The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type.

To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain bird species richness, recent studies suggest that unclassified remote-sensed images can provide equally good or better results. In our study, we aimed to investigate whether unclassified multispectral data from Landsat 8 can replace image classification for bird diversity modeling. Moreover, we also tested the Spectral Variability Hypothesis. Using the Atlas of Breeding Birds in the Czech Republic 2014-2017, we modeled species richness at two spatial resolutions of approx. 131 km2 (large squares) and 8 km2 (small squares). As predictors of the richness, we assessed 1) classified land cover data (Corine Land Cover 2018 database), 2) spectral heterogeneity (computed in three ways) and landscape composition derived from unclassified remote-sensed reflectance and vegetation indices. Furthermore, we integrated information about the landscape types (expressed by the most prevalent land cover class) into models based on unclassified remote-sensed data to investigate whether the landscape type plays a role in explaining bird species richness. We found that unclassified remote-sensed data, particularly spectral heterogeneity metrics, were better predictors of bird species richness than classified land cover data. The best results were achieved by models that included interactions between the unclassified data and landscape types, indicating that relationships between bird diversity and spectral heterogeneity vary across landscape types. Our findings demonstrate that spectral heterogeneity derived from unclassified multispectral data is effective for assessing bird diversity across the Czech Republic. When explaining bird species richness, it is important to account for the type of landscape and carefully consider the significance of the chosen spatial scale.

Performance of landscape composition metrics for predicting water quality in headwater catchments.

Land use is a predominant threat to the ecological integrity of streams and rivers. Understanding land use-water quality interactions is essential for the development and prioritization of management strategies and, thus, the improvement of water quality. Weighting schemes for land use have recently been employed as methods to advance the predictive power of empirical models, however, their performance has seldom been explored for various water quality parameters. In this work, multiple landscape composition metrics were applied within headwater catchments of Central Europe to investigate how weighting land use with certain combinations of spatial and topographic variables, while implementing alternate distance measures and functions, can influence predictions of water quality. The predictive ability of metrics was evaluated for eleven water quality parameters using linear regression. Results indicate that stream proximity, measured with Euclidean distance, in combination with slope or log-transformed flow accumulation were dominant factors affecting the concentrations of pH, total phosphorus, nitrite and orthophosphate phosphorus, whereas the unweighted land use composition was the most effective predictor of calcium, electrical conductivity, nitrates and total suspended solids. Therefore, both metrics are recommended when examining land use-water quality relationships in small, submontane catchments and should be applied according to individual water quality parameter.

Multiple drivers of plant diversity in coastal dunes: A Mediterranean experience.

In light of the global biodiversity loss, syntheses of the available knowledge about drivers of biodiversity are becoming increasingly important. However, despite the high number of studies analyzing patterns of plant species diversity, few attempts have been made to synthesize findings within different ecosystems. In this work, the relative role of a wide set of predictors imputable to three conceptual-methodological domains (abiotic, human-mediated disturbance and landscape domain, hereafter AD, DD and LD) was simultaneously analyzed in 644 random plots distributed along the coastal dunes of Central Italy. Native species richness and focal species cover, both field-recorded, were used as response variables. Predictors pertaining to the three domains were derived from both field surveys and high-resolution remotely sensed imagery (LiDAR and orthophotos). To test how AD, DD and LD affect native species richness and focal species cover, a GLM and a linear model were fitted respectively. The three domains were then ranked according to their relative importance. Although the role of the three domains was always significant, they turned out to unequally contribute to the explanation of native species richness and focal species cover patterns. For Mediterranean coastal dune ecosystems, AD appears to be the key biodiversity driver, followed by DD and LD. Our results suggest that as long as human disturbance is limited, plant diversity will distribute according to species abiotic tolerances, regardless of habitat loss and fragmentation per se. Representing a first effort towards a synthesis of plant diversity drivers in coastal dunes, this work points to the importance, in Mediterranean coastal dune ecosystems, of zonation dynamics, whose occurrence should be addressed as a priority issue by efficient conservations strategies.

Spatial spread of Eurasian beavers in river networks: a comparison of range expansion rates.

1. Accurately measuring the rate of spread for expanding populations is important for reliably predicting their future spread, as well as for evaluating the effect of different conditions and management activities on that rate of spread. 2. Although a number of methods have been developed for such measurement, all these are designed only for one- or two-dimensional spread. Species dispersing along rivers, however, require specific methods due to the distinctly branching structure of river networks. 3. In this study, we analyse data regarding Eurasian beavers' modern recolonization of the Czech Republic. We developed a new methodology for quantifying spread of species dispersing along streams based on representation of the river network by means of a weighted graph. 4. We defined two different network-based spread rate measures, one estimating the rate of range expansion, with the range defined as the total length of occupied streams, and the second, named range diameter, quantifying the progress along one or several main streams. In addition, we estimated the population growth rates, and, dividing the population size by the range size, we measured the density of beaver records within their overall range. Using linear regression, we compared four beaver populations under different environmental conditions in terms of each of these measures. Finally, we discuss the differences between our method and the classical approaches. 5. Our method provided substantially higher spread rate values than did the classical methods. Both population growth and range expansion were found to follow logistic growth. In cases of there being no considerable barriers in dispersal routes, the rate of progress along main streams did not differ significantly among populations. In homogeneous environments, population densities remained relatively constant over time even though overall population sizes increased. This indicates that at large spatial scales, the population growth of beavers occurs through progressive space filling rather than increasing population density.