Modelling semi-aquatic vertebrates’ distribution at the drainage basin scale: The case of the otter Lutra lutra in Italy

Modelling habitat suitability of semi-aquatic vertebrates for large scale conservation purposes is a particularly challenging task, due to the fine-scale linearity of riverine habitats, and to the ecological continuum represented by the riparian and the aquatic ecosystems, on one side, and by a river and its tributaries, on the other.     

Rarefaction and nonrandom spatial dispersion patterns

Rarefaction estimates how many species are expected in a random sample of individuals from a larger collection and allows meaningful comparisons among collections of different sizes. It assumes random spatial dispersion. However, two common dispersion patterns, within-species clumping and segregation among species, can cause rarefaction to overestimate the species richness of a smaller continuous area. We use field studies and computer simulations to determine (1) how robust rarefaction is to nonrandom spatial dispersion and (2) whether simple measures of spatial autocorrelation can predict the bias in rarefaction estimates. Rarefaction does not estimate species richness accurately for many communities, especially at small sample sizes. Measures of spatial autocorrelation of the more abundant species do not reliably predict amount of bias. Survey sites should be standardized to equal-sized areas before sampling. When sites are of equal area but differ in number of individuals sampled, rarefaction can standardize collections. When communities are sampled from different-sized areas, the mean and confidence intervals of species accumulation curves allow more meaningful comparisons among sites. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.     

Predictability of marine nematode biodiversity

In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.     

Genetic relatedness and space use in a behaviorally flexible species of marmot, the woodchuck (Marmota monax)

Solitary species show several patterns of space use and relatedness. Individuals may associate randomly or may live near female or male kin, often as a result of natal philopatry or dispersal patterns. Although usually described as solitary or asocial, woodchucks (Marmota monax) are behaviorally flexible marmots that exhibit greater sociality in some populations than others. I examined relationships between kinship, geographic distance, and home range overlap, as well as dispersal and philopatry, to determine the extent to which kin associated spatially. I used a combination of microsatellite DNA analysis, long-term behavioral observations, and radiotelemetry to test predictions that females, but not males, would associate with kin. Indeed, woodchucks lived closer and shared a greater proportion of their home range with more closely related animals. Overlap of females' and males' home ranges was positively correlated with kinship, and male–female dyads shared more area with closer kin. Most juveniles delayed dispersal beyond their first summer. Females often remained philopatric and settled near their natal range. Although males often dispersed as yearlings, some males also established territories within or immediately adjacent to their natal home ranges. A combination of factors can explain these spatial patterns, including high population density associated with the study site's location within a suburban environment, high dispersal costs, and abundant food. Thus, despite their asocial and solitary reputation, woodchucks displayed spatial patterns seen in other, more social species of ground-dwelling sciurids.     

Spatially explicit modeling of overstory manipulations in young forests: Effects on stand structure and light

Young forests can be manipulated in diverse ways to enhance their ecological values. We used stem maps from two dense, second-growth stands in western Washington and a spatially explicit light model (tRAYci) to simulate effects of five silvicultural manipulations on diameter distribution, species composition, spatial patterning, and light availability. Each treatment removed 30% of the basal area, but differed in how trees were selected for removal. Three primary treatments were thin from below (removing the smallest trees), random thin (removing trees randomly), and gap creation (removing all trees in circles ∼1 tree height in diameter). Two additional treatments combined elements of these approaches: random ecological thin (a mixture of thin from below and random thin) and structured ecological thin (a mixture of thin from below and gap creation).     

Neutral modelling of agricultural landscapes by tessellation methods—Application for gene flow simulation

Neutral landscape models are not frequently used in the agronomical domain, whereas they would be very useful for studying given agro-ecological or physical processes. Contrary to ecological neutral landscape models, agricultural models have to represent and manage geometrical patches and thus should rely on tessellation methods. We present a three steps approach that aimed at simulating such landscapes. Firstly, we characterized the geometry of three real field patterns; secondly, we generated simulated field patterns with two tessellation methods attempting to control the value of some of the observed characteristics and, thirdly, we evaluated the simulated field patterns. For this evaluation, we considered that good simulated field patterns should capture characteristics of real landscapes that are important for the targeted agro-ecological process. Real landscapes and landscapes simulated using either a Voronoi or a rectangular tessellation were thus compared when used as input data within a gene flow model. The results showed that neither tessellation method captured field shapes correctly, thus leading to over or (small) under estimation of gene flow. The Voronoi tessellation, though, performed better than the rectangular tessellation. Possible research directions are proposed to improve the simulated patterns, including the use of post-processing, the control of cell orientation or the implementation of other tessellation techniques.     

Impacts of Urbanization on Stream Habitat and Fish Across Multiple Spatial Scales

We analyzed the relation of the amount and spatial pattern of land cover with stream fish communities, in-stream habitat, and baseflow in 47 small southeastern Wisconsin, USA, watersheds encompassing a gradient of predominantly agricultural to predominantly urban land uses. The amount of connected impervious surface in the watershed was the best measure of urbanization for predicting fish density, species richness, diversity, and index of biotic integrity (IBI) score; bank erosion; and base flow. However, connected imperviousness was not significantly correlated with overall habitat quality for fish. Nonlinear models were developed using quantile regression to predict the maximum possible number of fish species, IBI score, and base flow for a given level of imperviousness. At watershed connected imperviousness levels less than about 8%, all three variables could have high values, whereas at connected imperviousness levels greater than 12% their values were inevitably low. Connected imperviousness levels between 8 and 12% represented a threshold region where minor changes in urbanization could result in major changes in stream condition. In a spatial analysis, connected imperviousness within a 50-m buffer along the stream or within a 1.6-km radius upstream of the sampling site had more influence on stream fish and base flow than did comparable amounts of imperviousness further away. Our results suggest that urban development that minimizes amount of connected impervious surface and establishes undeveloped buffer areas along streams should have less impact than conventional types of development.     

Slope,aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland

The slope and aspect of a vegetated surface strongly affects the amount of solar radiation intercepted by that surface. Solar radiation is the dominant component of the surface energy balance and influences ecologically critical factors of microclimate, including near-surface temperatures, evaporative demand and soil moisture content. It also determines the exposure of vegetation to photosynthetically active and ultra-violet wavelengths. Spatial variation in slope and aspect is therefore a key determinant of vegetation pattern, species distribution and ecosystem processes in many environments. Slope and aspect angle may vary considerably over distances of a few metres, and fine-scale species’ distribution patterns frequently follow these topographic patterns. The availability of suitable microclimate at such scales may be critical for the response of species distributions to climatic change at much larger spatial scales. However, quantifying the relevant microclimatic gradients is not straightforward, as the potential variation in solar radiation flux under clear-sky conditions is modified by local and regional variations in cloud cover, and interacts with long-wave radiation exchange, local meteorology and surface characteristics.     

A Bayesian approach to multi-source forest area estimation

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.