Search

Networks of protected areas (PAs) form the backbone for biodiversity conservation worldwide. The effectiveness of protected areas has been studied and it has been shown that confounding factors, such as remoteness and accessibility, correlated with both presence of protection and extractive behaviors, affect the outcomes. We investigated the effectiveness of Madagascar's PA network in decreasing deforestation pressures, using a novel counterfactual methodology, accounting for distance to roads, rivers, major cities and altitude, slope and annual rainfall. The assessment was independently conducted for two different time periods, 1990–2000 and 2000–2010, and for Madagascar's three major forest types. We found that PAs were effective to some extent in reducing deforestation and that some of this decrease can be attributed to the presence of PAs, not just to the confounding factors rendering the land assigned for protection less likely to be deforested. We found differences in PA effectiveness between the two time periods, and in general lower deforestation in the later time period has meant that the PAs have less pressures to resist. However, in the spiny forest, even if deforestation had overall diminished, the pressure on reference areas used to compare PAs seemed to have increased showing that PAs have indeed a mitigation effect and thus increased in effectiveness in the second time period. Our study highlights the alarming trend of what happens once enough forest has been lost in easily accessible areas and the pressures starts to spread to also more remote areas and lands comparable to PAs (remote and inaccessible). [ABSTRACT FROM AUTHOR]

Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by example and discuss recent computation tools and future directions. [ABSTRACT FROM AUTHOR]

Abstract: Distributions of species, animals or plants, terrestrial or aquatic, are influenced by numerous factors such as physical and biogeographical gradients. Dominant wind and current directions cause the appearance of gradients in physical conditions whereas biogeographical gradients can be the result of historical events (e.g. glaciations). No spatial modelling technique has been developed to this day that considers the direction of an asymmetric process controlling species distributions along a gradient or network. This paper presents a new method that can model species spatial distributions generated by a hypothesized asymmetric, directional physical process. This method is an eigenfunction-based spatial filtering technique that offers as much flexibility as the Moran''s eigenvector maps (MEM) framework; it is called asymmetric eigenvector maps (AEM) modelling. Information needed to construct eigenfunctions through the AEM framework are the spatial coordinates of the sampling or experimental sites, a connexion diagram linking the sites to one another, prior information about the direction of the hypothesized asymmetric process influencing the response variable(s), and optionally, weights attached to the edges (links). To illustrate how this new method works, AEM is compared to MEM analysis through simulations and in the analysis of an ecological example where a known asymmetric forcing is present. The ecological example reanalyses the dietary habits of brook trout (Salvelinus fontinalis) sampled in 42 lakes of the Mastigouche Reserve, Québec. [Copyright &y& Elsevier]