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Conserving Egypt's reptiles under climate change
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... We [437]. PAs are imperfect in their coverage of biodiversity [438], but because of their extent Egyptian PAs at least in theory protect biodiversity much better than those of many other countries [439]. ...
... indicating the variability in vegetation per pixel). The maps were then rescaled to 2.5 arc minutes [437]. 80 The predictor layer of 'habitat' was created by BioMAP [429], dividing Egypt into eleven classes (sea, littoral coast, cultivation, sand dune, wadi, metamorphic rock, igneous rock, gravels, serir sand sheets, sabkhas and sedimentary rocks). ...
... 80 The predictor layer of 'habitat' was created by BioMAP [429], dividing Egypt into eleven classes (sea, littoral coast, cultivation, sand dune, wadi, metamorphic rock, igneous rock, gravels, serir sand sheets, sabkhas and sedimentary rocks). Altitude data were downloaded and rescaled to a pixel size of 2.5 arc-minutes [437]. [406,442,443], including likely shifts in distribution, habitat change, gains and losses, turnover and extinction [444,445]. ...
This document explores the intricate relationships between climate change,
medicinal plants, and agricultural impacts. It delves into the medicinal plant
resources, emphasizing their bioactive molecules as crucial components of
ecosystem services and their vulnerability to global environmental changes.
Emerging scientific methodologies highlight the necessity of a transdisciplinary
framework to integrate medicinal biodiversity into global health solutions and
conservation strategies.
The impacts of climate change on agricultural productivity, crop adaptation,
and pest dynamics are detailed. The potential for climate-smart agriculture and
genomic advances to mitigate these challenges are discussed, emphasizing the
urgent need for sustainable practices and resilient crop varieties to ensure food
security amidst rapid climatic shifts.
Key sections also underline the socioeconomic disparity in accessing and
preserving medicinal plants. This disparity threatens biodiversity, necessitating
integrated conservation strategies and sustainable cultivation systems to protect
and leverage these natural resources.
Through comprehensive modeling, the document predicts the distribution of
key species under future climate scenarios and proposes adaptive measures to
safeguard these invaluable resources. It underscores the interconnectedness
between plant secondary metabolites, ecosystem functions, and human health,
proposing actionable steps to mitigate risks posed by climate change.
This document explores the intricate relationships between climate change,
medicinal plants, and agricultural impacts. It delves into the medicinal plant
resources, emphasizing their bioactive molecules as crucial components of
ecosystem services and their vulnerability to global environmental changes.
Emerging scientific methodologies highlight the necessity of a transdisciplinary
framework to integrate medicinal biodiversity into global health solutions and
conservation strategies.
The impacts of climate change on agricultural productivity, crop adaptation,
and pest dynamics are detailed. The potential for climate-smart agriculture and
genomic advances to mitigate these challenges are discussed, emphasizing the
urgent need for sustainable practices and resilient crop varieties to ensure food
security amidst rapid climatic shifts.
Key sections also underline the socioeconomic disparity in accessing and
preserving medicinal plants. This disparity threatens biodiversity, necessitating
integrated conservation strategies and sustainable cultivation systems to protect
and leverage these natural resources.
Through comprehensive modeling, the document predicts the distribution of
key species under future climate scenarios and proposes adaptive measures to
safeguard these invaluable resources. It underscores the interconnectedness
between plant secondary metabolites, ecosystem functions, and human health,
proposing actionable steps to mitigate risks posed by climate change.
... elevation. Relevant tiles were downloaded, united together, and clipped to the borders of Egypt at resolution of 2.5 arc-minutes (for more details, see El-Gabbas et al., 2016). There is collinearity among the environmental variables, which can be a problem in any modelling method, including species distribution modelling (Guisan et al., 2002). ...
... The mean species richness inside and outside each Protected Area (PA) were calculated just for the ensemble technique and for MaxEnt, to compare spatial conservation priority based on these two methods. Eygpt has 30 PAs established since 1983, covering about 15% of the land area (El-Gabbas et al., 2016). We created a 50-km buffer around each PA, and calculated the mean predicted species richness across all pixels inside each PA ('inside') and in the buffer ('outside'). ...
... Correlative SDMs are sensitive to the chosen modelling techniques (Araújo and New, 2007), hence variation among models is expected due to their different assumptions and algorithms (El-Gabbas et al., 2016;Marmion et al., 2009). Some SDM techniques can be described as "datahungry" in order to capture complex interactions and responses (Wisz et al., 2008), yet they can perform very well if properly handled and analysed (Guillera-Arroita et al., 2014), an advantage over MaxEnt since they use presence-absence data. ...
Understanding the relationship between the geographical distribution of taxa and their environmental conditions is a key concept in ecology and conservation. The use of ensemble modelling methods for species distribution modelling (SDM) have been promoted over single algorithms such as Maximum Entropy (MaxEnt). Nevertheless, we suggest that in cases where data, technical support or computational power are limited, for example in developing countries, single algorithm methods produce robust and accurate distribution maps. We fit SDMs for 114 Egyptian medicinal plant species (nearly all native) with a total of 14,396 occurrence points. The predictive performances of eight single-algorithm methods (maxent, random forest (rf), support-vector machine (svm), maxlike, boosted regression trees (brt), classification and regression trees (cart), flexible dis-criminant analysis (fda) and generalised linear models (glm)) were compared to an ensemble modelling approach combining all eight algorithms. Predictions were based originally on the current climate, and then projected into the future time slice of 2050 based on four alternate climate change scenarios (A2a and B2a for CMIP3 and RCP 2.6 and RCP 8.5 for CMIP5). Ensemble modelling, MaxEnt and rf achieved the highest predictive performances based on AUC and TSS, while svm and cart had the poorest performance. There is high similarity in habitat suitability between MaxEnt and ensemble predictive maps for both current and future emission scenarios, but lower similarity between rf and ensemble, or rf and MaxEnt. We conclude that single-algorithm modelling methods, particularly MaxEnt, are capable of producing distribution maps of comparable accuracy to ensemble methods. Furthermore, the ease of use, reduced computational time and simplicity of methods like MaxEnt provides support for their use in scenarios when the choice of modelling methods, knowledge or computational power is limited but the need for robust and accurate conservation predictions is urgent.
... 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 Additional file 1: 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. ...
... 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]; Additional file 1: Table S1). We compared equal-weighted vs Red List-weighted solutions. ...
... 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]. ...
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.
... The spatial distribution of reptiles is affected by various climate and topographical factors. Different studies have reported a variety of factors contributing to the explanation of reptile distributions, including precipitation (Fattahi et al., 2014;Sanchooli, 2017), temperature (Sillero and Carretero, 2013;Javed et al., 2017), altitude (El-Gabbas et al., 2016) and vegetation cover (Fattahi et al., 2014). However, among all these factors, temperature appears to dominate (directly and/or indirectly), which is not surprising since it is well known in affecting daily activities and reptile biology (Huey, 1982;Wilms et al., 2011). ...
... The predicted pattern of high reptile diversity and habitat suitability around the coast of Saudi Arabia is in-line with the findings of El- Gabbas et al. (2016), who modelled reptile species richness and habitat suitability in Egypt. The authors found that some coastal areas of Egypt had high predicted species richness, especially around the Sinai Gulf and Mediterranean Sea. ...
... The authors found that some coastal areas of Egypt had high predicted species richness, especially around the Sinai Gulf and Mediterranean Sea. Our model predicted that some habitats in Saudi Arabia would be suitable for at least 50 out of 62 species, which is similar to the findings of El-Gabbas et al. (2016), who found that some locations in Egypt would be suitable for at least 52 out of 75 modelled species. ...
Species distribution modelling is a powerful tool that can gives us ecological insights about species distributions, and potential effects of environmental factors, in poorly known habitats. For the first time the distribution of terrestrial reptiles in Saudi Arabia was modelled, and environmental factors that affect their current distribution and richness investigated. Reptiles are a major vertebrate group in Saudi Arabia and protecting them should be a priority for conservation in such an arid environment. Temperature was the most important of eleven predictors. Maximum species richness of reptiles was predicted in the central plateau, north-western borders, and in coastal areas of Saudi Arabia. Overall, the predicted and the observed patterns of species richness followed a similar pattern. Our analysis revealed that large scattered parts of Saudi Arabia are considered under-sampled in terms of sampling efforts of terrestrial reptile species. Our results represent the most comprehensive description of terrestrial reptile diversity distributions and habitat suitability in Saudi Arabia to date.
... Warmer temperatures and seasonal distributions of precipitation had a large impact on the survival of populations, species and communities (El- Gabbas et al., 2016). Through the past 40 years, the main reason for distributional shifts and extinctions is climate change, with a particularly strong impact on butterflies, birds and species at high latitudes (Hannah, 2014;El-Gabbas et al., 2016). ...
... Warmer temperatures and seasonal distributions of precipitation had a large impact on the survival of populations, species and communities (El- Gabbas et al., 2016). Through the past 40 years, the main reason for distributional shifts and extinctions is climate change, with a particularly strong impact on butterflies, birds and species at high latitudes (Hannah, 2014;El-Gabbas et al., 2016). These changes are expected to force some ecosystems and their species to move poleward or up-slope, downslope, and cause heterogeneous range, or contractions in their ranges (Inman et al., 2016). ...
... progress over the next 50 years and beyond (El-Gabbas et al., 2016). It is still not very clear how warming will affect the distribution and survival of many species of plants and animals and whether the effects will be positive or negative ( Vieites et al., 2007). ...
Human population is interrelated with the demand of fuel, water and food. This is consequently leading to increasing rates of energy use and therefore greenhouse gas (GHG) emissions. Globally, the atmospheric concentrations of GHG have increased by approximately 35% for carbon dioxide, 148% for methane, and 14% for nitrous oxide. Desert ecosystems, in particular, are highly variable and unpredictable, where organisms and humans have utilized arid environments regardless of their naturally uncertain availability of resources. The fast spread of desertification has led to environmental degradation, unstable local political situations and economic losses. The extreme weather events in the past two decades caused many loses in terms of ecosystem alteration, economic impacts as well as social influences. Dryland communities adapt to dynamic climatic and environmental conditions due to rainfall variability. Unfortunately, climate change impact is not fully understood. The effects of climate change on species diversity is generally slow, but these effects are expected to show rapid progress over the next 50 years and beyond. Remote sensing and GIS based models allow simulating the change in particular landscape elements over time and space, and investigating different types of future scenarios. The information represented in this paper aims to give a review and discussion of the impact of climate change on arid and semi-arid regions to the researchers, ecologists and decision makers. There is a lack of resources about the impact of climate change on the Arabian Gulf region in particular. Therefore, we hope that this review will simulate researchers in the region and worldwide to conduct their research and focus their studies on this region.
... Recent work shows that climate change is one of the main factors affecting the distribution of species and ecosystems (Alkemade et al. 2011); species really are shifting northwards (Parmesan and Yohe 2003;Root et al. 2003), and projections under future climate change predict much larger shifts (Thuiller et al. 2005;Ara ujo et al. 2006). These impacts are of concern to conservation biologists (Brooks et al. 2006), in particular because one of the predicted impacts is to change the efficiency of Protected Areas (PAs) in conserving species in the future (Araujo et al. 2011;Leach et al. 2013;El-Gabbas et al. 2016;Fois et al. 2018a). The increasing impact of climate change on plants is predicted to affect northern and Mediterranean countries in particular (Bakkenes et al. 2006). ...
... Conservation planning strategies are essential if we are to minimize biodiversity loss, because the threats to biodiversity are unevenly distributed (Brooks et al. 2006). Many studies have used spatial prioritization for conservation planning based only on biological data (Naidoo et al. 2006;Leach et al. 2013;El-Gabbas et al. 2016), which can undermine the conservation process if they do not take into consideration socioeconomic impacts (Knight et al. 2008;Faleiro et al. 2013). Recently, several studies have used socioeconomic data in conservation planning (Faleiro et al. 2013;Di Minin et al. 2017), especially in the Mediterranean basin (Petrosillo et al. 2010;Schmitz et al. 2012;Schmitz et al. 2017;Arnaiz-Schmitz et al. 2018) to see how such information changes spatial prioritization for conservation. ...
