From descriptive to predictive distribution models: a working example with Iberian amphibians and reptiles

BACKGROUND: Aim of the study was to identify the conditions under which spatial-environmental models can be used for the improved understanding of species distributions, under the explicit criterion of model predictive performance. I constructed distribution models for 17 amphibian and 21 reptile species in Portugal from atlas data and 13 selected ecological variables with stepwise logistic regression and a geographic information system. Models constructed for Portugal were extrapolated over Spain and tested against range maps and atlas data. RESULTS: Descriptive model precision ranged from 'fair' to 'very good' for 12 species showing a range border inside Portugal ('edge species', kappa (k) 0.35–0.89, average 0.57) and was at best 'moderate' for 26 species with a countrywide Portuguese distribution ('non-edge species', k = 0.03–0.54, average 0.29). The accuracy of the prediction for Spain was significantly related to the precision of the descriptive model for the group of edge species and not for the countrywide species. In the latter group data were consistently better captured with the single variable search-effort than by the panel of environmental data. CONCLUSION: Atlas data in presence-absence format are often inadequate to model the distribution of species if the considered area does not include part of the range border. Conversely, distribution models for edge-species, especially those displaying high precision, may help in the correct identification of parameters underlying the species range and assist with the informed choice of conservation measures

Occurrence of gastrointestinal parasites in goat kids Ocorrência de parasitos gastrintestinais em cabritos

Fecal samples from male and female goat kids, of different breeds and up to one year of age, were analyzed to determine egg and oocyst counts per gram of feces (EPG and OPG, respectively), and fecal culturing was performed to identify nematode genera. Helminth eggs and Eimeria spp. oocysts were found in 93.06% (188/202) and 77.22% (156/202) of the fecal samples, respectively. From fecal cultures, the following genera were identified: Cooperia in 11.88% (24/202), Haemonchus in 51.98% (105/202), Oesophagostomum in 9.4% (19/202), Strongyloides in 5.94 (12/202) and Trichostrongylus in 20.79% (42/202) of the samples. The Eimeria species found were E. alijevi in 25.24% (51/202), E. arloingi in 7.42% (15/202), E. caprina in 2.97% (6/202), E. caprovina in 10.39% (21/202), E. christenseni in 4.45% (9/202), E. joklchijevi in 11.38% (23/202), E. hirci in 9.4% (19/202) and E. ninakohlyakimovae in 28.71% (58/202) samples. Among the gastrointestinal parasites, the genus Haemonchus and two Eimeria species (E. ninakohlyakimovae and E. alijevi) were predominants.<br>Amostras fecais de cabritos machos e fêmeas, de diferentes raças e com até uma ano de idade, foram examinadas para determinação do número de ovos e oocistos por grama de fezes (OPG e OoPG, respectivamente) e coprocultura para identificação genérica dos nematódeos. Ovos de helmintos e oocistos de Eimeria spp. foram observados em 93,06% (188/202) e 77,22% (156/202) das amostras, respectivamente. Pelas coproculturas, foram identificados os gêneros Cooperia em 11,88% (24/202), Haemonchus em 51,98% (105/202), Oesophagostomum em 9,4% (19/202), Strongyloides em 5,94 (12/202) e Trichostrongylus em 20,79% (42/202) das amostras. As espécies de Eimeria encontradas foram E. alijevi em 25,24% (51/202), E. arloingi em 7,42% (15/202), E. caprina em 2,97% (6/202), E. caprovina em 10,39% (21/202), E. christenseni em 4,45% (9/202), E. joklchijevi em 11,38% (23/202), E. hirci em 9,4% (19/202) e E. ninakohlyakimovae em 28,71% (58/202) das amostras. Dentre os parasitas gastrintestinais, houve predominância do gênero Haemonchus e de duas espécies de Eimeria: E. ninakohlyakimovae e E. alijevi

Is the Climate Right for Pleistocene Rewilding? Using Species Distribution Models to Extrapolate Climatic Suitability for Mammals across Continents

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial “Pleistocene rewilding” proposal was used as a novel example to address some of the challenges of extrapolating modeled species-climate relationships outside of current ranges. Climatic suitability for three proposed proxy species (Asian elephant, African cheetah and African lion) was extrapolated to the American southwest and Great Plains using Maxent, a machine-learning species distribution model. Similar models were fit for Oryx gazella, a species native to Africa that has naturalized in North America, to test model predictions. To overcome biases introduced by contracted modern ranges and limited occurrence data, random pseudo-presence points generated from modern and historical ranges were used for model training. For all species except the oryx, models of climatic suitability fit to training data from historical ranges produced larger areas of predicted suitability in North America than models fit to training data from modern ranges. Four naturalized oryx populations in the American southwest were correctly predicted with a generous model threshold, but none of these locations were predicted with a more stringent threshold. In general, the northern Great Plains had low climatic suitability for all focal species and scenarios considered, while portions of the southern Great Plains and American southwest had low to intermediate suitability for some species in some scenarios. The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy. This has implications for modeling range shifts of organisms in response to climate change