and stable and better represents the overall Egyptian biodiversity [59]. Future evaluation should be run in close partnership with scientists, experts on Egyptian biodiversity, decision-makers, stakeholders and local communities to minimise the gap between doing research and its implementation [20, 57]. Conclusion: The robustness of conservation planning applications is subject to many sources of uncertainty which should be accounted for during the prioritisation process. Conservation planning in data-poor situations is sensitive to the selection of the surrogate group, correction for sampling bias, connectivity parameters, and the choice of modelling algorithm; collectively, these reflect data quality issues. This underlines the urgent need to improve data quality in the data-poor countries to enhance the usefulness of SDMs and conservation planning applications for long-term biodiversity conservation. We recommend the use of data on as many species groups as possible and more than one modelling algorithm to obtain a robust and stable conservation planning. Sampling bias can highly affect the efficiency of SCP output and therefore should be corrected for. Maintaining connectivity between top priority sites is essential for the effective long-term conservation of many species and therefore should be carefully integrated into conservation planning. However, as species-specific responses to habitat loss represents an important knowledge gap. We highlight the need for studies elaborating the response to habitat loss for less-studied species. Using currently available data on the Egyptian butterflies, reptiles, and mammals, the Egyptian protected areas network seems to be inefficient for wildlife conservation. We determined the top priority sites for further on-the-ground field evaluation as potential areas for protected areas expansion. Abbreviations: ABF: additive-benefit function; BQP: boundary-quality penalty; CAZ: core-area zonation; CBD: The Convention on Biological Diversity; PAs: Protected Areas; SCP: Spatial conservation prioritisation; SDMs: species distribution models. Declarations: Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and materials: The dataset analysed during the current study is presented in Appendix S1. Competing interests: The authors declare that they have no competing interests. Funding: AE-G is sponsored by the German Academic Exchange Service (DAAD) through a GERLS scholarship. The HPC “bwForCluster NEMO” is funded by the state of Baden-Württemberg through bwHPC and the German Research Foundation (DFG) through grant no INST 39/963-1 FUGG. BioMAP Project was funded by the Italian Cooperation (Debt Swap). Authors’ contribution: AE‐G, CFD, and FG contributed to idea and design of the study, and comments and revisions; AE-G contributed to data curation and statistical analysis, and first drafted the writing. All authors contributed critically to the drafts and gave final approval for publication. Acknowledgements: This work was performed on the computational resource ‘bwForCluster NEMO’. The authors acknowledge the support staff of the bwForCluster in making Zonation software available on the Centos Linux cluster. We appreciate the valuable comments from Alaaeldin Soultan on this manuscript.

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Figure 1: Permutation importance of factors affecting Zonation sensitivity across 2,560 option combinations (randomForest model). The dependent variable is the mean species representation in the top 17% priority cells. (For the top 10% and 25%, see Fig. S5.) Statistical interactions are included in the measure of variable importance. Figure 2: The spatial distribution of the top 17% priority cells for different connectivity options (using options CAZ, bias-free predictions, all study species, Maxent). The top-left map shows the pattern of important sites without connectivity integration. The second to fourth columns are for equivalent maps with steeper response curves (low, medium, and high connectivity; curves 2-4 in Fig. S3, respectively); while rows are for different numbers of effective neighbour cells (1 to 3). (Equivalent maps using ABF are shown in Fig. S4.). Figure 3: The spatial distribution of the top 12% priority cells (the darker, the higher the priority) for the four surrogates using core-area zonation, overlaid with current protected areas in Egypt (blue borders) (using bias-free predictions from Maxent). Equivalent maps using additive-benefit function are shown in Fig. S7.). Figure 4: The fraction of cells protected per Zonation rank for different surrogates using core-area zonation (using bias-free predictions from Maxent). The number on each panel represents Kendall’s correlation coefficient. (For equivalent results using additive-benefit function or elastic net, see Figs. S8-9). Figure 5: Performance curves for Zonation analyses (Maxent, CAZ, all species together). Left panel is without PA integration. Solid curves represent the average performance curve for all species or per species-group; while dashed lines represent the overall minimum and maximum performance curve per species group. The vertical grey line is for urban and agricultural areas (Fig. S2) forced to have low priority value; while the dashed vertical green line represents top priority sites existent in an area equals to the area covered by PAs. The right panel represents equivalent analysis with Egyptian PAs forced to have the highest priority scores. The vertical green line represents the area covered by PAs; while the vertical orange line represents the proposed areas for PAs expansion to 17% of Egypt. Performance curves are described in Fig. S1 and in the main text. For equivalent curves using ABF, see Fig. S10. Results for elastic net are shown in Fig. S11. Figure 6: The overall pattern of top priority sites using CAZ (left) or ABF (right), both using bias-free predictions from Maxent. Each map shows the summed rankings of the top 17% sites from the four surrogates used. Shading within current PAs (blue borders) are not shown to highlight the pattern for PA expansion (darker shading indicates higher rankings). The pattern for each species group is shown in Fig. S12. For equivalent maps using elastic net, see Fig. S13.