... A further environmental descriptor was a categorical habitat layer, derived from the Biomap project (for more detail, see Newbold et al. 2009). Altitude data were downloaded from http://www.cgiar-csi.org/data/elevation and the resolution rescaled from 90 m to be 2.5 arc-minutes (see El-Gabbas et al. 2016). Eleven of the 23 environmental variables remained for use after 12 were removed based on collinearity analysis using the Variance Inflation Factor (Supplementary Table S1), implemented in R v2.15 (the car package: R Development Core Team 2012). ...
The impact of climate change on conservation planning is affected by the availability of data (espe-cially in data-sparse countries) and socioeconomic impacts. We build models using MaxEnt for Egyptian medicinal plants as a model system, projecting them to different future times under two IPCC 4th assessment emission scenarios (A2a and B2a) assuming unlimited and no dispersal. We compare the effect of two indices of socioeconomic activity [Human Influence Index (HII) and human population density/km 2 ] as cost layers in spatial prioritization for conservation using zonation. We assess the efficacy of Egypt's network of Protected Areas (PAs) by comparing the predicted conservation value inside and outside each PA under the various scenarios. The results show that there are many locations in Egypt (the main cities, agricultural land, coastal areas) that are highly ranked for conservation before human socioeconomic impacts are included. The HII had a stronger impact than using human population density. The PA value excess (inside-outside) varied significantly with the type of cost and dispersal, but not with climate-change scenario or Zonation settings. We conclude that human socioeconomic impacts add new scope and insights for future conservation; and conservation planning without consideration of such impacts cannot be complete. ARTICLE HISTORY
... Then we ask whether Egypt's PAs provide and will provide suitable plant habitat compared with outside the PAs. There are about 30 PAs in Egypt covering approximately 15% of Egypt's land area [36]. PAs are imperfect in their coverage of biodiversity [37], but because of their extent Egyptian PAs at least in theory protect biodiversity much better than those of many other countries [28]. ...
... 252 maps represent data from 2004-2010, which were then clipped to Egypt's boundaries and used to create two predictors-maximum (Max_NDVI, indicating the maximum amount of vegetation there is per pixel) and the difference between the Minimum and Maximum (NDVI_differences, indicating the variability in vegetation per pixel). The maps were then rescaled to 2.5 arc minutes [36]. The predictor layer of 'habitat' Plant distribution and climate change was created by BioMAP [28], dividing Egypt into eleven classes (sea, littoral coast, cultivation, sand dune, wadi, metamorphic rock, igneous rock, gravels, serir sand sheets, sabkhas and sedimentary rocks). ...
... The predictor layer of 'habitat' Plant distribution and climate change was created by BioMAP [28], dividing Egypt into eleven classes (sea, littoral coast, cultivation, sand dune, wadi, metamorphic rock, igneous rock, gravels, serir sand sheets, sabkhas and sedimentary rocks). Altitude data were downloaded and rescaled to a pixel size of 2.5 arc-minutes [36]. Eleven of these 23 predictors ( Table 1) were eventually used after removing collinearity by applying the Variance Inflation Factor using R v2.15 (the 'car' package: R Development Core Team 2012). ...
Climate change is one of the most difficult of challenges to conserving biodiversity, especially for countries with few data on the distributions of their taxa. Species distribution modelling is a modern approach to the assessment of the potential effects of climate change on biodiversity, with the great advantage of being robust to small amounts of data. Taking advantage of a recently validated dataset, we use the medicinal plants of Egypt to identify hotspots of diversity now and in the future by predicting the effect of climate change on the pattern of species richness using species distribution modelling. Then we assess how Egypt's current Protected Area network is likely to perform in protecting plants under climate change. The patterns of species richness show that in most cases the A2a ‘business as usual’ scenario was more harmful than the B2a ‘moderate mitigation’ scenario. Predicted species richness inside Protected Areas was higher than outside under all scenarios, indicating that Egypt’s PAs are well placed to help conserve medicinal plants.
... Protected Areas currently cover about 12% of the terrestrial surface of the earth (Seiferling et al., 2012), while those that have been declared in Egypt cover 15% of the total land area (El-Gabbas et al., 2016). The 30 Egyptian Protected Areas were all established since 1983, based on the recommendations of experts familiar with Egyptian biodiversity (Newbold et al., 2009). ...
... A further environment layer was a habitat layer, derived from the Biomap project, which divided Egypt's terrain into eleven classes ("sea, littoral coastal land, cultivated land, sand dune, wadi, metamorphic rock, igneous rock, gravels, serir sand sheets, sabkhas and sedimentary rocks") (for more detail, see Newbold et al., 2009). Altitude data were downloaded from http://www.cgiar-csi.org/data/elevation and the resolution rescaled from 90 m to be 2.5 arc-minutes (see (El-Gabbas et al., 2016). Eleven of the 23 environmental variables (see Table 1) remained for use after 12 were removed based on collinearity analysis using the Variance Inflation Factor, implemented in R v2.15 (the 'car' package: R Development Core Team, 2012). ...
... The binary map converts each pixel value of the MaxEnt output (a continuous value between 0 and 1) into binary data (predicted suitable/unsuitable) by choosing a threshold rule (see Liu et al., 2005). We chose the "10% training presence" as our threshold rule (El-Gabbas et al., 2016), which produced a binary map for each of the 10 replicates for each species. Subsequently we produced a single consensus binary map for each species by allocating 'suitable' to a pixel that had 'suitable' values in more than 50% of the model runs (i.e. ...
... Various diversity indices are employed to assess the effects of biodiversity on ecosystem functions, encompassing taxonomic diversity, such as the Shannon index (Morris et al., 2014;Rasheed et al., 2022), and functional diversity, including functional traits (Diaz and Cabido, 2001;Macheroum et al., 2021). These indices are applicable across a wide range of taxonomic groups, including plants (Maamar et al., 2018), spiders (Conti et al., 2018), beetles (Amri et al., 2019), nematodes (Zhang et al., 2020), amphibians and reptiles (El-Gabbas et al., 2016;Mouane et al., 2024), birds (Guezoul et al., 2013), bats (Hall et al., 2016), and rodents (Inman et al., 2016), among others. ...
... Global climate and human activities are two interrelated factors that influence the future of deserts, as the rapid pace of climate change surpasses the adaptive capacities of organisms (Stringer et al., 2009). According to El-Gabbas et al. (2016), climate change models predict that between 15% and 37% of existing species may vanish by 2050. Furthermore, other species are anticipated to undergo range shifts and contractions toward latitudinal and longitudinal zones with more favorable environmental conditions (Arar et al., 2019). ...
Drylands are often erroneously perceived as inhospitable and isolated environments characterized by low biodiversity and misconceptions regarding drought and water scarcity. However, it is crucial to recognize that many individuals within these regions employ livelihood strategies that contribute to the preservation of biodiversity in innovative ways. This lack of awareness has led to insufficient attention from the international community, manifested in the form of limited coverage in academic literature and inadequate funding for development initiatives. To offer a comprehensive understanding of the arid biome and its defining features, this study undertook a thorough examination of electronic databases and scholarly publications, focusing on desert key-aspects such as geographical distribution, characteristics, threats, and conservation strategies and indicators of biodiversity. This contribution aims to foster greater awareness among all stakeholders and catalyze concerted efforts to promote biodiversity conservation and sustainable development of arid regions in the face of global warming.
... For these and other reasons, studying species distribution modelling has gained considerable attention (Guisan and Thuiller, 2005) as it deals with the issue of incomplete data Kaky, 2020). The technique has proven its ability to identify the best candidates for hotspots of habitat suitability under current and future scenarios, which can then be used in species conservation (El-Gabbas et al., 2016;Kaky and Gilbert, 2019;Alatawi et al., 2020), especially for less-studied species in relatively understudied habitats (for Egypt see El-Gabbas et al., 2016;Kaky and Gilbert, 2016;Kaky et al., 2020; for Saudi Arabia see Alatawi et al., 2020; for Iraq see Kaky, 2020;for Iran). ...
... For these and other reasons, studying species distribution modelling has gained considerable attention (Guisan and Thuiller, 2005) as it deals with the issue of incomplete data Kaky, 2020). The technique has proven its ability to identify the best candidates for hotspots of habitat suitability under current and future scenarios, which can then be used in species conservation (El-Gabbas et al., 2016;Kaky and Gilbert, 2019;Alatawi et al., 2020), especially for less-studied species in relatively understudied habitats (for Egypt see El-Gabbas et al., 2016;Kaky and Gilbert, 2016;Kaky et al., 2020; for Saudi Arabia see Alatawi et al., 2020; for Iraq see Kaky, 2020;for Iran). ...
The Wild goat (Capra aegagrus) has had less attention and study than many other mammals, especially in Iraq. We collected comprehensive data about this species in Iraq, a total of 36 records, in order to build species distribution models using Maximum Entropy and seven environmental variables. The results confirm that suitable habitat is limited to the northeastern part of Iraq, especially the Zagros mountains of Kurdistan region/Iraq. Elevation most influenced the predicted distribution. Habitat suitability under different future climate scenarios (RCP2.6 and RCP8.5 for 2050 and 2070) is stable but expands compared with the current period. Areas of marginally and highly suitable decrease, while suitable habitat increases compared to the present. Habitat suitability inside existing Protected Areas was significantly higher than outside. Applying IUCN Red List criteria at the national scale, the species was classified as Endangered. The main short-term threat is, against which urgent action is needed to avoid future declines. We conclude that climate change is not likely to be a long-term threat to the Wild goat in Iraq, but urgent action to stop poaching is needed to sustain many Iraqi mammals, especially the Wild goat. Our applied approach can help understand and conserve other critical species in Iraq.
... We chose to use the IPCC 4th assessment (IPCC, 2007: obtained from http://www.ccafsclimate.org/) and emission scenarios A2 and B2, rather than the latest 5th assessment and its very different scenarios, for continuity with previous work (e.g. El-Gabbas et al., 2016) and because the differences in SDMs are slight (Wright et al., 2016). The A2a and B2a scenarios involve different assumptions about the levels of CO 2 emissions, with A2 denoting large changes and B2 relatively small changes (Phillips et al., 2017;Hannah, 2011). ...
... evolution, environment, physiology, demography, dispersal, and species interactions: Urban et al., 2016) in a biologically realistic way, but such realism is a long way off. At about 15% of the total land, PAs in Egypt potentially represent a good level of conservation (El-Gabbas et al., 2016) when compared with the global average of about 12% (Chape et al., 2005). Egyptian PAs appear to have higher species richness within them than areas outside (Newbold et al., 2009;Leach et al., 2013), even in future projections under climate change (Kaky and Gilbert, 2017). ...
The IUCN Red List of Threatened Species is one of the most important of all conservation indicators, but most developing countries do not have enough information with which to make assessments. The use of species distribution models (SDMs) to predict habitat suitability, both currently and in the future under the effects of climate change, offers a potential solution for estimating extinction risk. With a set of validated observations, we used SDMs to make preliminary evaluations of the risk of extinction for 114 Egyptian medicinal plants based on IUCN Red-List Criteria and Categories. Using MaxEnt and eleven environmental variables, distributions were projected for 2020, 2050, and 2080 under two emission scenarios (A2a and B2a) assuming unlimited and no dispersal. The IUCN assessments used the predicted distributions as well as the actual records to measure both extent of occurrence (EOO) and area of occupancy (AOO). There was a positive correlation between EOO estimates based on actual records and those based on SDMs, demonstrating the ability of SDMs to compensate for a lack of data. Most species could be classified as Least Concern (LC) at the current time, whilst in the future under climate change, many species face some risk of extinction, depending on assumptions. We conclude that it is possible to make regional risk assessments even in data-sparse countries, in order to plan conservation management in the future. Using species distribution modelling together with IUCN Red-List assessment is a good method for countries where data are sparse in order to conserve and protect threatened species.
