Solving the Maximum Representation Problem to Prioritize Areas for the Conservation of Terrestrial Mammals at Risk In Oaxaca (original) (raw)
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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.
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.
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.
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 ...
Conservation Science and Practice, 2024
Anthropogenic loss of biodiversity continues to increase worldwide, and existing conservation area networks (CANs) are inadequate for its adequate representation and persistence. To identify a set of new nominal conservation areas in Oaxaca, a Mesoamerican biodiversity hotspot in Mexico, for terrestrial vertebrate species, we used a multi-criteria systematic conservation planning approach. Besides minimizing the area incorporated into the nominal CAN, we incorporated 25 socioeconomic variables using multi-attribute value theory. We constructed a portfolio of nominal CAN solutions for four different scenarios all of which satisfied a 10% representation target for the modeled suitable habitat of each vertebrate species: (1) existing protected area-based (PA) solution; (2) voluntary conservation area-based (VCA) solution; (3) PAVCA solution; and (4) R-C solution (rarity-complementary algorithm). The PA-VCA and PA solutions were the most expensive in terms of area that had to be included in the nominal CANs (13,352 km2 and 12,587 km2, respectively). In all our multi-criteria analyses, highest costs were associated with maximizing the number of airports, amount of tourism, and length of available highways in a nominal CAN. We have thus established a portfolio of multicriteria solutions to the problem of creating an adequate CAN for the representation of terrestrial vertebrate species.
Place prioritization for biodiversity representation using species’ ecological niche modeling
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.
Ecological-Niche Modeling and Prioritization of Conservation-Area Networks for Mexican Herpetofauna.
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Use of population viability analysis and reserve selection algorithms in regional conservation plans
Ecological …, 2003
Current reserve selection algorithms have difficulty evaluating connectivity and other factors necessary to conserve wide-ranging species in developing landscapes. Conversely, population viability analyses may incorporate detailed demographic data, but often lack sufficient spatial detail or are limited to too few taxa to be relevant to regional conservation plans. We developed a regional conservation plan for mammalian carnivores in the Rocky Mountain region using both a reserve selection algorithm (SITES) and a spatially explicit population model (PATCH). The spatially explicit population model informed reserve selection and network design by producing data on the locations of population sources, the degree of threat to those areas from landscape change, the existence of thresholds to population viability as the size of the reserve network increased, and the effect of linkage areas on population persistence. A 15% regional decline in carrying capacity for large carnivores was predicted within 25 years if no addition to protected areas occurred. Increasing the percentage of the region in reserves from the current 17.2% to 36.4% would result in a 1-4% increase over current carrying capacity, despite the effects of landscape change. The population model identified linkage areas that were not chosen by the reserve selection algorithm, but whose protection strongly affected population viability. A reserve network based on carnivore conservation goals incidentally protected 76% of ecosystem types, but was poor at capturing localized rare species. Although it is unlikely that planning for focal species requirements alone will capture all facets of biodiversity, when used in combination with other planning foci, it may help to forestall the effects of loss of connectivity on a larger group of threatened species and ecosystems. A better integration of current reserve selection tools and spatial simulation models should produce reserve designs that are simultaneously biologically realistic and taxonomically inclusive.