Distinguishing the distributions of two cryptic frogs (Anura: Discoglossidae) using molecular data and environmental modeling

Currently, the identification of two cryptic Iberian amphibians, Discoglossus galganoi Capula, Nascetti, Lanza, Bullini and Crespo, 1985 and Discoglossus jeanneae Busack, 1986, relies on molecular characterization. To provide a means to discern the distributions of these species, we used 385-base-pair sequences of the cytochrome b gene to identify 54 Spanish populations of Discoglossus. These data and a series of environmental variables were used to build up a logistic regression model capable of probabilistically designating a specimen of Discoglossus found in any Universal Transverse Mercator (UTM) grid cell of 10 km × 10 km to one of the two species. Western longitudes, wide river basins, and semipermeable (mainly siliceous) and sandstone substrates favored the presence of D. galganoi, while eastern longitudes, mountainous areas, severe floodings, and impermeable (mainly clay) or basic (limestone and gypsum) substrates favored D. jeanneae. Fifteen percent of the UTM cells were predicted to be shared by both species, whereas 51% were clearly in favor of D. galganoi and 34% were in favor of D. jeanneae, considering odds of 4:1. These results suggest that these two species have parapatric distributions and allow for preliminary identification of potential secondary contact areas. The method applied here can be generalized and used for other geographic problems posed by cryptic species.

Modelling the distribution of Bonelli's eagle in Spain: Implications for conservation planning

Bonelli’s eagle, Hieraaetus fasciatus , has recently suffered a severe population decline and is currently endangered. Spain supports about 70% of the European population. We used stepwise logistic regression on a set of environmental, spatial and human variables to model Bonelli’s eagle distribution in the 5167 UTM 10 × 10 km quadrats of peninsular Spain. We obtained a model based on 16 variables, which allowed us to identify favourable and unfavourable areas for this species in Spain, as well as intermediate favourability areas. We assessed the stepwise progression of the model by comparing the model’s predictions in each step with those of the final model, and selected a parsimonious explanatory model based on three variables — slope, July temperature and precipitation — comprising 76% of the predictive capacity of the

Otter (Lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the Iberian Peninsula

We used the results of the Spanish Otter Survey of 1994–1996, a Geographic Information System and stepwise multiple logistic regression to model otter presence/absence data in the continental Spanish UTM 10 10-km squares. Geographic situation, indicators of human activity such as highways and major urban centers, and environmental variables related with productivity, water availability, altitude, and environmental energy were included in a logistic model that correctly classified about 73% of otter presences and absences. We extrapolated the model to the adjacent territory of Portugal, and increased the model’s spatial resolution by extrapolating it to 1 1-km squares in the whole Iberian Peninsula. The model turned out to be rather flexible, predicting, for instance, the species to be very restricted to the courses of rivers in some areas, and more widespread in others. This allowed us to determine areas where otter populations may be more vulnerable to habitat changes or harmful human interventions. # 2003 Elsevier Ltd. All rights reserved.

How well can models predict changes in species distributions? A 13-year-old otter model revisited

Species distribution and ecological niche models are increasingly used in biodiversity management and conservation. However, one thing that is important but rarely done is to follow up on the predictive performance of these models over time, to check if their predictions are fulfilled and maintain accuracy, or if they apply only to the set in which they were produced. In 2003, a distribution model of the Eurasian otter (Lutra lutra) in Spain was published, based on the results of a country-wide otter survey published in 1998. This model was built with logistic regression of otter presence-absence in UTM 10 km2 cells on a diverse set of environmental, human and spatial variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent otter survey, carried out a decade later and after a significant expansion of the otter distribution area in this country. Despite the time elapsed and the evident changes in this species’ distribution, the model maintained a good predictive capacity, considering both discrimination and calibration measures. Otter distribution did not expand randomly or simply towards vicinity areas,m but specifically towards the areas predicted as most favourable by the model based on data from 10 years before. This corroborates the utility of predictive distribution models, at least in the medium term and when they are made with robust methods and relevant predictor variables.

Falar com sotaque

Bonelli’s eagle, Hieraaetus fasciatus , has recently suffered a severe population decline and is currently endangered. Spain supports about 70% of the European population. We used stepwise logistic regression on a set of environmental, spatial and human variables to model Bonelli’s eagle distribution in the 5167 UTM 10 × 10 km quadrats of peninsular Spain. We obtained a model based on 16 variables, which allowed us to identify favourable and unfavourable areas for this species in Spain, as well as intermediate favourability areas. We assessed the stepwise progression of the model by comparing the model’s predictions in each step with those of the final model, and selected a parsimonious explanatory model based on three variables — slope, July temperature and precipitation — comprising 76% of the predictive capacity of the

Spatial variability of satured soil hydraulic conductivity in the region of Araguaia River - Brazil.