... ArcGIS was used to create two types of species suitability distribution maps: (1) An overlapping probability map obtained by superimposing the MaxEnt prediction results of the four plants to discuss the spatial pattern changes of the distribution of several plants under current and future climate scenarios (El-Gabbas et al., 2016). (2) A "binary map" created by reclassifying the prediction results of each plant, with a probability value of 30% as the threshold, whereby areas above 30% were considered suitable habitats (denoted by "1"), and areas below 30% were considered non-suitable habitats (denoted by "0"). ...
Climate change has accelerated the habitat loss and fragmentation of wildlife. Dye plants of “Fengxiang dyeing” are important indigenous natural resources for traditional printing and dyeing craft in southwest China, is of practical and cultural importance for dozens of ethnic minorities. However, lack of the spatial distribution information of these plants has hampered holistic and efficient conservation management measures. We analyzed the potentially suitable areas of four dye plants (Liquidambar formosana, Strobilanthes cusia, Persicaria tinctoria and Indigofera tinctoria) necessary for “Fengxiang dyeing” based on their geographical distribution sites under different climatic situations using the maximum entropy (MaxEnt) model. The results showed that temperature, precipitation and elevation were the most important factors affecting the suitable geographical areas of the four dye plants. Under the current climate conditions, the overlapping suitable habitat areas of the four plants were mainly in the four southern provinces of China, including Guizhou, Guangxi, Guangdong and Hainan. L. formosana was used as the base plant for combination with the other three plants under the two future climate scenarios (SSP126 and SSP585), and the overlapping suitable habitat areas of the obtained seven combination patterns were considered suitable for potential craft development. Five patterns showed an increase, while two patterns showed a decreasing trend with the increasing carbon emission. The prediction results showed that the overlapping suitable habitat center of the four plants will gradually move to the northeast, indicating that the overlapping suitable habitat area and craft distribution area will be changed. These results provide the basis for understanding the spatial distribution pattern changes of dye plants caused by climate change and establishing measures for protecting and developing printing and dyeing craft.
... All 19 bioclimatic variables were downloaded from the WorldClim database (Hijmans et al., 2005) and cropped to the geographical extent of South Africa using ArcGIS (ESRI, 2012). Correlated variables (with a Pearson's correlation coefficient >0.7) for each species were assessed using the r package CorrPlot (Wei & Simko, 2021) in RStudio 2021.09.1 (RStudio Team, 2020) and were removed, preferentially retaining variables known to influence the distribution of each taxon (Bucklin et al., 2015;Cunningham et al., 2016;El-Gabbas et al., 2016;Hijmans et al., 2005;Lawson, 2010;Leaché et al., 2019;Parvizi et al., 2018Parvizi et al., , 2019 and the remaining limiting climatic variables were used to generate the models (Appendix S3: Table S3.8). A custom R script was used to specify a fixed distance buffer of 50 km based on the occurrence records for model building in order to correct the models for overprediction, as well as to produce minimum convex polygons of the distribution for each taxon. ...
We compare the phylogeographic structure of 13 codistributed ectotherms including four reptiles (a snake, a legless skink and two tortoise species) and nine invertebrates (six freshwater crabs and three velvet worm species) to test the presence of congruent evolutionary histories. Phylogenies were estimated and dated using maximum likelihood and Bayesian methods with combined mitochondrial and nuclear DNA sequence datasets. All taxa demonstrated a marked east/west phylogeographic division, separated by the Cape Fold Mountain range. Phylogeographic concordance factors were calculated to assess the degree of evolutionary congruence among the study species and generally supported a shared pattern of diversification along the east/west longitudinal axis. Testing simultaneous divergence between the eastern and western phylogeographic regions indicated pseudocongruent evolutionary histories among the study taxa, with at least three separate divergence events throughout the Mio/Plio/Pleistocene epochs. Climatic refugia were identified for each species using climatic niche modelling, demonstrating taxon‐specific responses to climatic fluctuations. Climate and the Cape Fold Mountain barrier explained the highest proportion of genetic diversity in all taxa, while climate was the most significant individual abiotic variable. This study highlights the complex interactions between the evolutionary history of fauna, the Cape Fold Mountains and past climatic oscillations during the Mio/Plio/Pleistocene. The congruent east/west phylogeographic division observed in all taxa lends support to the conclusion that the longitudinal climatic gradient within the Greater Cape Floristic Region, mediated in part by the barrier to dispersal posed by the Cape Fold Mountains, plays a major role in lineage diversification and population differentiation.
... Climate change will potentially expand the environmentally suitable areas of E. leucogaster under moderate (SSP2) and extreme (SSP5) scenarios and during both the mid (2041-2060) and late (2071-2100) twenty-first century. El-Gabbas et al. (2016) projected similar results for the congener species E. coloratus but not for E. pyramidum. These results corroborate a recent review by Needleman et al. (2018) who pointed out that the response of venomous snakes to climate change varied between taxa and among regions. ...
Knowledge of biodiversity distribution and how climate change may affect species across the Sahara-Sahel is scarce despite it harboring both high biodiversity and a high rate of endemism. As ectotherms, snakes are particularly vulnerable to climate change and susceptible to range shifts and demographic changes driven by climate change. Ecological niche models are a common method for predicting the probability of the occurrence of species and future range shifts induced by climate change. This study examines the probable gaps in the distribution of the white-bellied saw-scaled viper, Echis leucogaster, and the potential influence of climate change on its future geographic range in the western Sahara-Sahel. The currently predicted environmentally suitable areas fitted well with the known geographical range of the species showed relative congruence with the Sahara-Sahel ecoregion delineations and identified areas without known occurrences. In the future, the environmental conditions for the occurrence of E. leucogaster are predicted to increase, as the environmentally suitable areas will potentially experience an increase in their proportion. Future projections also showed that the potentially suitable areas might undergo moderate southward shifts during the late twenty-first century. The results of the present study significantly expand our knowledge on the potential distribution of E. leucogaster and provide valuable insights to guide future sampling efforts and conservation planning for the species.
... Corine land cover, NDVI, Forest Density κ.α.) και συνεπώς αποφασίστηκε για τους σκοπούς της παρούσας διατριβής να γίνει χρήση μόνο των δύο τα οποία δεν αναμένεται να αλλάξουν στο μέλλον. Αυτή η επιλογή δεδομένων αποτελεί τεχνική η οποία έχει ευρεία χρήση στις έρευνες οι οποίες επιχειρούν την σύγκριση παροντικών με μελλοντικών κατανομών (Salas, et al., 2017;El-Gabbas, et al., 2016). ...
Using Species Distribution Modeling (SDMs) and Least Cost Path Analysis in order to examine the current and future distribution of the Cyprus grass snake and the effect of climate change on it.
... In contrast, the mean of the elevation was useful for a few studied species. Previous studies also illustrated that NDVI (e.g., Taheri, 2010;Fattahi et al., 2014), temperature seasonality (e.g., Karamiani et al., 2019), precipitation of wettest quarter (e.g., Gadsden et al., 2012;Ananjeva et al., 2014), and mean of the elevation (e.g, El-Gabbas et al., 2016) is one of the most important variables to show the habitat suitability of reptiles. ...
Trade of non-native reptiles is an important and increasing driver of biodiversity loss and often compromises the standards required for protection. However, the growing interest in non-native reptiles as pets has posed serious concerns to wildlife managers and conservationists. Instituting effective policies regarding non-native reptiles requires a thorough understanding of the potential range of species in new environments. In this study, we used an ensemble of ten species distribution models to predict the potential distribution for 23 of the most commonly traded species of reptiles across the Middle East. We used ten modeling techniques implemented in the Biomod2 package and ensemble forecasts. Final models used thirty environmental variables, including climatic, topographic, and land cover/land use variables. Our results indicate that all Middle Eastern countries included suitable habitats for at least six species, except Qatar, Kuwait and Bahrain, for which the models did not predict any suitable habitats. Our study showed that Lebanon, Palestine, Turkey, and Israel face the highest risk of biological invasion based on the area of suitable habitats for all studied species. Also, the results showed that turtles posed the highest risk of spreading in in the Middle East. Information on which species pose a greater danger as invaders and the possible impacts of their introduction will be a valuable contribution to the development of conservation plans and policies.
... As a result, the body temperature of reptiles is strongly influenced by the thermal quality of microhabitats, exploiting the favourable environmental temperatures, and avoiding exposure to extreme thermal conditions (Hertz et al. 1993;Besson and Cree 2010). For these reasons, reptiles are considered to be especially vulnerable to changes in environmental temperatures produced by climate change (e.g., Deutsch et al. 2008;El-Gabbas et al. 2016;Winter et al. 2016). ...
The body temperature of lizards is strongly influenced by the thermal quality of microhabitats, exploiting the favourable environmental temperatures, and avoiding exposure to extreme thermal conditions. For these reasons, reptile populations are considered to be especially vulnerable to changes in environmental temperatures produced by climate change. Here, we study the thermal physiology of the critically endangered Añelo Sand Dunes Lizard (Liolaemus cuyumhue Avila, Morando, Perez and Sites, 2009). We hypothesise that (i) there is a thermal coadaptation between optimal temperature for locomotor performance of L. cuyumhue and its thermal preference; (ii) L. cuyumhue lives in an environment with low thermal quality; and (iii) a rise in environmental temperatures due to global warming will impose a decrement in locomotor speed represented by lower warming tolerance and narrower thermal safety margins, increasing their already high vulnerability. We recorded field body temperatures (Tb), preferred body temperatures (Tpref), the operative temperature (Te), and the thermal sensitivity of locomotion at different body temperatures. Our results indicate that this lizard is not currently under environmental stress or exceeding its thermal limits, but that it is thermoregulating below Tpref to avoid overheating, and that an increase in environmental temperature higher than 3.5 °C will strongly affect the use of microhabitats with direct sun exposure.
... For example, Sow et al. (2014) inferred that the richness of the reptiles would be modified by the altering climatic status, while others found that the sensitivity and exposure (Barrows 2011;Nori et al. 2016) or the vital rates or population for certain reptiles (Jones et al. 2016) would be altered from climate change. Moreover, the danger of losing areal ranges for some reptiles (El-Gabbas et al. 2016;Nasrabadi et al. 2018;Berriozabal-Islas et al. 2018;Zacarias and Loyola 2019) or the vulnerability for certain reptiles (Gonçalves et al. 2016) would increase under nonrandom scenarios of shifting climate conditions. However, compared with the studies for nonrandom scenarios of changing climate factors, the distribution changes of reptiles under stochastic scenarios of moving climatic conditions have not been extensively investigated in different regions. ...
Revealing the hazard features of forfeiting areal ranges for nonidentical scenarios of shifting climatic conditions is pivotal for the conformation of reptiles to climatic warming. Taking 115 reptiles in China as an example, the indefiniteness and danger of shrinking geographical range for the reptiles under stochastic and nonrandom scenarios of moving climatic situations were inspected via exploiting the scenarios of shifting climatic status associated with the representative concentration pathways, Monte Carlo simulation, and the classifications scheme based on the fuzzy set. For non-stochastic states of altering climatic elements, the richness of 115 reptiles improved in certain sites of northeastern, and western China and dropped in several areas of northern, eastern, central China, and southeastern China: roughly 59–74 reptiles forfeiting less than 20% of their present ranges, roughly 25–34 reptiles narrowing less than 20–40% of their present areal ranges, and roughly 105–111 reptiles inhabited more than 80% of their overall areal ranges. For the random status of shifting climatic elements, the count of reptiles that forfeited the various extent of the present or entire areal ranges descended with raising the eventuality; with a possibility of over 0.6, the count of reptiles that minified less than 20%, 20–40%, 40–60%, 60–80% and over 80% of the present ranges was roughly 28–49, 5–10, 1–3, 0–1 and 13–18, separately; the count of reptiles that inhabited below 20%, 20–40%, 40–60%, 60–80% and more than 80% of the entire real ranges was roughly 0–1, 5–6, 1–5, 0–2 and 35–36, separately. About 30% of 115 reptiles would face disappearance danger in response to moving climate conditions in the absence of adaption steps, and the conformation measures were indispensable for the reptiles that shrunk their areas.
