Aim:We test a new species distribution modelling (SDM) framework, while comparing results to more common distribution modelling techniques. This framework allows for the combination of presence-only (PO) and presence-absence (PA) data and accounts for imperfect detection and spatial bias in presence data. The new framework tested here is based on a Poisson point process model, which allows for predictions of population size. We compared these estimates to those provided by experts on the species. Species and Location: Presence data on Baird's tapir (Tapirus bairdii) throughout its range from southern México to northern Colombia were used in this research, primarily from the years 2000 to 2016. Methods: Four SDM frameworks are compared as follows: (1) Maxent, (2) a presenceonly (PO) SDM based on a Poisson point process model (PPM), (3) a presence-absence (PA) SDM also based on a PPM and (4) an Integrated framework which combines the previous two models. Model averaging was used to produce a single set of coefficient estimates and predictive maps for each model framework. A hotspot analysis (Gi*) was used to identify habitat cores from the predicted intensity of the Integrated model framework. Results: Important variables to model the distribution of Baird's tapir included land cover, human pressure and topography. Accounting for spatial bias in the presence data affected which variables were important in the model. Maxent and the Integrated model produced predictive maps with similar patterns and were considered to be more in agreement with expert knowledge compared to the PO and PA models.Main conclusions: Total abundance as predicted by the model was higher than expert opinion on the species, but local density estimates from our model were similar to available independent assessments. We suggest that these results warrant further validation and testing through collection of independent test data, development of more precise predictor layers and improvements to the model framework.
Rivers border the geographic distributions of primate taxa, but the extent to which the rivers are effective barriers is debated. We here provide the first statistically substantiated analysis of the role of rivers as barriers to the distribution of primates in west and central Africa, as judged by the coincidence of the edge of distributions of forest-dwelling primates with rivers. We use data from the literature to analyze the distributions of 14 genera, 44 species, and 77 subspecies around 18 rivers, river groups, or stretches of rivers. Rivers bordered the distributions of more new taxa (subspecies) than of old taxa (genera), although within genera, age of origin of the taxon did not correlate with likelihood of rivers bordering distributions. Fewer taxa crossed wide rivers than crossed narrow ones. Some analyses indicated that more smaller-bodied genera were stopped by smaller rivers than were largerbodied genera, but other analyses indicated no effect of body size. Genera with smaller geographic ranges crossed a smaller proportion of the rivers that they met than did genera with large ranges, implying a fundamental difference between smalland large-range taxa in their ability to disperse. Specialization might be the operating trait. Finally, although the aridity of the biogeographic gap in west Africa, the Dahomey Gap, is often argued to be the barrier to movement of species across the Gap, we found that for half the primate species that do not cross the Gap, the rivers that border the Gap could in fact be the effective barrier.
The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize on recent technological advances to improve remote sensing methods and generate more accurate, reliable, and scalable approaches to coffee mapping. In this study, we provide a systematic review of satellite-based approaches to mapping coffee extent, which produced 43 articles in the peer-reviewed and gray literature. We outline key considerations for employing effective approaches, focused on the need to balance data affordability and quality, classification complexity and accuracy, and generalizability and site-specificity. We discuss research opportunities for improved approaches by leveraging the recent expansion of diverse satellite sensors and constellations, optical/Synthetic Aperture Radar data fusion approaches, and advances in cloud computing and deep learning algorithms. We highlight the need for differentiating between production systems and the need for research in important coffee-growing geographies. By reviewing the range of techniques successfully used to map coffee extent, we provide technical recommendations and future directions to enable accurate and scalable coffee maps.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.