Progress in the use of scanning electron microscopy (SEM) applied to the taxonomy of the genus Bursaphelenchus (Nematoda: Parasitaphelenchidae)

The scanning electron microscope (SEM) has been a major tool in detailed morphological observations of plant parasitic nematodes during the last 30 years, efficiently complementing light microscopical (LM) studies. Nematodes are extremely difficult to observe and characterize due to their small size (aprox. 1 mm long) and paucity of morphological characters, so detailed surface observations of several organs and nematode regions are of the highest value. Among plant parasitic nematodes, one of the most devastating species is the “pinewood nematode” (PWN), Bursaphelenchus xylophilus, which has been a major problem for forest species, and in particular pines, in Asia (Japan, China, Korea) and has been recently detected in the European Union (Portugal). B. xylophilus belongs to a closely related, morphologically similar group of species, within the genus Bursaphelenchus, and designated by the “xylophilus group”. SEM has become a crucial tool in observing several genital characters of males and females, such as male genital papillae, male copulatory spicules, female vulval flap and female genital papillae.s In this presentation, we will show how SEM has been utilized to observe and characterize the shape of the vulval flap, the presence/ absence of papillae near the flap, and confirm the presence and the arrangement of the male genital papillae. LM is also used in this work to show its value as a complementary tool to SEM, in both genital characteristics and other, general, characters of the genus Bursaphelenchus, such as the male bursa and cephalic region.

fuzzySim: Fuzzy similarity in species distributions

fuzzySim is an R package for calculating fuzzy similarity in species occurrence patterns. It includes functions for data preparation, such as converting species lists (long format) to presence-absence tables (wide format), obtaining unique abbreviations of species names, or transposing (parts of) complex data frames; and sample data sets for providing practical examples. It can convert binary presence-absence to fuzzy occurrence data, using e.g. trend surface analysis, inverse distance interpolation or prevalence-independent environmental favourability modelling, for multiple species simultaneously. It then calculates fuzzy similarity among (fuzzy) species distributions and/or among (fuzzy) regional species compositions. Currently available similarity indices are Jaccard, Sørensen, Simpson, and Baroni-Urbani & Buser.

Evolução da distribuição da lontra (lutra lutra L.) na península ibérica: modelos a diferentes escalas e a sua projeção no espaço e no tempo

A Lontra Euroasiática foi alvo de quatro prospeções na Península Ibérica (1990-2008). Em 2003, foi publicado um modelo de distribuição da lontra, com base nos dados de presença/ausência das prospeções publicadas em 1998. Dadas as suas características, este tipo de modelos pode tornar-se um elemento chave nas estratégias de recuperação da lontra como também, de outras espécies, se comprovada a sua fiabilidade e capacidade de antecipar tendências na distribuição das mesmas. Assim, esta dissertação confrontou as previsões do modelo com os dados de distribuição de 2008, a fim de identificar potências áreas de discordância. Os resultados revelam que, o modelo de distribuição de lontra proposto, apesar de ter por base dados de 1998 e de não considerar explicitamente processos biológicos, conseguiu captar o essencial da relação espécie-ambiente, resultando num bom desempenho preditivo para a distribuição da mesma em Espanha, uma década depois da sua construção; Evolution of otter (Lutra lutra L.) distribution in the Iberian Peninsula: Models at different scales and their projection through space and time Abstract: The Eurasian otter was already surveyed four times in the Iberian Peninsula (1990-2008). In 2003, a distribution model for the otter based on presence/absence data from the survey published in 1998, was published. This type of models has advantages that can make it in a key element for otter conservation strategies and also, for other species, but only, if their reliability and capability to predict species distribution tendencies are validated. The present thesis compares the model predictions with 2008 data, in order to find potential mismatch areas. Results suggest that, although the distribution model for the otter was based on data from 1998 and, doesn’t include explicitly biological mechanisms, it managed to correctly identify the essence of the species-environment relationship, what was translated in a good predictive performance for its actual distribution in Spain, after a decade of its construction.

Biotic and abiotic parameters that distinguish types of temporary ponds in a Portuguese Mediterranean ecosystem

Temporary ponds are seasonal wetland habitats subjected to extreme and unstable ecological conditions. Some are classified as priority habitats for conservation by the European Union Habitats Directive. Our study area was the coastal plain of southwest Portugal, which spans across 100km north to south and hosts a large number of temporary ponds as a consequence of climatic and edaphic characteristics. Field sampling of floristic and edaphic data was carried out in 24 temporary ponds every spring between 2005 and 2008. We recorded a total of 174 plant species identified within visually homogeneous plots. We included the data in a geographic information system and classified ponds according to their floristic composition, using a biotic regionalization analysis based on species presence/absence, which is a practical and unambiguous criterion. We found three significantly different groups of ponds which corresponded to an eco-physiognomic pond typology: Mediterranean temporary ponds, marshlands, and disturbed ponds. For the first two pond types we defined characteristic or indicator plant species. We searched also for relationships between pond type and a series of large-scale climatic, geographic, and geological variables, as well as local-scale physical and chemical properties of the soil. Pond type was distinguished by a complex combination of some of these variables, including environmental energy, soil texture, nitrogen content of the soil and pH. A practical way of discriminating between different

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

Transferring distribution models between different geographical areas may be problematic, as the performance of models outside their original scope is hard to predict. A modelling procedure is needed that gets the gist of the environmental descriptors of a distribution area, without either overfitting to the training data or overestimating the species’ distribution potential.We tested the transferability power of the favourability function, a generalized linear model, on the distribution of the Iberian desman (Galemys pyrenaicus) in the Iberian territories of Portugal and Spain.We also tested the effects of two of the main potential constraints on model transferability: the analysed ranges of the predictor variables, and the completeness of the species distribution data. We modelled 10 km×10km presence/absence data from Portugal and Spain separately, extrapolated each model to the other country, and compared predictions with observations. The Spanish model, despite arguably containing more false absences, showed good predictive ability in Portugal. The Portuguese model, whose predictors ranged between only a subset of the values observed in Spain, overestimated desman distribution when transferred.We discuss possible reasons for this differential model behaviour, and highlight the importance of this kind of models for prediction and conservation applications

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.

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.