Supporting Information for “Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa” – BMC Ecology Ahmed El-Gabbas1*, Francis Gilbert2, Carsten F. Dormann1 1 Department of Biometry and Environmental System Analysis, University of Freiburg, D-79106 Freiburg, Germany 2 School of Life Sciences, University of Nottingham, Nottingham, United Kingdom ∗ To whom correspondence should be addressed; E-mail: elgabbas@outlook.com

Table S1: List of species used or excluded in this study. RL represents per-species national Red List status; while W represent the assigned weight

(A) Study species

Species	RL	W
Butterflies

1 Agrodiaetus loewii VU 3 2 Apharitis acamas VU 3 3 Azanus ubaldus LC 1 4 Belenois aurota NA 1 5 Borbo borbonica NA 1 6 Catopsilia florella NA 1 7 Chilades trochylus LC 1 8 Colias croceus NA 1 9 Colotis fausta VU 3 10 Danaus chrysippus LC 1 11 Deudorix livia LC 1 12 Euchloe aegyptiaca EN 4 13 Euchloe belemia DD 1 14 Gegenes nostrodamus LC 1 15 Hypolimnas misippus NA 1 16 Iolana alfierii LC 1 17 Junonia hierta LC 1 18 Lampides boeticus LC 1 19 Leptotes pirithous LC 1 20 Lycaena phlaeas NA 1 21 Melitaea deserticola VU 3 22 Papilio saharae VU 3 23 Pelopidas thrax LC 1 24 Pieris rapae LC 1 25 Plebejus philbyi VU 3 26 Pontia daplidice LC 1 27 Pontia glauconome LC 1 28 Spialia doris LC 1 29 Tarucus rosaceus LC 1 30 Vanessa atalanta NA 1 31 Vanessa cardui NA 1 32 Zizeeria karsandra LC 1

Reptiles

33 Acanthodactylus aegyptius LC 1 34 Acanthodactylus boskianus LC 1 35 Acanthodactylus longipes VU 3 36 Acanthodactylus pardalis VU 3 37 Acanthodactylus scutellatus LC 1 38 Agama spinosa LC 1 39 Cerastes cerastes LC 1 40 Cerastes vipera LC 1 41 Chalcides cf. humilis LC 1 42 Chalcides ocellatus LC 1 43 Chamaeleo africanus EN 4 44 Chamaeleo chamaeleon LC 1 45 Cyrtopodion scabrum LC 1 46 Echis coloratus LC 1 47 Echis pyramidum LC 1 48 Eirenis coronella VU 3 49 Eryx colubrinus VU 3 50 Eryx jaculus LC 1 51 Eumeces schneiderii LC 1 52 Hemidactylus flaviviridis VU 3 53 Hemidactylus robustus VU 3 54 Hemidactylus turcicus LC 1 55 Laudakia stellio LC 1 56 Leptotyphlops cairi EN 4 57 Lytorhynchus diadema LC 1 58 Macroprotodon cucullatus VU 3 59 Malpolon moilensis LC 1 60 Malpolon monspessulanus LC 1 61 Mesalina bahaeldini VU 3 62 Mesalina guttulata LC 1 63 Mesalina olivieri LC 1 64 Mesalina pasteuri VU 3 65 Mesalina rubropunctata LC 1 66 Naja haje LC 1 67 Naja nubiae VU 3 68 Natrix tessellata VU 3 69 Ophisops occidentalis VU 3 70 Platyceps florulentus LC 1 71 Platyceps rogersi LC 1 72 Platyceps saharicus LC 1 73 Pristurus flavipunctatus VU 3 74 Psammophis aegyptius LC 1 75 Psammophis schokari LC 1 76 Psammophis sibilans LC 1 77 Pseudotrapelus sinaitus LC 1 78 Ptyodactylus guttatus LC 1 79 Ptyodactylus hasselquistii LC 1 80 Ptyodactylus siphonorhina LC 1 81 Ramphotyphlops braminus NA 1 82 Scincus scincus LC 1 83 Spalerosophis diadema LC 1 84 Sphenops sepsoides LC 1 85 Stenodactylus mauritanicus VU 3 86 Stenodactylus petrii LC 1 87 Stenodactylus sthenodactylus LC 1 88 Tarentola annularis LC 1 89 Tarentola mauritanica LC 1 90 Tarentola mindiae VU 3 91 Telescopus dhara LC 1 92 Testudo kleinmanni VU 3 93 Trachylepis quinquetaeniata LC 1 94 Trachylepis vittata VU 3 95 Trapelus mutabilis LC 1 96 Trapelus pallidus LC 1 97 Trapelus savignii VU 3 98 Tropiocolotes bisharicus VU 3 99 Tropiocolotes nattereri LC 1 100 Tropiocolotes steudneri LC 1 101 Tropiocolotes tripolitanus LC 1 102 Uromastyx aegyptia LC 1 103 Uromastyx ocellata EN 4 104 Uromastyx ornata VU 3 105 Varanus griseus LC 1 106 Varanus niloticus VU 3 107 Walterinnesia aegyptia VU 3