... Across all archetypes, range contractions are more pronounced for localised endemics (i.e., Houniet et al., 2009;Busch et al., 2012;Kuhlman et al., 2012;Mokhatla et al., 2015;Simaika et al., 2015). Similar patterns are expected across all taxa, although uncertainty increases after mid-century (Baker et al., 2015;Box 5.5), and the exact response to future climate change is species specific (Coetzee et al., 2009;Houniet et al., 2009;Hole et al., 2009;Kuhlman et al., 2012;El-Gabbas et al., 2016;Taylor et al., 2016). ...
Scenario planning is a key approach for exploring the longer term consequences of nature-society interactions, and are used to inform policy making about the potential risks, opportunities and tradeoffs of different possible future pathways of change. Scenarios do not aim to forecast or predict the future, but rather to highlight how different potential futures may unfold and thereby assist in the formulation and implementation of policies and interventions. This assessment identified 355 scenario studies published since 2005 that have explored the future of biodiversity and Nature’s contributions to people (NCP) across Africa. The different scenario studies were clustered and compared in terms of five major alternative trajectories (or archetypes) of future change across Africa, respectively emphasising markets, policy reform, security (fortress world), and regional and local sustainability {5.1. 1, 5.2. 1, 5.3}.
For Africa as a whole, drivers related to population, urbanisation, consumption and natural resource use are expected to increase under all five major scenario trajectories assessed. Similarly, the impacts of climate change impacts in Africa are expected to increase under most scenarios (5.4, established but incomplete). However, substantial variation in all key drivers is expected between regions and different scenarios. The largest populations on the continent are expected under Fortress World scenarios, but remain largely rural with high direct dependence on natural resources, leading to sustained pressure on biodiversity and NCP. The lowest populations are expected under Policy Reform scenarios, and are expected to be largely concentrated in
... This corroborates the results from Sinervo et al. (2010), which derived global extinction projections for a large dataset comprising 34 families around the world. Currently, most of the studies on expansions or contractions of species distributions have focused on a restricted number of species (e.g., Kubisch et al. 2016;Park et al. 2017;Pontes-da-Silva et al. 2018) or on a restricted area (e.g., Aragón et al. 2010;Cabrelli et al. 2014;El-Gabbas et al. 2016). These studies also reinforce the results of the comprehensive approach developed by Sinervo et al. (2010). ...
While most of the available studies on climate change effects on Lepidosauria focused on changes in species distribution, none has focused on evaluating biogeographic and phylogenetic patterns of these effects. Here, we aimed to test if some lepidosaurian clades are more likely to be vulnerable than others and if their vulnerability corresponds with zoogeographic-related climatic conditions. We measured Pagel’s λ and Blomberg’s K and indicated a significant phylogenetic signal of lepidosaurians’ vulnerability to climate change, which tends to increase towards more recent clades. We performed a parsimony analysis of endemicity to determine the most climatically vulnerable zoogeographical realms, considering local lepidosaurian vulnerability. We recovered that taxa occurring in multiple zoogeographical realms are usually vulnerable across different geographic regions. Thus, we indicated that the lepidosaurian vulnerability is not related to their occurrence area, since most of the clades are shown to be vulnerable despite their biogeographic distribution or local climate conditions. We conducted a meta-analysis and showed that climate change is globally affecting taxa distribution, with no effects of heterogeneity. Finally, we performed a panbiogeographical analysis and found that Neotropical, Afrotropical, Australian, and Nearctic realms contain the highest number of biotic convergence zones. Areas with high spatial concentration of diversity also presented a greater number of vulnerable species, indicating that these areas can be possible targets for conservation at a larger scale and may help to identify especially diverse areas for conservation efforts at a small scale, focusing on buffering the effects of climate change on local populations.
... ABF, on the other hand, defines conservation value of a particular cell as the sum of values of the cells for all the species. We could not chose a priori between the two cell removal rules as both are frequently and collectively used to prioritize conservation areas in different climate conditions (Leach et al., 2013;Lehtomäki and Moilanen, 2013;El-Gabbas et al., 2016). Thus, we combined the results of prioritization ranking based on both cell removal rules in Scenarios 1 and 2 using Weighted Sum Overlay method in Arc GIS 9.3. ...
In recent years, scientific investigations on the effectiveness of Protected Areas (PAs) in conserving biodiversity and sustaining ecosystem functions under the impact of climate change have increasingly received more attention from researchers and park managers. In this study, we used a combination of species distribution modelling (SDM) and spatial hierarchical systematic conservation planning technique to delineate and prioritize areas for endemic plant species conservation under current and future (2050s, 2070s) climate conditions in Sikkim Himalaya. Data on endemic plant species occurrence were sourced from our already published studies and their potential suitable habitats under current and future climates were modelled using Maximum Entropy (MaxEnt) SDM. The MaxEnt habitat suitability projections of species were used in Zonation software to identify priority conservation areas in Sikkim Himalaya. We found that the existing PA network in the region was inadequate in conserving the endemic plant diversity either in the current or future climate scenarios. Based on our results, we propose addition of 896 square kilometres (sq km) to the existing PA network in the study area to ensure meaningful conservation goals. Additionally, we propose creation of 3 new PAs (Yumsedong, Lachen and Chungthang) and the need for expanding the boundaries of existing PAs (Maenam, Fambong Lho and Barsey) in the study area. Our analyses show that to mitigate the effects of ensuing climate change, a single large PA with wide geographic and elevational extents instead of several smaller PAs would be a more prudent strategy for conserving the plant diversity in the Himalaya.
... Ces relations sont projetées dans une région géographique spécifique en utilisant une couche environnementale maillée (Fig. 37). Le choix de la méthode SDM est influencé par l'accès au logiciel, par la multiplicité des algorithmes, par la disponibilité des données de présence/absence, mais aussi par les objectifs particuliers de l'étude (LEATHWICK et al., 2006 ;ARAUJO & PETERSON, 2012 ;EL-GABBAS et al., 2016). Bien que différentes disciplines et études sur des régions géographiques utilisent différentes techniques, celle des modèles de répartition des espèces s'impose dans de nombreuses études. ...
La compréhension des effets locaux du changement climatique sur la biodiversité est essentielle pour orienter les politiques environnementales et de gestion des espaces naturels. Le manque de connaissances au niveau régional a conduit au développement d’un programme de recherche « les sentinelles du climat ». L’hypothèse principale est que les effets sur la biodiversité sont particulièrement détectables chez des espèces ou groupes d’espèces qui ont de faibles capacités de déplacements. Ces espèces « sentinelles du climat » seraient les premières à répondre aux variations climatiques locales par adaptation ou par extinction locale. La région Nouvelle-Aquitaine est un contexte géographique intéressant car à la fois elle est exposée à l’évolution du climat et elle offre une grande variété d’écosystèmes naturels. A partir du territoire, une vingtaine d’indicateurs du changement climatique ont été développés dans différents écosystèmes suivant une méthode basée sur la production scientifique internationale et les connaissances empiriques naturalistes. Dans le cadre de la mise en place de l’analyse des données résultantes, un premier travail a été réalisé sur une synthèse bibliographique permettant de lister et de caractériser différents modèles existants et utilisés spécifiquement dans la recherche sur le changement climatique et ses impacts sur la répartition de la biodiversité. Des articles de revue à impact factor ont été sélectionnés et exploités afin de déterminer leurs pertinences pour les indicateurs choisis dans le programme. Un grand nombre de modèles ont été recensés allant des plus simples aux plus complexes. Chaque modèle possède ses avantages et ses limites, une combinaison de modèles est souhaitable pour consolider les hypothèses et permet d’obtenir des cartes prédictives plus fiables Par conséquent, la modélisation d’ensemble a été considérée comme la meilleure solution pour réduire les incertitudes et les biais. La prochaine phase est de développer et de tester l’outil retenu en intégrant peu à peu les données recueillies sur le terrain pour les analyser et aborder leurs extrapolations.
... Across all archetypes, range contractions are more pronounced for localised endemics (i.e., Houniet et al., 2009;Busch et al., 2012;Kuhlman et al., 2012;Mokhatla et al., 2015;Simaika et al., 2015). Similar patterns are expected across all taxa, although uncertainty increases after mid-century (Baker et al., 2015;Box 5.5), and the exact response to future climate change is species specific (Coetzee et al., 2009;Houniet et al., 2009;Hole et al., 2009;Kuhlman et al., 2012;El-Gabbas et al., 2016;Taylor et al., 2016). ...
EXECUTIVE SUMMARY
Scenario planning is a key approach for exploring the longer term consequences of nature-society interactions, and are used to inform policy making about the potential risks, opportunities and trade-offs of different possible future pathways of change. Scenarios do not aim to forecast or predict the future, but rather to highlight how different potential futures may unfold and thereby assist in the formulation and implementation of policies and interventions. This assessment identified 355 scenario studies published since 2005 that have explored the future of biodiversity and Nature’s contributions to people (NCP) across Africa. The different scenario studies were clustered and compared in terms of five major alternative trajectories (or archetypes) of future change across Africa, respectively emphasising markets, policy reform, security (fortress world), and regional and local sustainability {5.1.1, 5.2.1, 5.3}.
For Africa as a whole, drivers related to population, urbanisation, consumption and natural resource use are expected to increase under all five major scenario trajectories assessed. Similarly, the impacts of climate change impacts in Africa are expected to increase under most scenarios (5.4, established but incomplete). However, substantial variation in all key drivers is expected between regions and different scenarios. The largest populations on the continent are expected under Fortress World scenarios, but remain largely rural with high direct dependence on natural resources, leading to sustained pressure on biodiversity and NCP. The lowest populations are expected under Policy Reform scenarios, and are expected to be largely concentrated in large urban centres. However, increased wealth, consumption and global trade under this scenario also leads to high demand for food and other resources across Africa {5.4} (established but incomplete).
Under most future scenarios, Africa is increasingly interconnected with the rest of the world through global markets and trade (established but incomplete). Connections between different subregions in Africa are also likely to increase. Consequently, decisions and activities elsewhere in the world and in different parts of the continent may increasingly affect human well-being, NCP and biodiversity across Africa (5.8, established but incomplete). Large-scale resource extraction by multi-national companies are expected to lead to land grabbing, increased conflict, displacement and migration under several scenarios (5.4.4; 5.8, established but incomplete). While global trade has the potential to catalyse further economic and social development in Africa, this assessment suggests that under many scenarios the primary beneficiaries are overseas markets and investors. In the longer term, ecosystem service provision and local food security in Africa may be undermined unless trade and the distribution of its benefits are carefully governed {5.8}.
The impacts of human activities are expected to result in further losses of terrestrial, freshwater and marine biodiversity, as well most reductions in many provisioning and regulating services across Africa (established, but incomplete). In the short-term, habitat loss through land-use change may have more severe consequences for biodiversity and NCP than a changing climate. Current protected areas across Africa are generally not well aligned with future climate-related range shifts of species, implying increased resource needs to meet conservation objectives in the future. Although there is variation in the level of water availability across different scenarios and regions, water stress in Africa is expected to increase under all scenarios, particularly in the southern African region. Similarly, pollination services and regulation of climate and storm protection in Africa are likely to decrease under most scenarios. On the other hand, terrestrial food production and energy provision through biofuels is expected to increase under most future scenarios {5.5}.
Increasing trade-offs are expected in the water-food-energy nexus. The increase in trade-offs is particularly pronounced under scenarios that emphasise economic growth (5.7; 5.8, established but incomplete). There are more opportunities for synergies under scenarios that emphasise sustainability and the adoption and enforcement policies that increase and modernise agricultural production and access (5.7 established, but incomplete). Under all scenarios, achieving the goal of eradicating hunger is unlikely without compromising water quality. Energy security and access is best met under scenarios that focus on
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mitigating the impacts of climate change through proactive climate action and efforts to enhance regional sustainability (5.4; 5.7, established but incomplete).
