Miguel A. Ortega-Huerta

Predicting distributions of known and unknown reptile species in Madagascar

Despite the importance of tropical biodiversity, informative species distributional data are seldom available for biogeographical study or setting conservation priorities. Modelling ecological niche distributions of species offers a potential soluion; however, the utility of old locality data from museums, and of more recent remotely sensed satellite data, remains poorly explored, especially for rapidly changing tropical landscapes. Using 29 modern data sets of environmental land coverage and 621 chameleon occurrence localities from Madagascar (historical and recent), here we demonstrate a significant ability of our niche models in predicting species distribution. At 11 recently inventoried sites, highest predictive success (85.1%) was obtained for models based only on modern occurrence data (74.7% and 82.8% predictive success, respectively, for pre-1978 and all data combined). Notably, these models also identified three intersecting areas of over-prediction that recently yielded seven chameleon species new to science. We conclude that ecological niche modelling using recent locality records and readily available environmental coverage data provides informative biogeographical data for poorly known tropical landscapes, and offers innovative potential for the discovery of unknown distributional areas and unknown species.

Biodiversity Conservation: Uncertainty in predictions of extinction risk/Effects of changes in climate and land use/Climate change and extinction risk (reply).

Thomas et al. reply — We reconsider our estimates of climate-related extinction in the light of three questions raised by Thuiller et al., Buckley and Roughgarden and Harte et al.. We are able to confirm our original conclusion that climate change represents a major threat to terrestrial species.

Potential distributional changes and conservation priorities of endemic amphibians in western Mexico as a result of climate change

There is a growing concern regarding the conservation status of amphibian species worldwide; they are more threatened and declining more rapidly than mammals or birds, and Mexico is considered one of the richest countries on Earth in terms of reptile and amphibian species. Composite models of the current distribution patterns of endemic amphibians in western Mexico were used to predict their potential distributional changes as a consequence of expected climatic changes. The models identified the most significant conservation areas within the region (hotspots), considering existing natural protected areas (NPAs) and previously recognized terrestrial priority regions for conservation (TPRCs). Three niche modelling algorithms (Bioclim, GARP and MaxEnt) used 2412 locality records for 29 species to model their climate envelopes under current and future conditions for the years 2020, 2050 and 2080. The models indicated that overall species persistence was 60% for the years 2020 and 2050, but dropped to

Mapping coffee plantations with Landsat imagery: an example from El Salvador

Considering the potential of shaded coffee plantations mixed with natural vegetation for promoting biodiversity conservation, this project assessed the utility of multi-date Landsat Thematic Mapper (TM) satellite imagery for the characterization of natural vegetation versus coffee plantations in western El Salvador. For assembling a multi-temporal Landsat TM data set, we applied a regression analysis model to remove cloud cover and cloud shadows. Then, through a hybrid classification approach, a nine-class land use/land cover (LULC) map was generated. We identified two types of coffee plantations (‘open-canopy’ and ‘close-canopy’) along with natural forest/shrubland, mangrove, water bodies, sandy coastal soils, bare soil, urban areas and agriculture. Notwithstanding the small sample size of the accuracy data, our assessment revealed an overall accuracy of 76.7% (Kappa coefficient = 0.68), considering only the four classes with independent field data. The overall classification accuracy for distinguishing coffee plantations from non-mangrove natural forest was 81.6% and the classification accuracy for distinguishing ‘open-canopy’ from ‘close-canopy’ coffee plantations was 85.7%. We are encouraged by the results of this prototype study. They indicate that remote-sensing techniques can be used to distinguish different classes of coffee production systems and to differentiate coffee from natural forest.

A faunal study of Cerambycidae (Coleoptera) from one region with tropical dry forest in Mexico: Sierra de San Javier, Sonora

Abstract We present the results of a faunal study of cerambycids from San Javier, Sonora, a locality in México with tropical dry forest. The study was carried out between November, 2003 and October, 2004. The collections were carried out during five days of every month and the collection methods included light trapping, Malaise trapping and direct collecting. Vegetational phenology was related to seasonal variations in species richness by using MODIS-NDVI data. A total of 82 species, 62 genera, 27 tribes and 3 subfamilies were recorded. The subfamily with the greatest number of species was Cerambycinae with 52, followed by Lamiinae with 27, and Prioninae with three. The tribes with the largest number of genera and species were Elaphidiini with 12 and 20, Trachyderini with 9 and 9 and Acanthocinini with eight and 12. The genera with the most species were Anelaphus Linsley with four and Anopliomorpha Linsley, Neocompsa Martins and Lepturges Bates with three. The richness value using the non-parametric estimator ICE was 121 species. The species abundance pattern showed few very abundant species and many with few individuals. The diversity value calculated with the Shannon Index over the entire year was 3.35. Two main patterns are revealed in the temporal relationship between vegetation phenology and species richness: (1) a direct relationship occurring at the end of the dry season and during the first half of the greening up of vegetation, and (2) an inverse relationship which starts in the second half of the dry season. The fauna was more similar to the fauna of Chamela, Jalisco than to Sierra de Huautla, Morelos, San Buenaventura, Jalisco or El Aguacero, Chiapas and consists of 18% species endemic to Mexico.

Modelling the potential winter distribution of the endangered Black-capped Vireo ( Vireo atricapilla )

We applied the ecological niche/habitat modelling approach to predict the potential winter distribution of the endangered Black-capped Vireo Vireo atricapilla. We used historical and current field records along with climatic and topographic variables to generate three different models (Biomapper, Maxent, and GARP). Using field data on species occurrence, a model was selected based on the accuracy of assessment results. A final model was obtained by eliminating those areas mapped as known unsuitable habitat, using high resolution land use/land cover data. The GARP model obtained the best accuracy values. It showed the winter distribution of the Black-capped Vireo to cover an area in western Mexico of about 141,000 km that runs along the Pacific coast from southern Sonora (Rı́o Yaqui, Alvaro Obregón Dam) to the southern state of Oaxaca (Salina Cruz on the Pacific coast and Matias Romero, and inland). One third of the proposed model’s area was located at elevations of 0–500 m, while 83% occurred at elevations , 1,250 m; however, a significant area (17%) consists of sites . 1,250 m in elevation. For the most part, the distribution model proposed closely followed the tropical dry forest boundaries and clearly avoided temperate areas at higher elevations. This situation seems to be critical for the species, since the dry forest is one of most endangered Neotropical ecosystems, both nationally and internationally. Furthermore, the array of areas under protection regimes included only about 7.1% of the predicted wintering area. However, this figure could be misleading when it is considered that some protected areas are just ‘‘paper reserves’’ without significant conservation programmes developed in situ.

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the countrys land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computers screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.