Species Potential Distribution

Potential distribution and landscape connectivity: criteria for reevaluating the threat degree of Campylorhynchus yucatanicus (Aves: Troglodytidae). Geographic distribution and habitat quality are key criteria for assessing the degree of risk of species extinction threat. Campylorhynchus yucatanicus (Yucatán Wren, Troglodytidae) is an endemic bird of the Northern coast of the Yucatán Peninsula, Mexico, with a distribution restricted to a narrow strip of habitat, between Campeche and Yucatán states. Currently, the Yucatán coast has lost more than half of the coastal dune vegetation, and other habitats have been modified mainly because there is no urban development plan and the natural resources management is poor. These factors threaten C. yucatanicus, which is listed as a near threatened species by IUCN and as an endangered species by Mexican law NOM-059-2010. In this paper, C. yucatanicus´s potential distribution was modeled using 64 presence records from several sources (1960 y 2009), a set of climate variables, and a vegetation index layer of normalized difference (NDVI). To assess the degree of landscape connectivity we used a map of vegetation types and land use, distance to villages and paved roads. The potential distribution model showed an area of approximately 2 711 km 2 , which is 2 % of the total area of the Yucatán Peninsula distribution. In this area, only 27 % is protected by Biosphere Reserve category and only 10 % belong to core conservation areas, with land use restrictions and relatively effective protection. The populations from Ría Lagartos and Western Celestún regions appear to be the most isolated following the model of landscape connectivity. Landscape permeability among fragments of dune vegetation near the coast is low, mainly due to the distribution of urban areas. These results can be used to establish management strategies, and show that the species is in more delicate conditions than what it has been described by IUCN. We consider that C. yucatanicus should be given endangered category by IUCN, because of their distribution and the context of the current landscape connectivity.

The historic and contemporary distribution for the Psittacidae family in Mexico using the greatest database assembled until now was estimated. Theecological niche models were generated with the MaxEnt algorithm. Temperature and precipitation were used, as well as an analysis of vegetationand availability of protected areas established until 2015. The models did not present omission errors and allow to have updated estimates for eachspecies. The species with the greatest potential distribution was Amazona albifrons, and Amazona auropalliata had the smallest one. The results indicate that all the species have lost suitable habitat area, especially Ara macao, Amazona auropalliata and A. oratrix. Although tropical forestsare present on most species distributions, temperate forests had a high cover percentage for 6 species. The proportion of protected area for eachspecies was higher when compared with previous estimates from the year 2000. The precise estimate of the distribution of Mexican Psittacidae hasinternational conservation implications, as 6 species are endemic and 4 species may present their greater potential distribution in Mexico accordingto IUCN information. Due to habitat loss some species have a high vulnerability, present and future, making it necessary to examine the relationshipbetween environmental suitability and patterns of population abundance. The use of ecological niche models to evaluate distributional changesassociated with climate change may be also necessary.

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 State of Guerrero is ranked fourth in terms of biodiversity in Mexico, including 930 vertebrate species in its territory. However, Guerrero shows significant loss of its original natural habitats. It is evident the need to generate information on the processes involved in loss of natural habitats and to identify the impact on the distribution of species. We modeled the potential distribution of suitable habitat for 47 species of mammals using MaxEnt, and those were further refined to produce models of the current distribution of suitable habitats. The relationship between the amplitude of the potential distribution of suitable habitats and the proportion of remaining natural habitat for each species were examined, both at the state (Guerrero) and nationwide levels (Mexico), and there were identified areas of Guerrero needed to achieve fixed conservation goals. The results showed no significant correlation between the amplitude of species potential distribution of suitable habitats and species distributions of remaining areas of original habitat, neither at the state or national scales. In fact, there are significant differences in the proportions of remaining habitat between the 2 scales, suggesting that scale is an important factor in establishing conservation strategies for the same species at local, regional or national scales.

The margay (Leopardus wiedii) is a small felid endangered mainly by habitat loss. Natural protected areas (NPA) are important for margay conservation due to the felid’s preference for native vegetation with dense coverage. Our objective was to generate a potential distribution model for L. wiedii in México to identify NPA devoid of georeferenced records of margay and with suitable environmental conditions for its presence. We obtained 97 records with unique geographic coordinates from 1944 to 2015, to which a rarefaction analysis was done. We used MaxEnt version 3.4.0 to generate the potential distribution model (AUC = 0.8574) from 66 records without spatial nor environmental correlation and 8 climatic variables. The model was overlaid on digital maps of NPA and terrestrial ecoregions of México. Ecological niche modeling predicted high climatic suitability for margay’s presence in 17 NPA that did not present records with geographic coordinates. The model also concurred in 16 NPA with georeferenced records from which only 2 NPA presented high climatic suitability. The potential distribution model is an interpretation of the margay’s contemporary geographic distribution and may be applied as a guide to verify the species’ presence in the NPA.

Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high-resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high-resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910–2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin-plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26–1.12 °C and 24.67% for monthly precipitation, and a RTMSE of 0.48–0.56 °C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high-resolution climatology for the country.