Place prioritization for biodiversity representation using species’ ecological niche modeling (original) (raw)
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Modelling spatial patterns of biodiversity for conservation prioritization in North���eastern Mexico
2004
Relationships between spatial patterns of bird and mammal species richness in northeastern Mexico were analysed in relation to the location of three biosphere reserves (El Abra-Tanchipa, El Cielo, and Sierra Gorda) and 13 priority areas recently identified for conservation. Ecological niches were modelled and potential distributions delimited for 285 bird and 114 mammal species using a genetic algorithm based on locality information from museum specimens and 15 selected environmental attributes. Potential distributions were transformed into hypothesized current distributions based on species-habitat associations as reflected in a recent land-use map. Although species richness was lower when distributions were reduced from potential to current, spatial patterns of potential and current richness were similar. Heuristic, complementarity-based prioritization procedures were used to identify combinations of areas and sites with maximal species representation: the biosphere reserves included 79% of birds and 74% of mammal species; eight priority areas provided an additional 11% of birds and 13% of mammals; the remaining 10% of birds and 13% of mammals were concentrated in new sites across the study area.
Conservation Science and Practice, 2019
Systematic conservation planning (SCP) identifies priority areas for biodiversity conservation using surrogates for adequate representation of biodiversity content. The use of multiple well‐known taxonomic groups rather than single ones as a surrogate set is expected to enable a better representation of most important biodiversity constituents in prioritizing conservation areas. We quantitatively analyzed if single‐ or multitaxa groups of terrestrial vertebrates serve best as surrogates for representing biodiversity constituents in the state of Oaxaca, a biodiversity hotspot in southern Mexico. We produced species distribution models for 1,063 terrestrial vertebrate species using a maximum entropy algorithm. To determine which fraction of each terrestrial vertebrate group best represented the remaining groups, we produced solutions requiring different proportions of species to be represented with a 10% target of the species' potential distribution in conservation area networks. Precedence to geographical rarity, minimizing area, and enhancing compactness in shape of the selected priority areas was established using ConsNet. We further evaluated performance with surrogacy graphs for determining the representation of each group of terrestrial vertebrates treated as biodiversity constituents. Inclusion of multiple terrestrial vertebrate groups as surrogates performed best for the representation of biodiversity constituents compared to a single terrestrial vertebrate group, in all solutions with different proportions of species in these conservation areas. Terrestrial vertebrate species were poorly represented in the few protected areas (<2% of species), but representation increased significantly (99% of species) when complemented with other established conservation initiatives. SCP should include multitaxa surrogate sets and all established conservation initiatives to ensure priority areas for conservation with an adequate biodiversity representation.
Diversity and Distributions, 2008
Oaxaca, located in south-west México within the Mesoamerican biodiversity hotspot, holds exceptionally high biodiversity for several taxa, including mammals. It has four decreed natural protected areas (NPAs) covering 5% of its total area, but only three of these, covering only 0.2% of the area, are strictly protected as National Parks. The current study develops ecological niche models for 183 terrestrial mammals for use as biodiversity surrogates in a systematic conservation planning exercise. Forty-five of these species were selected on the basis of their being either endangered or threatened or otherwise listed under the Mexican Red List or because they were endemic to either Oaxaca or to Mexico. The niche models were constructed with a machine-learning algorithm (GARP, Genetic Algorithm for Rule-Set Prediction) and refined by restricting each model to sites with suitable vegetation and habitat patches contiguous with known occurrences of the species. If the entire predicted geographical distribution of each of the 45 species listed above is put under protection, the entire state of Oaxaca gets included. Therefore, we imposed different constraints on the maximum area that can be put under protection (5–30% of the area of Oaxaca) and selected nominal conservation area networks based on different percentage representation targets for the species’ modelled distributions based on their conservation status (10–100%). The area selection utilized a rarity- and complementarity-based algorithm (in the ResNet software package). The goal was to have as many as possible of the 45 species at risk meet their specified representation targets in the budgeted area. The methods developed here combine ecological niche modelling and area prioritization algorithms for integrated conservation planning in a protocol that is suitable for other highly biodiverse regions.
Diversity and …, 2008
Oaxaca, located in south-west México within the Mesoamerican biodiversity hotspot, holds exceptionally high biodiversity for several taxa, including mammals. It has four decreed natural protected areas (NPAs) covering 5% of its total area, but only three of these, covering only 0.2% of the area, are strictly protected as National Parks. The current study develops ecological niche models for 183 terrestrial mammals for use as biodiversity surrogates in a systematic conservation planning exercise. Forty-five of these species were selected on the basis of their being either endangered or threatened or otherwise listed under the Mexican Red List or because they were endemic to either Oaxaca or to Mexico. The niche models were constructed with a machine-learning algorithm (GARP, Genetic Algorithm for Rule-Set Prediction) and refined by restricting each model to sites with suitable vegetation and habitat patches contiguous with known occurrences of the species. If the entire predicted geographical distribution of each of the 45 species listed above is put under protection, the entire state of Oaxaca gets included. Therefore, we imposed different constraints on the maximum area that can be put under protection (5 -30% of the area of Oaxaca) and selected nominal conservation area networks based on different percentage representation targets for the species' modelled distributions based on their conservation status (10 -100%). The area selection utilized a rarityand complementarity-based algorithm (in the ResNet software package). The goal was to have as many as possible of the 45 species at risk meet their specified representation targets in the budgeted area. The methods developed here combine ecological niche modelling and area prioritization algorithms for integrated conservation planning in a protocol that is suitable for other highly biodiverse regions.
