Variation in wood density across South American tropical forests

Wood density is a critical control on tree biomass, so poor understanding of its spatial variation can lead to large and systematic errors in forest biomass estimates and carbon maps. The need to understand how and why wood density varies is especially critical in tropical America where forests have exceptional species diversity and spatial turnover in composition. As tree identity and forest composition are challenging to estimate remotely, ground surveys are essential to know the wood density of trees, whether measured directly or inferred from their identity. Here, we assemble an extensive dataset of variation in wood density across the most forested and tree-diverse continent, examine how it relates to spatial and environmental variables, and use these relationships to predict spatial variation in wood density over tropical and sub-tropical South America. Our analysis refines previously identified east-west Amazon gradients in wood density, improves them by revealing fine-scale variation, and extends predictions into Andean, dry, and Atlantic forests. The results halve biomass prediction errors compared to a naïve scenario with no knowledge of spatial variation in wood density. Our findings will help improve remote sensing-based estimates of aboveground biomass carbon stocks across tropical South America.

Multiple Long-Term, Landscape-Scale Data Sets Reveal Intraspecific Spatial Variation in Temporal Trends for Bird Species

Quantifying temporal changes in species occurrence has been a key part of ecology since its inception. We quantified multidecadal site occupancy trajectories for 18 bird species in four independent long-term, large-scale studies (571 sites, ~1000 km latitude) in Australia. We found evidence of a year × long-term study interaction in the best-fitting models for 14 of the 18 species analysed, with differences in the temporal trajectories of the same species in multiple studies consistent with non-stationarity. Non-stationarity patterns in occupancy were not related to the distance from a species niche centroid; species in locations further from their niche centroid did not demonstrate differing temporal trajectories to those closer to their niche centroid. Furthermore, temporal trajectories of species were not associated with climatic values for each study relative to their niche. Our findings demonstrate the need for multiple long-term studies across a species range, especially when tailoring conservation decisions for populations.

Deconstructing the geography of human impacts on species' natural distribution

It remains unknown how species' populations across their geographic range are constrained by multiple coincident natural and anthropogenic environmental gradients. Conservation actions are likely undermined without this knowledge because the relative importance of the multiple anthropogenic threats is not set within the context of the natural determinants of species' distributions. We introduce the concept of a species 'shadow distribution' to address this knowledge gap, using explainable artificial intelligence to deconstruct the environmental building blocks of current species distributions. We assess shadow distributions for multiple threatened freshwater fishes in Switzerland which indicated how and where species respond negatively to threats - with negative threat impacts covering 88% of locations inside species' environmental niches leading to a 25% reduction in environmental suitability. Our findings highlight that conservation of species' geographic distributions is likely insufficient when biodiversity mapping is based on species distribution models, or threat mapping, without also quantifying species' expected or shadow distributions. Overall, we show how priority actions for nature's recovery can be identified and contextualised within the multiple natural constraints on biodiversity to better meet national and international biodiversity targets.

A multispecies hierarchical model to integrate count and distance-sampling data

Integrated community models-an emerging framework in which multiple data sources for multiple species are analyzed simultaneously-offer opportunities to expand inferences beyond the single-species and single-data-source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random-effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single-species and single-data-source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications.