Natural and anthropogenic factors drive large-scale freshwater fish invasions.

We analyzed the large-scale drivers of biological invasions using freshwater fish in a Mediterranean country as a test case, and considering the contribution of single species to the overall invasion pattern. Using Boosted Regression Tree (BRT) models, variation partitioning and Redundancy Analysis (RDA), we found that human factors (especially eutrophication) and climate (especially temperature) were significant drivers of overall invasion. Geography was also relevant in BRT and RDA analysis, both at the overall invasion and the single species level. Only variation partitioning suggested that land use was the second most significant driver group, with considerable overlap between different invasion drivers and only land use and human factors standing out for single effects. There was general accordance both between different analyses, and between invasion outcomes at the overall and the species level, as most invasive species share similar ecological traits and prefer lowland river stretches. Human-mediated eutrophication was the most relevant invasion driver, but the role of geography and climate was at least equally important in explaining freshwater fish invasions. Overall, human factors were less prominent than natural factors in driving the spread and prevalence of invasion, and the species spearheading it.

Species interactions and climate change: How the disruption of species co-occurrence will impact on an avian forest guild.

Interspecific interactions are crucial in determining species occurrence and community assembly. Understanding these interactions is thus essential for correctly predicting species' responses to climate change. We focussed on an avian forest guild of four hole-nesting species with differing sensitivities to climate that show a range of well-understood reciprocal interactions, including facilitation, competition and predation. We modelled the potential distributions of black woodpecker and boreal, tawny and Ural owl, and tested whether the spatial patterns of the more widespread species (excluding Ural owl) were shaped by interspecific interactions. We then modelled the potential future distributions of all four species, evaluating how the predicted changes will alter the overlap between the species' ranges, and hence the spatial outcomes of interactions. Forest cover/type and climate were important determinants of habitat suitability for all species. Field data analysed with N-mixture models revealed effects of interspecific interactions on current species abundance, especially in boreal owl (positive effects of black woodpecker, negative effects of tawny owl). Climate change will impact the assemblage both at species and guild levels, as the potential area of range overlap, relevant for species interactions, will change in both proportion and extent in the future. Boreal owl, the most climate-sensitive species in the guild, will retreat, and the range overlap with its main predator, tawny owl, will increase in the remaining suitable area: climate change will thus impact on boreal owl both directly and indirectly. Climate change will cause the geographical alteration or disruption of species interaction networks, with different consequences for the species belonging to the guild and a likely spatial increase of competition and/or intraguild predation. Our work shows significant interactions and important potential changes in the overlap of areas suitable for the interacting species, which reinforce the importance of including relevant biotic interactions in predictive climate change models for increasing forecast accuracy.

Evaluating mortality rates with a novel integrated framework for nonmonogamous species.

The conservation of wildlife requires management based on quantitative evidence, and especially for large carnivores, unraveling cause-specific mortalities and understanding their impact on population dynamics is crucial. Acquiring this knowledge is challenging because it is difficult to obtain robust long-term data sets on endangered populations and, usually, data are collected through diverse sampling strategies. Integrated population models (IPMs) offer a way to integrate data generated through different processes. However, IPMs are female-based models that cannot account for mate availability, and this feature limits their applicability to monogamous species only. We extended classical IPMs to a two-sex framework that allows investigation of population dynamics and quantification of cause-specific mortality rates in nonmonogamous species. We illustrated our approach by simultaneously modeling different types of data from a reintroduced, unhunted brown bear (Ursus arctos) population living in an area with a dense human population. In a population mainly driven by adult survival, we estimated that on average 11% of cubs and 61% of adults died from human-related causes. Although the population is currently not at risk, adult survival and thus population dynamics are driven by anthropogenic mortality. Given the recent increase of human-bear conflicts in the area, removal of individuals for management purposes and through poaching may increase, reversing the positive population growth rate. Our approach can be generalized to other species affected by cause-specific mortality and will be useful to inform conservation decisions for other nonmonogamous species, such as most large carnivores, for which data are scarce and diverse and thus data integration is highly desirable.