Uncovering the spatial variability of recent deforestation drivers in the Brazilian Cerrado.

In recent years, the Cerrado deforestation has increased considerably, reaching rates higher than in the Amazonian realm. Although the effects of deforestation are well known, the understanding of its drives at regional levels is incipient. Most studies consider that a driver influences deforestation likewise in all regions. However, deforestation has a strong spatial structure that can lead drivers to vary their influence on deforestation in different regions. Here, we evaluated the spatial variability in the relationship between the recent Cerrado deforestation and socioeconomic, environmental, and structural drivers at a regional scale. We used a geographically weighted regression (GWR) to assess the spatial variability of predictor variables. We identified regions that respond similarly to the drivers by grouping municipalities, considering their GWR coefficients through hierarchical clustering. The analyses that consider the spatial variability of predictors are more appropriated to assess the causes of recent deforestation. Remnant natural vegetation influenced the recent deforestation in all defined regions. Greater access to rural credit concession was the main driving force of deforestation in the northeast region defined here. Distance to roads increased deforestation in the northeast and north regions, while it inhibited deforestation in the central-east and southeast regions. Rainfall inhibited deforestation in the northeast, north, and southwest regions. Steep slope prevented deforestation mainly in the northeast, north, and southwest regions. Our results highlight that, to effectively reduce Cerrado deforestation, public policies should integrate strategies focusing not only at national and biome levels but also at the regional spatial level.

Climatically-mediated landcover change: impacts on Brazilian territory.

In the face of climate change threats, governments are drawing attention to policies for mitigating its effects on biodiversity. However, the lack of distribution data makes predictions at species level a difficult task, mainly in regions of higher biodiversity. To overcome this problem, we use native landcover as a surrogate biodiversity, because it can represent specialized habitat for species, and investigate the effects of future climate change on Brazilian biomes. We characterize the climatic niches of native landcover and use ecological niche modeling to predict the potential distribution under current and future climate scenarios. Our results highlight expansion of the distribution of open vegetation and the contraction of closed forests. Drier Brazilian biomes, like Caatinga and Cerrado, are predicted to expand their distributions, being the most resistant to climate change impacts. However, these would also be affected by losses of their closed forest enclaves and their habitat-specific or endemic species. Replacement by open vegetation and overall reductions are a considerable risk for closed forest, threatening Amazon and Atlantic forest biomes. Here, we evidence the impacts of climate change on Brazilian biomes, and draw attention to the necessity for management and attenuation plans to guarantee the future of Brazilian biodiversity.

Body size and trophic structure explain global asymmetric response of tetrapod diversity to climate effects.

Although climate-based hypotheses are widely used to explain large-scale diversity patterns, they fall short of explaining the spatial variation among taxonomic groups. Integrating food web and metabolic theories into macroecology is a promising step forward, as they allow including explicit taxon-specific traits that can potentially mediate the relationship between climate and diversity. Our investigation focuses on the role of body size and trophic structure in mediating the influence of contemporary climate and historical climate change on global tetrapods species richness. We used piecewise structural equation modeling to assess the direct effects of contemporary climate and climate instability of species richness and the indirect effects of climate on tetrapod richness mediated by community-wide species traits. We found that birds and mammals are less sensitive to the direct effect of contemporary climate than amphibians and squamates. Contemporary climate and climate instability favored the species richness of mammals and amphibians. However, for birds and squamates, this link is only associated with contemporary climate. Moreover, we showed that community-wide traits are correlated with species richness gradients. However, we highlight that this relationship is dependent upon the specific traits and taxonomic groups. Specifically, bird communities with smaller bodies and bottom-heavy structures support higher species richness. Squamates also tend to be more diverse in communities with prevalence of smaller bodies, while mammals are correlated with top-heavy structures. Moreover, we showed that higher contemporary climate and climate instability reduce the species richness of birds and mammals through community-wide traits and indirectly increase squamate species richness. We also showed that body size and trophic structure are driving a global asymmetric response of tetrapod diversity to climate effects, which highlights the limitation to use the "typical" climate-based hypotheses. Furthermore, by combining multiple theories, our research contributes to a more realistic and mechanistic understanding of diversity patterns across taxonomic groups.

A quixotic view of spatial bias in modelling the distribution of species and their diversity.

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.

Geographical patterns in climate and agricultural technology drive soybean productivity in Brazil.

The impacts of global climate change have been a worldwide concern for several research areas, including those dealing with resources essential to human well being, such as agriculture, which directly impact economic activities and food security. Here we evaluate the relative effect of climate (as indicated by the Ecological Niche Model-ENM) and agricultural technology on actual soybean productivity in Brazilian municipalities and estimate the future geographic distribution of soybeans using a novel statistical approach allowing the evaluation of partial coefficients in a non-stationary (Geographically Weighted Regression; GWR) model. We found that technology was more important than climate in explaining soybean productivity in Brazil. However, some municipalities are more dependent on environmental suitability (mainly in Southern Brazil). The future environmental suitability for soybean cultivation tends to decrease by up 50% in the central region of Brazil. Meanwhile, southern-most Brazil will have more favourable conditions, with an increase of ca. 25% in environmental suitability. Considering that opening new areas for cultivation can degrade environmental quality, we suggest that, in the face of climate change impacts on soybean cultivation, the Brazilian government and producers must invest in breeding programmes and more general ecosystem-based strategies for adaptation to climate change, including the development of varieties tolerant to climate stress, and strategies to increase productivity and reduce costs (social and environmental).

Temporal degradation of data limits biodiversity research.

Spatial and/or temporal biases in biodiversity data can directly influence the utility, comparability, and reliability of ecological and evolutionary studies. While the effects of biased spatial coverage of biodiversity data are relatively well known, temporal variation in data quality (i.e., the congruence between recorded and actual information) has received much less attention. Here, we develop a conceptual framework for understanding the influence of time on biodiversity data quality based on three main processes: (1) the natural dynamics of ecological systems-such as species turnover or local extinction; (2) periodic taxonomic revisions, and; (3) the loss of physical and metadata due to inefficient curation, accidents, or funding shortfalls. Temporal decay in data quality driven by these three processes has fundamental consequences for the usage and comparability of data collected in different time periods. Data decay can be partly ameliorated by adopting standard protocols for generation, storage, and sharing data and metadata. However, some data degradation is unavoidable due to natural variations in ecological systems. Consequently, changes in biodiversity data quality over time need be carefully assessed and, if possible, taken into account when analyzing aging datasets.

Climatically-mediated landcover change: impacts on Brazilian territory

ABSTRACT In the face of climate change threats, governments are drawing attention to policies for mitigating its effects on biodiversity. However, the lack of distribution data makes predictions at species level a difficult task, mainly in regions of higher biodiversity. To overcome this problem, we use native landcover as a surrogate biodiversity, because it can represent specialized habitat for species, and investigate the effects of future climate change on Brazilian biomes. We characterize the climatic niches of native landcover and use ecological niche modeling to predict the potential distribution under current and future climate scenarios. Our results highlight expansion of the distribution of open vegetation and the contraction of closed forests. Drier Brazilian biomes, like Caatinga and Cerrado, are predicted to expand their distributions, being the most resistant to climate change impacts. However, these would also be affected by losses of their closed forest enclaves and their habitat-specific or endemic species. Replacement by open vegetation and overall reductions are a considerable risk for closed forest, threatening Amazon and Atlantic forest biomes. Here, we evidence the impacts of climate change on Brazilian biomes, and draw attention to the necessity for management and attenuation plans to guarantee the future of Brazilian biodiversity.