This study evaluates the spatial variability of saturated hydraulic conductivity in the soil in an area of 51,850 ha at the headwaters of the Araguaia River MT/GO. This area is highly vulnerable because it is a location of recharging through natural water infiltration of the Guarani Aquifer System and an area of intense increases in agriculture since its adoption by growers in the last 30 years. Soil samples were collected at 383 points, geographically located by GPS. The samples were collected from depths of 0 - 20 cm and 60 - 80 cm. Exploratory statistics and box-plot were used in the descriptive analysis and semivariogram were constructed to determine the spatial model. The exploratory analysis showed that the mean hydraulic conductivity in the superficial layer was less than at the level of 60-80 cm; however, the greatest variability evaluated with a coefficient of variation also was from this layer. Data tended towards a normal distribution. These results can be explained by the greater soil compaction in the superficial layer. The semivariogram models, adjusted for the two layers, were exponential and demonstrated moderate and strong dependence, with ranges of 5000 and 3000 utm respectively. It was concluded that soil use is influencing the spatial distribution model of the hydraulic conductivity in the region.

Caracterização ambiental da microbacia do Córrego Espraiado.

A caracterização ambiental da microbacia estudada foi realizada com o objetivo de criar um conjunto de planos de informação (PI's), de modo a permitir um conhecimento apropriado do local pela equipe do projeto, o planejamento de atividades de pesquisa e a identificação de áreas de risco. A microbacia do Córrego Espraiado está localizada entre os municípios de Ribeirão Preto e Cravinhos. As informações básicas utilizadas na caracterização ambiental foram: I) conjunto de cartas planialtimétricas, escala 1:10.000 (IGC, 1992); II) mapa de solos, escala 1:25.000 (MIKLOS, 1996); III) imagem de satélite LANDSAT TM 5, passagem 01/09/93, bandas 3, 4 e 5. A partir destas informações foram gerados os planos: limite da microbacia, redes de drenagem e viária, modelo numérico de terreno (MNT), classes de declive, uso da terra (1995) e solos. Outros planos foram gerados a partir do cruzamento dos anteriores: potencial de infiltração e escoamento superficial da água, potencial natural de erosão, perdas de solo e expectativa de erosão. A versão do IDRISI utilizada foi a 4.1 (DOS). Para a entrada de dados vetoriais foi utilizado o TOSCA 2.12. Na geração dos mapas foi utilizado o COREL DRAW 4. Os planos foram exportados do IDRISI para o COREL DRAW no formato TIF. O limite da microbacia foi traçado sobre as cartas planialtimétricas e posteriormente digitalizado. Com este PI foi construída uma máscara, utilizada para extrair as células que não pertenciam à microbacia em vários procedimentos posteriores. As redes de drenagem e viária foram digitalizadas a partir das informações contidas nas cartas planialtimétricas e rasterizadas pelo módulo LINERAS, gerando os PI's correspondentes. As informações de altimetria passaram por processo semelhante ao anterior, porém na rasterização foi também usado o módulo POINTRAS. Posteriormente as informações de altimetria, já rasterizadas foram interpoladas de modo a preencher todas as células (módulo INTERCON), gerando o MNT da microbacia. Para eliminar os defeitos nas bordas da microbacia, foram também digitalizadas algumas informações de altitude fora do limite da microbacia. Finalmente, para excluir os resultados da interpolação, fora da área de interesse, foi utilizada a máscara realizando-se uma multiplicação entre planos, função presente no módulo OVERLAY. As classes de declive foram obtidas após o calculo da declividade por meio de função presente no módulo SURFECE e o MNT, resultando em um PI intermediário com os valores de declividade em cada célula. Então, utilizou-se o módulo RECLASS para agrupar as células em 7 classes. A imagem em "falsa cor" (composição colorida) foi obtida usando-se o módulo COMPOSIT e posteriormente registrada por meio do módulo RESAMPLE. As coordenadas UTM dos pontos de controle foram extraídos das cartas planialtimetricas. O PI-Uso Atual, foi obtido das informações também retiradas das cartas planialtimetricas, análise da imagem de satélite e de visitas ao campo, realizadas no ano de 1995. O arquivo vetorial produzido foi editado no TOSCA e os polígonos gerados, com o uso do módulo CYCLE. Finalmente o arquivo com os polígonos foi rasterizado (POLYRAS). O PI-Solos foi gerado da digitalização do mapa de solos semidetalhado produzido por MIKLOS (1996). Novamente foi utilizado o TOSCA, seguido dos módulos CYCLE e POLYRAS para produzir o arquivo matricial correspondente. O PI-Potencial de infiltração e Escoamento Superficial da Água foi obtido pelo cruzamento das informações de condutividade hidráulica dos solos e da declividade do terreno. Este PI e um passo intermediário de um método proposto para a identificação das áreas de risco de contaminação por agrotóxicos, apresentado em LUIS (1996) e GOMES (1996). Foram também gerados alguns PI's relacionados com o estudo de erosão na microbacia, utilizando a Equação Universal de Perdas de Solo - EUPS. O IDRISI forneceu os recursos necessários aos objetivos do trabalho. As principais deficiências encontradas são a interface homem/máquina e a criação dos polígonos com a TOSCA, onde há necessidade de informar para cada arco digitado os identificadores dos polígonos por ele dividido. Esta operação consome um esforço considerável e está sujeita a freqüentes erros.