Overall levels of human well-being are expected to improve under most future scenario trajectories, but Africa continues to face unique challenges (established but incomplete). Poverty is generally expected to decline, but major pockets of poverty persist, particularly in rural areas. Equity similarly shows mixed results, with progress towards greater equity threatened by patchy development across Africa and asset capture by foreign companies. Health is not expected to improve significantly under most scenarios, though health concerns shift from lack of access to food and medicine to problems associated with modern lifestyles (e.g., diabetes, air pollution). Security and freedom of choice are only expected to improve significantly under very particular scenario conditions where global cooperation and African national governance align effectively {5.5}.
Alignment of the Agenda 2063 aspirations, Sustainable Development Goals and Aichi targets can facilitate interventions that achieve multiple transformative outcomes by linking the conservation of biodiversity and NCP with enhanced human well-being in Africa (established but incomplete). Scenarios that prioritise sustainable development trajectories, with strong regional integration, collaboration, proactive and inclusive governance, show the potential for avoiding dependencies and lock-in behaviours associated with scenarios where rapid exploitation of the natural environment for short-term gains are promoted. While all of the scenarios involve trade-offs, scenarios that involve the development of strong regional institutions and good governance offer the best options for maintaining ecological integrity in support of human well-being and sustainable development {5.7}.
There are currently clear gaps in the type and distribution of scenario studies in Africa, with some subregions – such as central, northern and western Africa – being particularly poorly covered (established but incomplete). Most of the studies assessed in this chapter have addressed future changes in southern Africa (37%) and eastern Africa (18%). Almost 50% of the studies focused on local scales, while 26% covered multiple countries, and 18% are part of global scenario exercises. Only 11% of the assessed studies were conducted at the national scale, which is arguably the most useful scale for decision-making. The majority of the studies (80%) have had a broad exploratory focus, with only 24% focused on assessing specific policies or interventions. Furthermore, most studies (46%) used existing scenario storylines from other (often global) studies to explore future impacts on biodiversity and NCP in Africa; only 14% developed new integrated scenario storylines (5.2.2, established but incomplete). Furthermore, the links between NCP and human well-being are not often explored in much detail beyond climate change impacts on disease vectors and livelihoods {5.5}.
Scenario studies in Africa are heavily biased towards modelling climate change impacts, and do not sufficiently incorporate broad stakeholder participation or indigenous and local knowledge (ILK). Only 12% of the studies assessed included a participatory approach, and only 3% integrated ILK to some extent. In contrast, modelling exercises have been widespread (90% of studies), but mostly focus on climate change impacts (60%). The main models used in African scenario studies are correlative models (48%), followed by process-based models (29%) and expert-based models (8%) (5.2.2, established but incomplete). There is a critical need to broaden the scenario approaches used in the region to better incorporate ILK and participatory approaches.
Concerted efforts are needed to mobilise financial resources and build the capacity of African researchers, policymakers and institutions to understand, carry out and use scenario analyses. Although over half (56%) the studies assessed included at least one African-based author, only 19% of the studies involved only authors affiliated with African institutions. South Africa is by far the most productive African country, contributing to 29% of all studies. However, there is very little collaboration between South Africa-based authors and authors from other African countries (section 5.2.2, established but incomplete). Existing regional and international expertise should be leveraged to train a wider set of researchers in the use of scenario methods, and in communicating outputs of scenarios to decision-makers (5.2.2, unresolved).
... As the correlation between predictors varies from one study area to another, different environmental predictor combinations were used at regional and global scales. Some predictors were not useful at the regional scale, and hence were excluded a priori; for example, precipitation of driest month does not show any variability across Egypt because most of Egypt receives no precipitation at all in summer, reflecting its hyper-arid climate (El-Gabbas, Baha El Din, Zalat, & Gilbert, 2016). We ensured minimum multi-collinearity at both scales by selecting only predictors that maintain a maximum generalized variance inflation factor value less than 3 (see Table S2 for the list of predictors used at either scale). ...
Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence-only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species-specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point-process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor (“prior”) to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias-free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data-poor regions.
... The potential effects of climate change can be assessed by developing models that provide working hypotheses to support research and conservation strategies (El-Gabbas et al. 2016;Jones et al. 2016). Previous works on lizards from temperate (Vera-Escalona et al. 2012;Nori et al. 2016) and cold environments Breitman et al. 2015) in Patagonia, have shown changes in the distributional ranges by comparing modeled present niche versus other temporary scenarios. ...
The consequences of global climate change can already be seen in many physical and biological systems and these effects could change the distribution of suitable areas for a wide variety of organisms to the middle of this century. We analyzed the current habitat use and we projected the suitable area of present conditions into the geographical space of future scenarios (2050), to assess and quantify whether future climate change would affect the distribution and size of suitable environments in two Pristidactylus lizard species. Comparing the habitat use and future forecasts of the two studied species, P. achalensis showed a more restricted use of available resource units (RUs) and a moderate reduction of the potential future area. On the contrary, P. nigroiugulus uses more available RUs and has a considerable area decrease for both future scenarios. These results suggest that both species have a moderately different trend towards reducing available area of suitable habitats, the persistent localities for both 2050 CO2 concentration models, and in the available RUs used. We discussed the relation between size and use of the current habitat, changes in future projections along with the protected areas from present-future and the usefulness of these results in conservation plans. This work illustrates how ectothermic organisms might have to face major changes in their availability suitable areas as a consequence of the effect of future climate change.
The strategic geographical location of Palestine, along with its topography, diversity of ecosystems and climatic changes, create a suitable environment for the presence of reptiles. Zoos in the Gaza Strip have a variety of zoo animals including terrestrial, freshwater and marine reptiles. The present study aims to document the Palestinian reptiles held in captivity and/or kept stuffed at zoos in the Gaza Strip. Repeated visits to Gaza zoos were conducted over a ten-year period from 2010 to 2019 to achieve the purpose of the study. Digital cameras and guidebooks were used to document and identify Palestinian reptiles. In addition, interviews were carried out with zoo owners and workers and some reptile hunters who provide live and even dead specimens to Gaza zoos and had their specimens identified and photographed. A total of 29 Pal-estinian reptile species (one crocodile, four turtles, six lizards and 18 snake species), belonging to 3 orders and 15 families, were recorded as live or preserved specimens at Gaza zoos. The Nile Crocodile (Crocodylus niloticus), which went extinct in Palestine since the beginning of the 20 th century, is the biggest reptilian encountered in the current study. Nearly all these reptiles were trapped using different means in the marine, freshwater and terrestrial environments of the Gaza Strip. Two of the highly threatened global sea turtle species were encountered: the Loggerhead Sea Turtle (Caretta caretta) and the Green Sea Turtle (Chelonia mydas). The Spur-thighed Tortoise (Testudo graeca), which is classified as vulnerable by the IUCN, is kept in relatively large numbers in cages at Gaza zoos. The Desert Monitor (Varanus griseus), Pales-tine Viper (Daboia palaestinae), and Syrian Black Snake (Coluber jugularis asianus) were the most occurring lizard and snake species at Gaza zoos. In conclusion, zoos are good tools that contribute to Palestinians' knowledge of their wildlife resources. The ecological role of reptiles in their ecosystems requires Palestinians to protect and conserve them as well as all forms of wildlife in a sustainable manner.
Climate change is expected to cause irreversible changes to biodiversity, but predicting those risks remains uncertain. I synthesized 485 studies and more than 5 million projections to produce a quantitative global assessment of climate change extinctions. With increased certainty, this meta-analysis suggests that extinctions will accelerate rapidly if global temperatures exceed 1.5°C. The highest-emission scenario would threaten approximately one-third of species, globally. Amphibians; species from mountain, island, and freshwater ecosystems; and species inhabiting South America, Australia, and New Zealand face the greatest threats. In line with predictions, climate change has contributed to an increasing proportion of observed global extinctions since 1970. Besides limiting greenhouse gases, pinpointing which species to protect first will be critical for preserving biodiversity until anthropogenic climate change is halted and reversed.
Reptilian species can be regarded as a bioindicator of climate change. However, limited studies are available on the effect of climate change on the distribution of reptiles in India. Nilssonia nigricans is a Critically Endangered Soft-shell Turtle found in Northeastern India, facing severe threats due to the Anthropocene. Previously, this species was considered extinct in the wild; however, recently, some populations have been discovered in the Brahmaputra Valley. In this context, a study was initiated to understand the impact of climate change on the distribution of N. nigricans. Maximum Entropy (MaxEnt) is employed to predict the potential distribution range of this species for two time periods: the 2050s (2041-2060) and the 2070s (2061-2080) under both RCP 4.5 and RCP 8.5 scenarios. The study found that the Mean Temperature of the Wettest Quarter, Elevation and Precipitation Seasonality will be the major factors that determine the distribution of N. nigricans. The model indicated that under current conditions, 21.78% of the study area provides a suitable habitat for N. nigricans. Future predictions suggest a potential range contraction of 16.09% during the 2050s under RCP 4.5 and an increase of 25.83% in the 2050s under the RCP 8.5 scenario. However, in the 2070s, the habitat range of this species may decline to 10.77% under RCP 4.5 and 19.97% under RCP 8.5. The comparison of various RCP scenarios illustrated that the habitat range is shrinking under RCP 4.5 scenarios. Among the states, Assam is the only place that shows the highest potential for suitable sites, covering an area of 58,535 km 2 (58.47%), while the remaining 41,581 km 2 (41.53%) is deemed unsuitable for its current distribution. The study reveals that N. nigricans serves as a bioindicator of climate change in Northeast India, and the study's results will be helpful in creating conservation and management attention for the species.
The range extension of animals is influenced by various factors, particularly environmental variables and ecological requirements. In this study, we have attempted to quantify the potential current distribution range of the Burmese Python Python bivittatus in and around the Ganga Basin. We collected the Burmese Python sightings between 2007 and 2022 from various direct and indirect sources and recorded 38 individuals, including eight females and five males; the rest were not examined for their sex. Out of these, 12 individuals were rescued from human habitations. Most python sightings were observed in Uttarakhand and Uttar Pradesh (n = 12 each), followed by Bihar (n = 6). The expanded minimum convex polygon (MCP) range was calculated as 60,534.2 km2. In addition, we quantified the potential current distribution status of this species using 19 bioclimatic variables with the help of MaxEnt software and the SDM toolbox in Arc GIS. The suitable area for the python distribution was calculated as 1,03,547 km2. We found that the following variables influenced the python distribution in the range extended landscape: Annual Mean Temperature (20.9%), Precipitation of Wettest Quarter (6.4 %), Precipitation of Driest Quarter (30.1 %), Precipitation of Warmest Quarter (0.3%), Isothermality (0.1%), Temperature Annual Range (18.7 %), Mean Temperature of Wettest Quarter (11.4 %), Mean Temperature of Driest Quarter (2.2 %), Land use/land cover (3.3 %), and Elevation (6.6 %). These results will support the field managers in rescuing individuals from conflict areas and rehabilitating them based on the appropriate geographical region.
Wildlife trade for non-native pets is an important and increasing driver of biodiversity loss and often compromises the standards required for protection. However, the growing interest in non-native pets has posed the issue of invasive non-native species to wildlife managers and conservationists. Instituting effective policies regarding non-native species requires a thorough understanding of the potential range of the species in new environments. In this study, we used an ensemble of ten species distribution models to predict the potential distribution for 23 commonly traded species of reptiles across the Middle East. We used ten modeling techniques implemented in the Biomod2 package and ensemble forecasts. Final models contained fourteen environmental variables, including climatic, topographic, and land cover/land use variables. Our results indicate that all Middle Eastern countries included suitable habitats for at least six species, except Qatar, Kuwait and Bahrain for which the models did not predict any suitable habitats. Our study showed that Lebanon, Palestine, Turkey, and Israel face the highest risk of biological invasion by the species on the whole. Also, the results showed that Centrochelys sulcata , Chamaeleo calyptratus , and Trachemys scripta posed the highest risk of spreading in this area. Information on which species pose a greater danger as invaders and the possible impacts of their introduction will be a valuable contribution to the development of conservation plans and policies.
Recognizing spatiotemporal patterns of rainfall trends has been of major concern for better planning and management of water resources. In this study, characterization of the variability in rainfall patterns was performed at 11 stations in Sinai Peninsula during the period 1990–2014 using the wavelet transform and descriptive statistics. Discrete wavelet transform technique was utilized in accordance with Mann–Kendall test to investigate trends and dominant periodicities associated with the seasonal and annual rainfall. Furthermore, the evaluation of significant periods of variations corresponding to the rainfall time series was done using the continuous wavelet transform. The results indicated that the rainfall was in general decreasing and declined trends were recognized in both winter and annual rainfall series; however, observed trends were statistically significant only in the northeastern part of the study area. Moreover, the results wavelet transformation analysis revealed an overall periodic variation in annual records accompanied by 4-–8-month signal that is more significant in influencing the identified trends; however, most of the periodic components were statistically insignificant. Based on the results of the spatial analysis, significant negative trends were mostly identified at the investigated stations. Overall, the winter and the annual rainfall records over Sinai Peninsula exhibited a considerable temporal variability accompanied by significant negative trends in northeastern part throughout the investigated time frame.
Species distribution models have become essential tools in ecology and wildlife conservation. However, their reliability when used for conservation management is often compromised by many challenges and limitations, as for example the lack of sufficient data-quality and sampling bias. Especially when used for areas in developing countries, where unbiased good-quality data are scarce, the robustness of species distribution models is questionable. In this thesis, I studied some crucial issues affecting the reliability of presence-only species distribution models for wildlife conservation in developing countries.
In the first chapter, I studied the issue of sampling bias in data-poor situations of developing countries and how to correct for it in presence-only species distribution models. I implemented model-based bias correction by incorporating additional bias-predictors describing site accessibility (distance to closest city, road, and protected area) or estimated sampling effort. I showed that bias correction led to improved predictions, with comparable results using the three modelling algorithms (GLMs with subset selection, GLMs fitted with an elastic-net penalty and Maxent, all under the point process modelling framework). The improved prediction due to sampling bias correction was dependent on how well the bias-predictors describe the bias in the available data, with higher improvement when accessibility bias variables were used. However, objectively evaluating sampling bias correction requires bias-free presence-absence testing data, which is typically not available in data-poor situations. Nevertheless, my results showed that model-based bias correction is a useful tool to improve predictions in data-poor situations, in which other bias correction methods may not be applicable.
In the second chapter, I evaluated the adequacy of presence-only data from within one developing country’s boundary to calibrate national species distribution models. I used spatially explicit information representing predictions from the species’ global environmental niche (potential distribution) as an additional predictor (prior) in regional models aiming to improve predictions. The use of priors did not lead to improved regional predictions; meanwhile, the correction for sampling bias led to improved predictions whichever prior was used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global data did not improve regional model performance. However, the actual improvement could not be quantified without enough bias-free regional data. Nevertheless, predictions from global models, interpolated to regional scales, can still have great potential to guide future surveys and improve regional sampling in data-poor regions.
In the third chapter, I assessed the sensitivity (robustness) of a spatial conservation prioritisation application in an exemplary data-poor developing country to common sources of uncertainty, which are related to the quality of available species distributional data and the choice of some of the user-defined software parameters. I also evaluated the effectiveness of the Egyptian protected areas network for conservation. Conservation planning in data-poor situations was found to be 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. Results showed a lower limit for data quality for the usefulness of the spatial conservation prioritisation approach, demanding improved data quality for data-poor countries. Using currently available data on the Egyptian butterflies, reptiles, and mammals, the Egyptian protected areas network was found inefficient for wildlife conservation. I determined the top priority sites for further on-the-ground field evaluation as potential areas for protected areas expansion.
Despite the promising results of improved predictions after correcting for sampling bias in data-poor developing countries, this improvement is not guaranteed and hence should not be considered a replacement for the urgent need for improving sampling strategies for the collection of biodiversity data on as many taxonomic groups as possible. Improving sampling strategies and data-quality from data-poor countries (mainly from the less visited areas) will consolidate the use of species distribution models for conservation planning in these areas.
Ptyas doriae is a rare snake in northeastern India, Myanmar and southern China. The original description and subsequent accounts of this species were all very brief. We studied most of the available specimens of this species and on this basis we provide a detailed redescription of the species. We give an account of intrapopulational variation in scalation characters and colouration and we also report on a previously undescribed colour morph from Manipur, India. We have examined the holotype of Ptyas hamptoni and found that characters used to distinguish it from P. doriae are inadequate and therefore we herein synonymize the former with the latter species. We also predict the potential distribution of P. doriae by using Maximum Entropy modeling.
The evidence for rapid climate change now seems overwhelming. Global temperatures are predicted to rise by up to 4 °C by 2100, with associated alterations in precipitation patterns. Assessing the consequences for biodiversity, and how they might be mitigated, is a Grand Challenge in ecology.
The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models. It avoids the supposed subjectivity in the threshold selection process, when continuous probability derived scores are converted to a binary presence–absence variable, by summarizing overall model performance over all possible thresholds. In this manuscript we review some of the features of this measure and bring into question its reliability as a comparative measure of accuracy between model results. We do not recommend using AUC for five reasons: (1) it ignores the predicted probability values and the goodness-of-fit of the model; (2) it summarises the test performance over regions of the ROC space in which one would rarely operate; (3) it weights omission and commission errors equally; (4) it does not give information about the spatial distribution of model errors; and, most importantly, (5) the total extent to which models are carried out highly influences the rate of well-predicted absences and the AUC scores.
PERSPECTIVES scious processing in humans. Moreover, several studies have reported residual local and specific brain activation patterns in vegetative state patients, whereas long-range neural integration observed during conscious processing was lacking (4, 5). ...
Many museums and academic institutions maintain first-rate collections of biological materials, ranging from preserved whole organisms to DNA libraries and cell lines. These biological collections make innumerable contributions to science and society in areas as divergent as homeland secu- rity, public health and safety, monitoring of environmental change, and traditional taxonomy and systematics. Moreover, these collections save governments and taxpayers many millions of dollars each year by effectively guiding government spending, preventing catastrophic events in public health and safety, eliminating redundancy, and securing natural and agricultural resources. However, these contributions are widely underappre- ciated by the public and by policymakers, resulting in insufficient financial support for maintenance and improvement of biological collections.
Several biogeographic theories propose that the species richness depends on the structure and ecosystems diversity. The habitat productivity, a surrogate for these variables, can be evaluated through satellite imagery, namely using vegetation indexes (e.g. NDVI). We analyzed the correlation between species richness (from the Portuguese Atlas of Amphibians and Reptiles) and NDVI (from Landsat, MODIS, and Vegetation images). The species richness database contains more than 80000 records, collected from bibliographic sources (at 1 or 10 km of spatial resolution) and fieldwork sampling stations (recorded with GPS devices). Several study areas were chosen for Landsat images (three subsets), and all Portugal for MODIS and Vegetation images. The Landsat subareas had different climatic and habitat characteristics, located in the north, center and south of Portugal. Different species richness datasets were used depending on the image spatial resolution: data with metric resolution were used for Landsat, and with 1 km resolution, for MODIS and Vegetation images. The NDVI indexes and all the images were calculated/processed in an open source software (Quantum GIS). Several plug-ins were applied in order to automatize several procedures. We did not find any correlation between the species richness of amphibians and reptiles (not even after separating both groups by species of Atlantic and Mediterranean affinity) and the NDVI calculated with Landsat, MODIS and Vegetation images. Our results may fail to find a relationship because as the species richness is not correlated with only one variable (NDVI), and thus other environmental variables must be considered.
Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.
Decision-making for conservation management often involves evaluating risks in the face of environmental uncertainty. Models support decision-making by (1) synthesizing available knowledge in a systematic, rational and transparent way and (2) providing a platform for exploring and resolving uncertainty about the consequences of management decisions. Despite their benefits, models are still not used in many conservation decision-making contexts. In this article, we provide evidence of common objections to the use of models in environmental decision-making. In response, we present a series of practical solutions for modellers to help improve the effectiveness and relevance of their work in conservation decision-making.
Global review.
We reviewed scientific and grey literature for evidence of common objections to the use of models in conservation decision-making. We present a set of practical solutions based on theory, empirical evidence and best-practice examples to help modellers substantively address these objections.
We recommend using a structured decision-making framework to guide good modelling practice in decision-making and highlight a variety of modelling techniques that can be used to support the process. We emphasize the importance of participatory decision-making to improve the knowledge-base and social acceptance of decisions and to facilitate better conservation outcomes. Improving communication and building trust are key to successfully engaging participants, and we suggest some practical solutions to help modellers develop these skills.
If implemented, we believe these practical solutions could help broaden the use of models, forging deeper and more appropriate linkages between science and management for the improvement of conservation decision-making.
Nubian Ibex (Capra nubiana) is a one of the few species of large mammal species in the Egyptian fauna. The range inhabited by the species in the mountains of Sinai represents an important bridge between its distribution in Asia and Africa. The impact of various environmental factors shaping its distribution were investigated using distribution modeling methods. Maximum entropy modeling showed that presence of water resources is most influential in the species distribution in south Sinai, followed by slope, habitat, altitude and finally aspect. The current range of the species was estimated as 506 square kilometers in the mountains of south Sinai.
Spanish
El íbice de Nubia (Capra nubiana) es una de las pocas especies de mamíferos grandes en la fauna egipcia. El rango que ocupa la especie en las montañas del Sinai, representa un puente entre Africa y Asia, en su área de distribución. El impacto ambiental de varios factores que determinan su distribución se ha investigado modelizando su distribución. El modelo de máxima entropía muestra que la presencia de recursos hídricos es lo que más influye en la distribución de la especie en el sur del Sinai, seguido de la pendiente, hábitat, altitud y por último el aspecto. El área de distribución de la especie se ha estimado en unos 506 Km 2 , en las montañas del sur del Sinai. Palabras clave: asentamientos beduinos, corredores, íbice de Nubia, Maxent, rango de distribución, Sinai meridional.
Habitat suitability models can be generated using methods requiring information on species presence or species presence and absence. Knowledge of the predictive performance of such methods becomes a critical issue to establish their optimal scope of application for mapping current species distributions under different constraints. Here, we use breeding bird atlas data in Catalonia as a working example and attempt to analyse the relative performance of two methods: the Ecological Niche factor Analysis (ENFA) using presence data only and Generalised Linear Models (GLM) using presence/absence data. Models were run on a set of forest species with similar habitat requirements, but with varying occurrence rates (prevalence) and niche positions (marginality). Our results support the idea that GLM predictions are more accurate than those obtained with ENFA. This was particularly true when species were using available habitats proportionally to their suitability, making absence data reliable and useful to enhance model calibration. Species marginality in niche space was also correlated to predictive accuracy, i.e. species with less restricted ecological requirements were modelled less accurately than species with more restricted requirements. This pattern was irrespective of the method employed. Models for wide-ranging and tolerant species were more sensitive to absence data, suggesting that presence/absence methods may be particularly important for predicting distributions of this type of species. We conclude that modellers should consider that species ecological characteristics are critical in determining the accuracy of models and that it is difficult to predict generalist species distributions accurately and this is independent of the method used. Being based on distinct approaches regarding adjustment to data and data quality, habitat distribution modelling methods cover different application areas, making it difficult to identify one that should be universally applicable. Our results suggest however, that if absence data is available, methods using this information should be preferably used in most situations.