Mammals

108 Hemiechinus auritus LC 1 109 Paraechinus aethiopicus LC 1 110 Crocidura olivieri VU 3 111 Rousettus aegyptiacus LC 1 112 Taphozous nudiventris VU 3 113 Taphozous perforatus LC 1 114 Asellia tridens LC 1 115 Rhinolophus clivosus LC 1 116 Rhinolophus mehelyi EN 4 117 Rhinopoma cystops LC 1 118 Rhinopoma microphyllum VU 3 119 Tadarida aegyptiaca VU 3 120 Tadarida teniotis VU 3 121 Nycteris thebaica LC 1 122 Eptesicus bottae VU 3 123 Hypsugo ariel VU 3 124 Otonycteris hemprichii LC 1 125 Pipistrellus kuhlii LC 1 126 Pipistrellus rueppellii VU 3 127 Plecotus christii LC 1 128 Lepus capensis LC 1 129 Allactaga tetradactyla EN 4 130 Jaculus jaculus LC 1 131 Jaculus orientalis VU 3 132 Eliomys melanurus EN 4 133 Acomys cahirinus LC 1 134 Acomys dimidiatus LC 1 135 Acomys russatus LC 1 136 Arvicanthis niloticus LC 1 137 Dipodillus campestris VU 3 138 Dipodillus dasyurus LC 1 139 Dipodillus mackilligini VU 3 140 Dipodillus simoni VU 3 141 Gerbillus amoenus LC 1 142 Gerbillus andersoni VU 3 143 Gerbillus floweri VU 3 144 Gerbillus gerbillus LC 1 145 Gerbillus henleyi LC 1 146 Gerbillus perpallidus LC 1 147 Gerbillus pyramidum LC 1 148 Meriones crassus LC 1 149 Meriones libycus LC 1 150 Meriones shawi EN 4 151 Mus musculus NA 1 152 Nesokia indica EN 4 153 Pachyuromys duprasi VU 3 154 Psammomys obesus LC 1 155 Rattus norvegicus NA 1 156 Rattus rattus NA 1 157 Sekeetamys calurus LC 1 158 Spalax aegyptiacus EN 4 159 Canis aureus DD 1 160 Vulpes rueppellii LC 1 161 Vulpes vulpes LC 1 162 Vulpes zerda EN 4 163 Acinonyx jubatus CR 5 164 Caracal caracal DD 1 165 Felis chaus LC 1 166 Felis margarita VU 3 167 Felis silvestris LC 1 168 Panthera pardus CR 5 169 Herpestes ichneumon LC 1 170 Hyaena hyaena LC 1 171 Ictonyx libyca EN 4 172 Mustela subpalmata VU 3 173 Procavia capensis LC 1 174 Equus asinus CR 5 175 Ammotragus lervia CR 5 176 Capra nubiana EN 4 177 Gazella dorcas VU 3 178 Gazella leptoceros EN 4