Identifying conservation priorities in Mexico through geographic information systems and modeling
Ecological …, 1995
Environmental assessments of regional development projects have been used in Mexico to determine where conflicts between conservation of biodiversity and resource extraction are likely to occur. Species-rich areas have been acknowledged as a priority for conservation. However, biological information is incomplete and biased toward accessible sites, so species-rich areas cannot be depicted directly from current biological knowledge.
El modelado de la distribución de especies y la conservación de la diversidad biológica
Place prioritization for biodiversity representation is essential for conservation planning, particularly in megadiverse countries where high deforestation threatens biodiversity. Given the collecting biases and uneven sampling of biological inventories, there is a need to develop robust models of species' distributions. By modeling species' ecological niches using point occurrence data and digitized environmental feature maps, we can predict potential and extant distributions of species in untransformed landscapes, as well as in those transformed by vegetation change (including deforestation). Such distributional predictions provide a framework for use of species as biodiversity surrogates in place prioritization procedures such as those based on rarity and complementarity. Beyond biodiversity conservation, these predictions can also be used for place prioritization for ecological restoration under current conditions and under future scenarios of habitat change (e.g., deforestation) scenarios. To illustrate these points, we (1) predict distributions under current and future deforestation scenarios for the Mexican endemic mammal Dipodomys phillipsii, and show how areas for restoration may be selected; and (2) propose conservation areas by combining nonvolant mammal distributional predictions as biodiversity surrogates with place prioritization procedures, to connect decreed natural protected areas in a region holding exceptional biodiversity: the Transvolcanic Belt in central Mexico.
Biodiversity and Conservation, 2010
Ecological niche modeling (ENM) has become an important tool in conservation biology. Despite its recent success, several basic issues related to algorithm performance are still being debated. We assess the ability of two of the most popular algorithms, GARP and Maxent, to predict distributions when sampling is geographically biased. We use an extensive data set collected in the Brazilian Cerrado, a biodiversity hotspot in South America. We found that both algorithms give richness predictions that are very similar to other traditionally used richness estimators. Also, both algorithms correctly predicted the presence of most species collected during fieldwork, and failed to predict species collected only in very few cases (usually species with very few known localities, i.e., <5). We also found that Maxent tends to be more sensitive to sampling bias than GARP. However, Maxent performs better when sampling is poor (e.g., low number of data points). Our results indicates that ENM, even when provided with limited and geographically biased localities, is a very useful technique to estimate richness and composition of unsampled areas. We conclude that data generated by ENM maximize the utility of existing biodiversity data, providing a very useful first evaluation. However, for reliable conservation decisions ENM data must be followed by well-designed field inventories, especially for the detection of restricted range, rare species.
We used ecological niche modelling projected as species' potential (based on the original vegetation map) and extant (based on the 2000 land use and vegetation map) distributions to analyse changes on patterns of endemism of terrestrial mammals occurring in Mexico. Based on the biogeographic method of Parsimony Analysis of Endemicity, we obtained cladograms under scenarios of species' potential distribution (t1) and extant distributions (t2). We found that the resolution of consensus cladogram in t2 was poorer, while there were more geographic synapomorphies in t1, and more autapomorphies in t2 due to a reduction of species' distributions as a consequence of deforestation. We defined a hierarchical regionalization with two regions with the cladogram of t1; a transitional zone, two subregions, five dominions, and 15 provinces. Conversely, the consensus cladogram of t2 had a basal trichotomy, and the position of the Sierra Madre Occidental changed compared with t1. In t1 and t2, the Yucatán Peninsula+Chiapas+Isthmus of Tehuantepec clade was maintained, although in t2 it was separated from the remaining areas of the country. The impact of deforestation on species distributions strongly affected the biogeographic regionalization of terrestrial mammals in Mexico.
Ecological niche modeling in practice: flagship species and regional conservation planning
Oecologia Australis
Conservation of rare or endemic species is a multifaceted matter, especially whenever knowledge gaps in species’ distribution and anthropogenic pressures converge. We combined Geographic Information Systems and ecological niche modeling tools with field data to characterize the habitat types used for different behavioral activities and to identify important areas for conservation of a charismatic bird endemic to northeast South America, the Guianan cock-of-the-rock (Rupicola rupicola). Using species’ occurrences and climatic, topographic, and remotely sensed vegetation variables we developed potential distribution models at two scales: (1) broad geographic scale (northern South America), based on georeferenced occurrences obtained from literature and natural history museum specimens, and (2) local scale, based on precise occurrences (GPS coordinates) recorded in the field (Caverna do Maroaga Protected Area, Amazonas, Brazil). We identified six priority areas for the conservation of ...
Predicting distributions of Mexican mammals using ecological niche modeling
Journal of …, 2004
Given the uneven and biased nature of present understanding of geographic distributions of mammal species, tools for extrapolating from what is known to a more general prediction would be most useful. We used the genetic algorithm for rule-set prediction (GARP) to generate ecological niche models that were then projected onto geography to predict potential geographic distributions for 17 mammal species of Insectivora, Chiroptera, Rodentia, and Artiodactyla in Oaxaca, Mexico. GARP depends on point occurrence localities from museum records of species, along with electronic maps describing features of climate, topography, and vegetation type. Point localities were divided in 2 sets: one of localities from museum records dated before 1960, which was used to generate the predicted distributions, and the other of localities of museum records resulting from recent inventories (post-1960), which was used to test model accuracy. Predicted distributions for 11 of 17 species were statistically significantly more coincident with independent test points than random expectations; tests for the remaining 6 species would have required larger numbers of test localities to establish significance. GARP is a robust tool for modeling species' geographic distributions, with excellent potential for applicability to strategies for conservation of mammals in Oaxaca and elsewhere.