Range shifts due to climate change may cause species to move out of protected areas. Climate change could therefore result in species range dynamics that reduce the relevance of current fixed protected areas in future conservation strategies. Here, we apply species distribution modeling and conservation planning tools in three regions (Mexico, the Cape Floristic Region of South Africa, and Western Europe) to examine the need for additional protected areas in light of anticipated species range shifts caused by climate change. We set species representation targets and assessed the area required to meet those targets in the present and in the future, under a moderate climate change scenario. Our findings indicate that protected areas can be an important conservation strategy in such a scenario, and that early action may be both more effective and less costly than inaction or delayed action. According to our projections, costs may vary among regions and none of the three areas studied will fully meet all conservation targets, even under a moderate climate change scenario. This suggests that limiting climate change is an essential complement to adding protected areas for conservation of biodiversity.
Background/Question/Methods
Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity. Model complexity is typically constrained via a process known as L1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and compare models selected using information criteria to models selected using other criteria that are common in the literature. We evaluate model performance using occurrence data generated from a known “true” initial Maxent model, using several different metrics for model quality and transferability.
Results/Conclusions
We demonstrate that models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species’ distributions, and reduced transferability to other time periods. We also measure the relative effectiveness of different model selection criteria, and demonstrate that information criteria may offer significant advantages over the AUC-based methods commonly used in the literature.
Abstract Although bioclimatic modelling is often used to estimate potential impacts of likely climate changes, little has been done to assess the reliability and variability of projections. Here, using four niche-based models, two methods to derive probability values from models into presence–absence data and five climate change scenarios, I project the future potential habitats of 1350 European plant species for 2050. All 40 different projections of species turnover across Europe suggested high potential species turnover (up to 70%) in response to climate change. However variability in the potential distributional changes of species across climate scenarios was obscured by a strong variability in projections arising from alternative, yet equally justifiable, niche-based models. Therefore, projections of future species distributions and derived community descriptors cannot be reliably discussed unless model uncertainty is quantified explicitly. I propose and test an alternative way to account for modelling variability when deriving estimates of species turnover (with and without dispersal) according to a range of climate change scenarios representing various socio-economic futures.
Aim Several studies have found that more accurate predictive models of species’ occurrences can be developed for rarer species; however, one recent study found the relationship between range size and model performance to be an artefact of sample prevalence, that is, the proportion of presence versus absence observations in the data used to train the model. We examined the effect of model type, species rarity class, species’ survey frequency, detectability and manipulated sample prevalence on the accuracy of distribution models developed for 30 reptile and amphibian species.
Location Coastal southern California, USA.
Methods Classification trees, generalized additive models and generalized linear models were developed using species presence and absence data from 420 locations. Model performance was measured using sensitivity, specificity and the area under the curve (AUC) of the receiver‐operating characteristic (ROC) plot based on twofold cross‐validation, or on bootstrapping. Predictors included climate, terrain, soil and vegetation variables. Species were assigned to rarity classes by experts. The data were sampled to generate subsets with varying ratios of presences and absences to test for the effect of sample prevalence. Join count statistics were used to characterize spatial dependence in the prediction errors.
Results Species in classes with higher rarity were more accurately predicted than common species, and this effect was independent of sample prevalence. Although positive spatial autocorrelation remained in the prediction errors, it was weaker than was observed in the species occurrence data. The differences in accuracy among model types were slight.
Main conclusions Using a variety of modelling methods, more accurate species distribution models were developed for rarer than for more common species. This was presumably because it is difficult to discriminate suitable from unsuitable habitat for habitat generalists, and not as an artefact of the effect of sample prevalence on model estimation.
Giant pandas (Ailuropoda melanoleuca) are one of the most widely recognized endangered species globally. Habitat loss and fragmentation are the main threats, and climate change could significantly impact giant panda survival. We integrated giant panda habitat information with general climate models (GCMs) to predict future geographic distribution and fragmentation of giant panda habitat. Results support a major general prediction of climate change—a shift of habitats towards higher elevation and higher latitudes. Our models predict climate change could reduce giant panda habitat by nearly 60% over 70 years. New areas may become suitable outside the current geographic range but much of these areas is far from the current giant panda range and only 15% fall within the current protected area system. Long-term survival of giant pandas will require the creation of new protected areas that are likely to support suitable habitat even if the climate changes.
Lythrum salicaria constituye una especie invasora perjudicial para los hábitats pantanosos en Norteamérica. Para estimar cuáles son las áreas vulnerables a esta especie en Kansas, modelamos la distribución potencial de la especie utilizando registros actuales del estado, datos de índices de vegetación tomados remotamente por el Moderate Resolution Imaging Spectrometer (MODIS), y el Genetic Algorithm for Rule-Set Prediction (GARP). Modelos construidos usando sólo las localidades del noreste de Kansas (el origen de la invasión dentro del estado) predijeron consistentemente a las localidades de prueba en otras partes del estado con omisión despreciable. Un análisis adicional usando registros de todos los condados en los cuales se conoce que la especie está presente mostró una predicción similar. Todos los modelos indicaron condiciones apropiadas para la especie en la mayor parte del este y del centro de Kansas, tanto como en áreas ribereñas e irrigadas en la parte occidental del estado. El enfoque presentado aquí posiblemente será de utilidad en la evaluación del potencial de colonización regional de otras especies invasoras recientemente detectadas antes de que otros estudios puedan ser realizados.
Many museums and academic institutions maintain first-rate collections of biological materials, ranging from preserved whole
organisms to DNA libraries and cell lines. These biological collections make innumerable contributions to science and society
in areas as divergent as homeland security, public health and safety, monitoring of environmental change, and traditional
taxonomy and systematics. Moreover, these collections save governments and taxpayers many millions of dollars each year by
effectively guiding government spending, preventing catastrophic events in public health and safety, eliminating redundancy,
and securing natural and agricultural resources. However, these contributions are widely underappreciated by the public and
by policymakers, resulting in insufficient financial support for maintenance and improvement of biological collections.
REPTILE SPECIES ARE DECLINING ON A GLOBAL SCALE. SIX SIGNIFICANT THREATS TO REPTILE POPULATIONS ARE HABITAT
LOSS AND DEGRADATION, INTRODUCED INVASIVE SPECIES, ENVIRONMENTAL POLLUTION, DISEASE, UNSUSTAINABLE
USE, AND GLOBAL CLIMATE CHANGE.
The Mediterranean basin is considered a hotspot of biological diversity with a long history of modification of natural ecosystems by human activities, and is one of the regions that will face extensive changes in climate. For 181 terrestrial mammals (68% of all Mediterranean mammals), we used an ensemble forecasting approach to model the future (approx. 2100) potential distribution under climate change considering five climate change model outputs for two climate scenarios. Overall, a substantial number of Mediterranean mammals will be severely threatened by future climate change, particularly endemic species. Moreover, we found important changes in potential species richness owing to climate change, with some areas (e.g. montane region in central Italy) gaining species, while most of the region will be losing species (mainly Spain and North Africa). Existing protected areas (PAs) will probably be strongly influenced by climate change, with most PAs in Africa, the Middle East and Spain losing a substantial number of species, and those PAs gaining species (e.g. central Italy and southern France) will experience a substantial shift in species composition.
BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package.
Current climate change is a major threat to biodiversity. Species unable to adapt or move will face local or global extinction and this is more likely to happen to species with narrow climatic and habitat requirements and limited dispersal abilities, such as amphibians and reptiles. Biodiversity losses are likely to be greatest in global biodiversity hotspots where climate change is fast, such as the Iberian Peninsula. Here we assess the impact of climate change on 37 endemic and nearly endemic herptiles of the Iberian Peninsula by predicting species distributions for three different times into the future (2020, 2050 and 2080) using an ensemble of bioclimatic models and different combinations of species dispersal ability, emission levels and global circulation models. Our results show that species with Atlantic affinities that occur mainly in the North-western Iberian Peninsula have severely reduced future distributions. Up to 13 species may lose their entire potential distribution by 2080. Furthermore, our analysis indicates that the most critical period for the majority of these species will be the next decade. While there is considerable variability between the scenarios, we believe that our results provide a robust relative evaluation of climate change impacts among different species. Future evaluation of the vulnerability of individual species to climate change should account for their adaptive capacity to climate change, including factors such as physiological climate tolerance, geographical range size, local abundance, life cycle, behavioural and phenological adaptability, evolutionary potential and dispersal ability.
Aim Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available.
Location Madagascar.
Methods Models were developed and evaluated for 13 species of secretive leaf‐tailed geckos ( Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km ² grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP).
Results We found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included.
Main conclusions We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species.
Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species’ bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale-dependent context.
Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species’ distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species’ occurrence data. Presence-only data were effective for modelling species’ distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.
Aim We explore the relationship between current European distributions of amphibian and reptile species and observed climate, and project species potential distributions into the future. Potential impacts of climate warming are assessed by quantifying the magnitude and direction of modelled distributional shifts for every species. In particular we ask, first, what proportion of amphibian and reptile species are projected to lose and gain suitable climate space in the future? Secondly, do species projections vary according to taxonomic, spatial or environmental properties? And thirdly, what climate factors might be driving projections of loss or gain in suitable environments for species?
Location Europe.
Methods Distributions of species are modelled with four species–climate envelope techniques (artificial neural networks, generalized linear models, generalized additive models, and classification tree analyses) and distributions are projected into the future using five climate‐change scenarios for 2050. Future projections are made considering two extreme assumptions: species have unlimited dispersal ability and species have no dispersal ability. A novel hybrid approach for combining ensembles of forecasts is then used to group linearly covarying projections into clusters with reduced inter‐model variability.
Results We show that a great proportion of amphibian and reptile species are projected to expand distributions if dispersal is unlimited. This is because warming in the cooler northern ranges of species creates new opportunities for colonization. If species are unable to disperse, then most species are projected to lose range. Loss of suitable climate space for species is projected to occur mainly in the south‐west of Europe, including the Iberian Peninsula, whilst species in the south‐east are projected to gain suitable climate. This is because dry conditions in the south‐west are projected to increase, approaching the levels found in North Africa, where few amphibian species are able to persist.
Main conclusions The impact of increasing temperatures on amphibian and reptile species may be less deleterious than previously postulated; indeed, climate cooling would be more deleterious for the persistence of amphibian and reptile species than warming. The ability of species to cope with climate warming may, however, be offset by projected decreases in the availability of water. This should be particularly true for amphibians. Limited dispersal ability may further increase the vulnerability of amphibians and reptiles to changes in climate.
Climate Change Biology, 2e examines the evolving discipline of human-induced climate change and the resulting shifts in the distributions of species and the timing of biological events. The text focuses on understanding the impacts of human-induced climate change by drawing on multiple lines of evidence, including paleoecology, modeling, and current observation. This revised and updated second edition emphasizes impacts of human adaptation to climate change on nature and greater emphasis on natural processes and cycles and specific elements. With four new chapters, an increased emphasis on tools for critical thinking, and a new glossary and acronym appendix, Climate Change Biology, 2e is the ideal overview of this field. ? Expanded treatment of processes and ? Additional exercises and elements to encourage independent and critical thinking ? Increased on-line supplements including mapping activities and suggested labs and classroom activities.
This tutorial gives a basic introduction to use of the Maxent program for maximum entropy modelling of species’ geographic distributions, written by Steven Phillips, Miro Dudik, and Rob Schapire, with support from AT&T Labs-Research, Princeton University, and the Center for Biodiversity and Conservation, American Museum of Natural History.
Climate Change Biology is a new textbook which examines this emerging discipline of human-induced climate change and the resulting shifts in the distributions of species and the timing of biological events. The text focuses on understanding the impacts of human-induced climate change, but draws on multiple lines of evidence, including paleoecology, modelling and current observation. Climate Change Biology lays out the scope and depth of understanding of this new discipline in terms that are accessible to students, managers and professional biologists.