  (B) Excluded species

Butterflies Azanus jesous Chilades eleusis Colotis phisadia Elphinstonia charlonia Lycaena thersamon Carcharodus alceae Carcharodus stauderi Colotis danae Pseudotergumia pisidice Anthene amarah Apharitis myrmecophila Calopieris eulimene Charaxes hansali Chazara persephone Colotis chrysonome Colotis liagore Euchloe falloui Gomalia elma Melitaea trivia Pieris brassicae Polyommatus icarus Pseudophilotes abencerragus Pseudophilotes sinaicus Sarangesa phidyle Satyrium jebelia Tomares ballus Zegris eupheme Zizina otis

Reptiles Ablepharus rueppellii Atractaspis engaddensis Dasypeltis scabra Dolichophis jugularis Hemidactylus foudaii Hemidactylus mindiae Hemidactylus sinaitus Hemorrohis algirus Hemorrohis nummifer Latastia longicaudata Leptotyphlops macrorhynchus Leptotyphlops nursii Lycophidion capense Mesalina brevirostris Mesalina martini Ophisops elbaensis Ophisops elegans Philochortus zolii Platyceps sinai Psammophis punctulatus Pseuderemias mucronata Pseudocerastes persicus Ptyodactylus ragazzi Rhynchocalamus melanocephalus Telescopus hoogstraali Tropiocolotes nubicus Typhlops vermicularis

Mammals Crocidura floweri Crocidura suaveolens Pipistrellus deserti Crocidura religiosa Rhinolophus hipposideros Atelerix algirus Suncus murinus Barbastella leucomelas Nycticeinops schlieffeni Hystrix cristata Hystrix indica Gerbillus nanus Meriones sacramenti Meriones tristrami Vulpes cana Proteles cristata Ictonyx striatus Meles meles Vormela peregusna Genetta genetta Gazella gazella

Table S2: Kendall’s correlation between priority rankings of different surrogate groups (bias-free predictions) using core-area zonation (light grey) and additive-benefit function (dark grey); using Maxent (A) and elastic net (B).

(A) Maxent

Butterflies	Reptiles	Mammals	All species

Butterflies 0.28 0.21 0.30 Reptiles 0.47 0.40 0.49 Mammals 0.43 0.44 0.39 All species 0.41 0.49 0.44

(B) elastic net

Butterflies	Reptiles	Mammals	All species

Butterflies 0.31 0.20 0.31 Reptiles 0.54 0.40 0.54 Mammals 0.38 0.48 0.43 All species 0.47 0.56 0.43

Figure S1: An example performance curve produced during the Zonation analysis. This curve quantifies the proportion of features’ original distribution remaining (here, 14 species; y-axis) at each top fraction of the cells (x-axis). The x-axis denotes the priority ranking of the solution, with the highest rank site has a value of one, while the lowest rank site has a value of zero. The per-species performance curve is shown as grey curves, with their overall average in blue. It is possible in some Zonation analyses to handle hundreds of species, and hence the per-species performance curve can be hard to interpret. The overall mean (or per taxonomic group) performance curve is more appropriate in many situations. It is possible for any percentage of the top (or low) priority fraction of the landscape to get representation level of each species. In this figure, the red area represents the top 17% priority cells. The minimum (1), maximum (2), and the overall mean (3) species representation are shown in the figure. Similarly, the green area represents the urban and agricultural areas in Egypt (Fig. S2.2) which were forced in our analyses to have the lowest priority ranking. The mean species representation in this area is also shown (4). For an example performance curve using the full species list, see Fig. 5.

Figure S2: Urban and agricultural areas masked during Zonation prioritisation. These areas have high human population density, more polluted, and are of exceptionally high economic value (replacement cost), and hence it is challenging to apply strict conservation actions or construct new PAs in these areas.

Figure S3: Response curves used in the boundary-quality penalty connectivity analyses. These curves describe a range of species sensitivity to habitat loss in neighbour cells, ranging from no response (1) to strong response (4). The x-axis shows the percentage of neighbour cells (specified by three values of radii) remaining: 100 represents no habitat loss; while 0 represents total habitat loss of all neighbour cells. The y-axis shows the percentage reduction in local cell value in response to habitat loss.

