Patricia Illoldi-Rangel

Identification of areas of endemism from species distribution models: threshold selection and Nearctic mammals

We evaluated the relevance of threshold selection in species distribution models on the delimitation of areas of endemism, using as case study the North American mammals. We modeled 40 species of endemic mammals of the Nearctic region with Maxent, and transformed these models to binary maps using four different thresholds: minimum training presence, tenth percentile training presence, equal training sensitivity and specificity, and 0.5 logistic probability. We analyzed the binary maps with the optimality method in order to identify areas of endemism and compare our results regarding previous analyses. The majority of the species tend to have very low values for the minimum training presence, whereas most of the species have a value of the tenth percentile training presence around 0.5, and the equal training sensitivity and specificity was around 0.3. Only with the tenth percentile threshold we recovered three out of the four patterns of endemism identified in North America, and detected more endemic species.The best identification of areas of endemism was obtained using the tenth percentile training presence threshold, which seems to recover better the distributional area of the mammals analyzed.

Species distribution models and ecological suitability analysis for potential tick vectors of lyme disease in Mexico.

Species distribution models were constructed for ten Ixodes species and Amblyomma cajennense for a region including Mexico and Texas. The model was based on a maximum entropy algorithm that used environmental layers to predict the relative probability of presence for each taxon. For Mexico, species geographic ranges were predicted by restricting the models to cells which have a higher probability than the lowest probability of the cells in which a presence record was located. There was spatial nonconcordance between the distributions of Amblyomma cajennense and the Ixodes group with the former restricted to lowlands and mainly the eastern coast of Mexico and the latter to montane regions with lower temperature. The risk of Lyme disease is, therefore, mainly present in the highlands where some Ixodes species are known vectors; if Amblyomma cajennense turns out to be a competent vector, the area of risk also extends to the lowlands and the east coast.

Ecological niche models and patterns of richness and endemism of the southern Andean genus Eurymetopum (Coleoptera, Cleridae)

Eurymetopum is an Andean clerid genus with 22 species. We modeled the ecological niches of 19 species with Maxent and used them as potential distributional maps to identify patterns of richness and endemicity. All modeled species maps were overlapped in a single map in order to determine richness. We performed an optimality analysis with NDM/VNDM in a grid of 1o latitude-longitude in order to identify endemism. We found a highly rich area, located between 32o and 41o south latitude, where the richest pixels have 16 species. One area of endemism was identified, located in the Maule and Valdivian Forest biogeographic provinces, which extends also to the Santiago province of the Central Chilean subregion, and contains four endemic species (E. parallelum, E. prasinum, E. proteus, and E. viride), as well as 16 non-endemic species. The sympatry of these phylogenetically unrelated species might indicate ancient vicariance processes, followed by episodes of dispersal. Based on our results, we suggest a close relationship between these provinces, with the Maule representing a complex area.

NEW RECORD OF THE ROCK SQUIRREL (SPERMOPHILUS VARIEGATUS) IN THE STATE OF OAXACA, MEXICO

Abstract We collected 3 specimens of the rock squirrel (Spermophilus variegatus) in San Francisco Cotahuixtla, Oaxaca, Mexico, which represent the first record of this species for this state. The new record expands the distributional range southward by 85 km.

Opuntia in México: Identifying Priority Areas for Conserving Biodiversity in a Multi-Use Landscape

Background México is one of the worlds centers of species diversity (richness) for Opuntia cacti. Yet, in spite of their economic and ecological importance, Opuntia species remain poorly studied and protected in México. Many of the species are sparsely but widely distributed across the landscape and are subject to a variety of human uses, so devising implementable conservation plans for them presents formidable difficulties. Multi–criteria analysis can be used to design a spatially coherent conservation area network while permitting sustainable human usage. Methods and Findings Species distribution models were created for 60 Opuntia species using MaxEnt. Targets of representation within conservation area networks were assigned at 100% for the geographically rarest species and 10% for the most common ones. Three different conservation plans were developed to represent the species within these networks using total area, shape, and connectivity as relevant criteria. Multi–criteria analysis and a metaheuristic adaptive tabu search algorithm were used to search for optimal solutions. The plans were built on the existing protected areas of México and prioritized additional areas for management for the persistence of Opuntia species. All plans required around one–third of Méxicos total area to be prioritized for attention for Opuntia conservation, underscoring the implausibility of Opuntia conservation through traditional land reservation. Tabu search turned out to be both computationally tractable and easily implementable for search problems of this kind. Conclusions Opuntia conservation in México require the management of large areas of land for multiple uses. The multi-criteria analyses identified priority areas and organized them in large contiguous blocks that can be effectively managed. A high level of connectivity was established among the prioritized areas resulting in the enhancement of possible modes of plant dispersal as well as only a small number of blocks that would be recommended for conservation management.