Two morphologically and ecologically distinct populations of lizards belonging to the Acanthodactylus scutellatus species group (previously treated as conspecifics under the name A. longipes) are documented as occurring in sympatry over a wide geographical area in Egypt. The occurrence of bona fide A. longipes in the Western Desert of Egypt is confirmed; while populations in the eastern part of the country and previously referred to A. longipes, are described as a new species. Kurzfassung Es werden zwei morphologisch und ökologisch unterschiedliche Populationen von Eidechsen aus der Acanthodactylus scutellatus-Artengruppe dokumentiert, die bisher als konspezifisch unter dem Namen A. longipes behandelt wurden. Es wird gezeigt, dass beide Populationen in einem großen Arealbereich symatrisch vorkommen. Die Zugehörigkeit von Tieren aus der Westlichen Wüste Ägyptens zu A. longipes wird bestätigt, während Populationen aus östlichen Landesteilen, die bisher ebenfalls als zu A. longipes gehörend angesehen wurden, als neue Art beschrieben wird.
Knowledge about the distribution of species is limited, with extensive gaps in our knowledge, particularly in tropical areas and in arid environments. Species distribution models offer a potentially very powerful tool for filling these gaps in our knowledge. They relate a set of recorded occurrences of a species to environmental variables thought to be important in determining the distributions of species, in order to predict where species will be found throughout an area of interest. In this thesis, I explore the development, potential applications and possible limitations of distribution models using species from various taxonomic groups in two regions of the world: butterflies, mammals, reptiles and amphibians in Egypt, and butterflies, hoverflies and birds in Great Britain. Specifically I test: 1) which modelling methods produce the best models; 2) which variables correlate best with the distributions of species, and in particular whether interactions among species can explain observed distributions; 3) whether the distributions of some species correlate better with environmental variables than others and whether this variation can be explained by ecological characteristics of the species; 4) whether the same environmental variables that explain species’ occurrence can also explain species richness, and whether distribution models can be combined to produce an accurate model of species richness; 5) whether the apparent accuracy of distribution models is supported by ground-truthing; and 6) whether the models can predict the impact of climate change on the distribution of species. Overall the use of distribution models is supported; my models for species in both Egypt and Britain explained observed occurrence very well. My results shed some light on factors that may be important in determining the distributions of species, particularly on the importance of interactions among species. As they currently stand, distribution models appear unable to predict accurately the impacts of climate change.
Presence-only data abounds in ecology, often accompanied by a background sample. Although many interesting aspects of the species’ distribution can be learned from such data, one cannot learn the overall species occurrence probability, or prevalence, without making unjustified simplifying assumptions. In this forum article we question the approach of Royle et al. (2012) that claims to be able to do this.
The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.
Aim
Models of species niches and distributions have become invaluable to biogeographers over the past decade, yet several outstanding methodological issues remain. Here we address three critical ones: selecting appropriate evaluation data, detecting overfitting, and tuning program settings to approximate optimal model complexity. We integrate solutions to these issues for Maxent models, using the Caribbean spiny pocket mouse, H eteromys anomalus , as an example.
Location
N orth‐western S outh A merica.
Methods
We partitioned data into calibration and evaluation datasets via three variations of k ‐fold cross‐validation: randomly partitioned, geographically structured and masked geographically structured (which restricts background data to regions corresponding to calibration localities). Then, we carried out tuning experiments by varying the level of regularization, which controls model complexity. Finally, we gauged performance by quantifying discriminatory ability and overfitting, as well as via visual inspections of maps of the predictions in geography.
Results
Performance varied among data‐partitioning approaches and among regularization multipliers. The randomly partitioned approach inflated estimates of model performance and the geographically structured approach showed high overfitting. In contrast, the masked geographically structured approach allowed selection of high‐performing models based on all criteria. Discriminatory ability showed a slight peak in performance around the default regularization multiplier. However, regularization levels two to four times higher than the default yielded substantially lower overfitting. Visual inspection of maps of model predictions coincided with the quantitative evaluations.
Main conclusions
Species‐specific tuning of model parameters can improve the performance of Maxent models. Further, accurate estimates of model performance and overfitting depend on using independent evaluation data. These strategies for model evaluation may be useful for other modelling methods as well.
Concerns about the impacts of climate change loom large among biodiversity scientists. A pressing issue is the role of Protected Area networks under future climate change, because of the shifting of species distributions polewards due to climate warming. In this study we use two techniques in conservation science, first, to estimate the likely impacts on the distributions of mammals and butterflies in Egypt (MaxEnt), and second, to measure the effectiveness of Egypt’s Protected Area network (Zonation). We predict that future climate will have significant effects on species richness and the relative value for conservation of sites in Egypt: some areas will increase in species richness, whilst others will decrease significantly. Currently, the sites of highest relative conservation value are found in the Nile Delta, south-eastern and Sinai regions of Egypt and along the Mediterranean and Red Sea coastlines, with Protected Areas having a higher conservation value than unprotected areas. Under future climate scenarios the relative conservation value of Protected Areas are predicted initially to decline and then gradually increase by the 2080s. It is predicted that many areas, especially the Nile Delta and the southeast, will require extra protection in the future; areas that are currently not protected, but have high species richness and conservation value, may need to be protected to prevent loss of biodiversity.
The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models. It avoids the supposed subjectivity in the threshold selection process, when continuous probability derived scores are converted to a binary presence-absence variable, by summarizing overall model performance over all possible thresholds. In this manuscript we review some of the features of this measure and bring into question its reliability as a comparative measure of accuracy between model results. We do not recommend using AUC for five reasons: (1) it ignores the predicted probability values and the goodness-of-fit of the model; (2) it summarises the test performance over regions of the ROC space in which one would rarely operate; (3) it weights omission and commission errors equally; (4) it does not give information about the spatial distribution of model errors; and, most importantly, (5) the total extent to which models are carried out highly influences the rate of well-predicted absences and the AUC scores.
In many cases, the designation of Protected Areas (PAs) is not based on biological information, particularly in tropical regions where such information is generally lacking. Thus it is unclear whether tropical PAs are well-placed for conserving biodiversity currently, or under future climate change. We used reserve-design software (‘Zonation’) to investigate current and future conservation value of PAs of Thailand (N = 187 PAs, covering ∼20% of Thailand) in relation to forest-cover and butterfly diversity. Currently, PAs are about 2 °C cooler than non-PAs because PAs tend to occur at higher elevation (66% of land above 1000 m is protected compared with only 6% below 250 m). Temperature is predicted to increase in Thailand in future, but PAs are predicted to remain ∼2 °C cooler than non-PAs in future. We obtained modelled distribution data for 161 butterfly species (∼12% of national butterfly fauna), and used Zonation to rank areas (∼1 km2 grid resolution) based on species richness, complementarity, and forest cover. The conservation value of PAs was approximately twice that of non-PA areas, although many highly-ranked areas are not currently protected. The species richness of PAs was projected to decline by ∼30% in future, but the relative conservation rankings of individual PAs were projected to change very little. The preponderance of PAs in montane regions makes them well-placed to support forest species shifting from areas at lower elevation that become climatically unsuitable in future. By contrast, the conservation value of low-elevation PAs may decline in future if climate conditions become unsuitable for species.
Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species’ environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species’ occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately.
We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions.
Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors.
Synthesis and applications . To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.
For planning practical measures aimed at biodiversity protection, conservation priorities must be identified at a local scale. Unfortunately, identifying local conservation priorities requires high-resolution data on species distribution, and these are often unavailable. Atlases of species distribution provide data for several groups of organisms in many different areas but are often too coarse in resolution to provide valuable information. We explored the possibility of cross-scale modelling species distributions and we clarified, for the first time, its effect on prioritization exercises. We used different modelling techniques for scaling down atlas data for Sardinian reptiles, validated the outcomes with detailed, field-sampled data, and compared conservation priorities deriving from atlas maps and downscaled models. Doing this, we obtained as a further result the identification of priority species and areas for future conservation strategies. Our results encourage us to experiment further with this approach. Through the downscaling procedure, we obtain high-resolution models with strong variations in predictive performances, although most of the models show satisfactory/excellent scores. This testifies that low-resolution data can be downscaled maintaining low rates of omission and commission errors. Increasing the resolution of distribution maps used for prioritization influences the spatial patterns of priority but does not modify the evaluation of species representation. Overall, we show that atlases can meet the large demand for distribution data by decision makers if appropriate downscaling procedures are adopted. In addition, we provide practical instruments for the conservation of reptiles in Sardinia by identifying priority species and areas that require strict management.
The realized species richness of tropical forests cannot yet be reliably mapped at a regional scale due to lack of systematically collected data. An estimate of the potential species richness (PSR), however, can be produced through the use of species distribution modelling. PSR is interpretable as a climatically determined upper limit to observed species richness. We mapped current PSR and future PSR under climate change scenarios for Mesoamerica by combining the spatial distributions of 2000 tree species as predicted by generalized additive models built from herbaria records and climate layers. An explanatory regression tree was used to extract conditional rules describing the relationship between PSR and climate. The results were summarized by country, ecoregion and protected area status in order to investigate current and possible future variability in PSR in the context of regional biodiversity conservation. Length of the dry season was found to be the key determinant of PSR. Protected areas were found to have higher median PSR values than unprotected areas in most of the countries within the study area. Areas with exceptionally high PSR, however, remain unprotected throughout the region. Neither changes in realized species richness nor extinctions will necessarily follow changes in modelled PSR under climate change. However model output suggests that an increase in temperature of around 3°C, combined with a 20 percent decrease in rainfall could lead to a widespread reduction of around 15 percent of current PSR, with values of up to 40 percent in some moist lower montane tropical forests. The modelled PSR of dry forest ecoregions was found to be relatively stable. Some cooler upper montane forests in northern Mesoamerica, where few species of tropical origin are currently found, may gain PSR if species are free to migrate.
Anthropogenic global climate change has already led to alterations in biodiversity patterns by directly and indirectly affecting species distributions. It has been suggested that poikilothermic animals, including reptiles, will be particularly affected by global change and large-scale reptile declines have already been observed. Currently, half of the world's freshwater turtles and tortoises are considered threatened with extinction, and climate change may exacerbate these declines. In this study, we assess how global chelonian species richness will change in the near future. We use species distribution models developed under current climate conditions for 78% of all extant species and project them onto different Intergovernmental Panel on Climate Change (IPCC) scenarios for 2080. We detect a strong dependence of temperature shaping most species ranges, which coincide with their general temperature-related physiological traits (i.e., temperature-dependent sex determination). Furthermore, the extent and distribution of the current bioclimatic niches of most chelonians may change remarkably in the near future, likely leading to a substantial decrease of local species abundance and ultimately a reduction in species richness. Future climatic changes may cause the ranges of 86% of the species to contract, and of these ranges, nearly 12% are predicted to be situated completely outside their currently realized niches. Hence, the interplay of increasing habitat fragmentation and loss due to climatic stress may result in a serious threat for several chelonian species.
Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.
The effects of climate change and habitat destruction and their interaction are likely to be the greatest challenge to animal and plant conservation in the twenty first century. We used the world's smallest butterfly, the Sinai baton blue (Pseudophilotes sinaicus), as an exemplar of how global warming and human population pressures may act together to cause species extinctions. We mapped the entire global range of this butterfly and obtained extensive data on the intensity of livestock grazing. As with an increasing number of species, it is confined to a network of small habitat patches and is threatened both by indirect human-induced factors (global warming) and by the direct activities of humans (in this case, livestock grazing and collection of medicinal plants). In the absence of global warming, grazing, and plant collection, our model suggested that the butterfly will persist for at least 200 years. Above a threshold intensity of global warming, the chance of extinction accelerated rapidly, implying that there may be an annual average temperature, specific to each endangered species, above which extinction becomes very much more likely. By contrast, there was no such threshold of grazing pressure-the chance of extinction increased steadily with increasing grazing. The impact of grazing, however, decreased with higher levels of year-to-year variation in habitat quality. The effect of global warming did not depend on the future level of grazing, suggesting that the impacts of global warming and grazing are additive. If the areas of habitat patches individually fall below certain prescribed levels, the butterfly is likely to go extinct. Two patches were very important for persistence: if either were lost the species would probably go extinct. Our results have implications for the conservation management of all species whose habitats are at risk because of the direct activities of humans and in the longer term because of climate change.
MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south-west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.
Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.