ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches

Tigers are critically endangered due to deforestation and persecution. Yet in places, Sumatran tigers (Panthera tigris sumatrae) continue to coexist with people, offering insights for managing wildlife elsewhere. Here, we couple spatial models of encounter risk with information on tolerance from 2386 Sumatrans to reveal drivers of human-tiger conflict. Risk of encountering tigers was greater around populated villages that neighboured forest or rivers connecting tiger habitat; geographic profiles refined these predictions to three core areas. People's tolerance for tigers was related to underlying attitudes, emotions, norms and spiritual beliefs. Combining this information into socio-ecological models yielded predictions of tolerance that were 32 times better than models based on social predictors alone. Pre-emptive intervention based on these socio-ecological predictions could have averted up to 51% of attacks on livestock and people, saving 15 tigers. Our work provides further evidence of the benefits of interdisciplinary research on conservation conflicts.

/pdf/addressing-human-tiger-conflict-using-socio-ecological-bl9v1z66mc.pdf

Addressing human-tiger conflict using socio-ecological information on tolerance and risk

Environmental observatory networks (EONs) provide information to better understand, model and forecast the spatial and temporal dynamics of Earth's biophysical process. Consequently, representative...

Representativeness of FLUXNET sites across Latin America

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/ECB8C33A86F6C19ADAD92C78F14E4A40/S0033589418001527a.pdf/div-class-title-spatiotemporal-patterns-of-pre-columbian-people-in-amazonia-div.pdf

Spatiotemporal patterns of pre-Columbian people in Amazonia

Setting appropriate conservation measures to halt the loss of biodiversity requires a good understanding of species' habitat requirements and potential distribution. Recent (past few decades) ecological data are typically used to estimate and understand species' ecological niches. However, historical local extinctions may have truncated species-environment relationships, resulting in a biased perception of species' habitat preferences. This may result in incorrect assessments of the area potentially available for their conservation. Incorporating long-term (centuries-old) occurrence records with recent records may provide better information on species-environment relationships and improve the modelling and understanding of habitat suitability. We test whether neglecting long-term occurrence records leads to an underestimation of species' historical niche and potential distribution and identify which species are more vulnerable to this effect. We compare outputs of species distribution models and niche hypervolumes built using recent records only with those built using both recent and long-term (post-1500) records, for a set of 34 large mammal species in South Africa. We find that, while using recent records only is adequate for some species, adding historical records in the analyses impacts estimates of the niche and habitat suitability for 12 species (34%) in our dataset, and that this effect is significantly higher for carnivores. These results show that neglecting long-term biodiversity records in spatial analyses risks misunderstanding, and generally underestimating, species' niches, which in turn may lead to ill-informed management decisions, with significant implications for the effectiveness of conservation efforts. This article is part of a discussion meeting issue 'The past is a foreign country: how much can the fossil record actually inform conservation?'

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2019.0215

Shifted distribution baselines: neglecting long-term biodiversity records risks overlooking potentially suitable habitat for conservation management

There is growing interest among conservationists in biodiversity mapping based on stacked species distribution models (SSDMs), a method that combines multiple individual species distribution models to produce a community-level model. However, no user-friendly interface specifically designed to provide the basic tools needed to fit such models was available until now.
The “ssdm” package is a computer platform implemented in r providing a range of methodological approaches and parameterisation at each step in building the SSDM: e.g. pseudo-absence selection, variable contribution and model accuracy assessment, inter-model consensus forecasting, species assembly design, and calculation of weighted endemism.
The object-oriented design of the package is such that: users can modify existing methods, extend the framework by implementing new methods, and share them to be reproduced by others.
The package includes a graphical user interface to extend the use of SSDMs to a wide range of conservation scientists and practitioners.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12841

Dimitri Justeau

Papers

SSDM : an R package to predict distribution of species richness and composition based on stacked species distribution models