Figure S4: The spatial distribution of the top 17% priority cells at different options of connectivity (additive-benefit function; using bias-free predictions of all the study species for Maxent). The top-left map shows the pattern of important sites without connectivity integration. The second to the fourth column is for equivalent maps with steeper response curves (low, medium, and high connectivity; curves 2-4 in Fig. S2.3, respectively); while rows are for different number of effective neighbour cells used (1 to 3). Equivalent maps using core-area zonation are shown in Fig. 2.

Figure S5: Permutation importance of factors affecting the Zonation output’s uncertainty, calculated from a randomForest model (varimp function using party package). Here, the dependent variable is the mean species representation at the top 10% (above) and 25% (below) priority cells.

Figure S6: The relative importance of various factors affecting the uncertainty of Zonation’s prioritisation (and their first order interactions) using the mean sum of squares from a generalised linear model. Only variables having more than 1% importance are shown.

Figure S7: The spatial distribution of the top priority cells of equal area to current PAs (the darker, the higher the priority) for different surrogates using additive-benefit function, overlaid with current protected areas in Egypt (blue borders). These maps were prepared using bias-free predictions from Maxent. Equivalent maps using core-area zonation are shown in Fig. 3.

Figure S8: The fraction of cells protected at each 1% intervals of Zonation rank for different surrogates using additive-benefit function (using bias-free predictions from Maxent). The number at each panel represents Kendall’s correlation coefficient. Equivalent results for core-area zonation are shown in Fig. 4; while results using elastic net are shown in Fig. S2.9.

Figure S9: The fraction of cells protected at each 1% intervals of Zonation rank for different surrogates using core-area zonation (top row) or additive-benefit function (bottom row), using bias-free predictions for elastic net. The numbers at each panel represent Kendall’s correlation coefficient.

Figure S10: Performance curves for Zonation analyses (Maxent – additive-benefit function – all species together). Left panel represents analysis without Egyptian PAs integration. Solid curves represent the average performance curve for all species or per species-group; while dashed lines represent the overall minimum and maximum performance curves per species group. The vertical grey line is for urban and agricultural areas (Fig. S2.2) forced to have low priority value; while the dashed vertical green line represents top priority sites existent in an area equals to the area covered by PAs. The right panel represents equivalent analysis with Egyptian PAs forced to have highest priority scores. The vertical green line represents the area covered by PAs; while the vertical orange line represents the proposed areas for PAs expansion to 17% of Egypt. For equivalent curves using core-area zonation, see Fig. 5. Results for elastic net are shown in Fig. S2.11.

Figure S11: Performance curves for elastic-net models using core-area zonation (A) and additive benefit function (B). For more details, see Fig. 5 and main text.

Figure S12: Potential areas for Egyptian PAs expansion (to 17% of Egypt) using core-area zonation (above) and additive-benefit function (below) for each surrogate. All maps use bias-free predictions from Maxent. Egyptian PAs (shown in blue) were always forced to have the highest ranking. Grey areas outside PAs represent potential areas for Egyptian PAs expansion. The ranking of these maps are added together to summarise the overall pattern of important sites outside PAs (see Fig. 6, and similarly Fig. S2.13 for elastic net).

Figure S13: The overall pattern of top priority sites using core-area zonation (left) or additive-benefit function (right), both using bias-free predictions from elastic net. Each map shows the summed rankings of the top 17% sites from the four surrogates used. Top priority sites within current protected areas (blue borders) are not shown to highlight the overall pattern of potential areas for PAs expansion (the darker the colour, the higher the cumulative rank of the site using the four surrogates). For equivalent maps using Maxent, see Fig. 6.

Figure S14: Boxplots for the predictive consistency of cross-validated models with (left) and without (right) correction for sampling bias. Predictions from Maxent are shown in white boxes, while elastic-net is shown in grey boxes. The median predictive consistency is about 0.8, with relatively higher median consistency for elastic net (environment-only models). Predictive consistency was one method of weighting species in Zonation, see the main text for details.

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

Figure S15: Current Egyptian protected areas.  

Figure S16: Field evaluation sites visited during protected areas identification mission between December 1996 and April 1998. (Sherif Baha El Din, pers. comm.). 

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Egypt Maxent Elastic Net Point-Process Models Species Distribution Models Conservation Mammals Bats Sampling Bias Reptiles Butterflies Spatial Conservation Prioritization Zonation Open-Access Rstats R-Code