The pet trade threatens wild capuchin monkey individuals and populations across Brazil. Monkeys formerly kept as pets are sent to or surrendered to rescue centers to be rehabilitated and then eventually released back into the wild. It is necessary for the survival of these monkeys to find suitable habitats for their release. Suitable release sites must contain food, water, and shelter and be relatively far from areas of human development. At the same time, release sites must consider resident conspecifics, other animal species, and humans that may be impacted by the release of capuchin monkeys. In collaboration with Dr. Renata Ferreira and her team (Operation Sapajus), in this study I aimed to identify and map suitable areas for reintroduction of captive capuchin monkeys into the Caatinga biome in Brazil. First, I discussed habitat suitability criteria with Dr. Ferreira, relying on her expert knowledge, we created a list of important habitat variables to consider in my models. I used a species distribution model to model suitable areas for two species of palm tree (Syagrus romanzoffiana and Attalea speciosa) which are key resources for the capuchin monkeys. Then, I used a multi-criteria decision analysis (MCDA) model to model suitable release areas with respect to palm tree suitability, water, protected areas, and distance to roads and cities. The result of the analysis is a capuchin habitat suitability map covering the entire Caatinga biome. This map will contribute directly to capuchin monkey conservation by providing Dr. Ferreira with potential sites to release capuchin monkeys. I coded the results of this analysis into a Google Earth Engine app, making them easily and freely accessible to the researchers on the ground in Brazil. MCDA models such as the one used in this study can be applied across primate taxa for the purposes of reintroductions, translocations, predicting species presence, or for identifying potential conservation areas. This study adds valuable insights into primate habitat suitability mapping and to the applied sustainable conservation of capuchin monkeys at a time when biodiversity loss, the exotic pet trade, land use change, and climate change are threatening non-human primates around the world.
The landscape between the Adirondack Mountains in New York and the Laurentian Mountains in Québec is one of three potential north-south transboundary wildlife movement linkages that connect wilderness areas in northeastern USA and southeastern Canada. Although this region still maintains habitats of high ecological integrity and biodiversity, increasing land-cover change and fragmentation is putting landscape connectivity at risk. We measured changes in landscape composition and configuration within the Adirondack-to-Laurentians transboundary wildlife linkage (A2L) between 1992 and 2018 to identify priority areas for conservation and restoration. Land-cover change was calculated by measuring area and proportion of land-cover classes, and landscape fragmentation was determined by measuring patch number, mean patch size, the effective mesh size, and road density, at three spatial scales and four fragmentation geometries (i.e., combinations of fragmenting elements). Extensive changes in land-cover and landscape fragmentation occurred within the A2L between 1992 and 2018. Forest areas declined by 1363 km2 and wetlands declined by 1365 km2 (69%). This was most pronounced in the Québec portion of the A2L where wetland areas declined by 872 km2 (88.5%). Forest areas in the Québec portion experienced the greatest amount of fragmentation with a meff_CUT decline of 3262.5 km2 (58.5%) since 2000. Monitoring of land-cover changes and landscape fragmentation is an effective way to identify priority areas for conservation. Strengthening conservation strategies that enhance landscape connectivity and protect ecosystems at the local level, will improve post-2020 biodiversity commitments at the national and transboundary levels.
All seven turtle species living in Quebec are at risk. Accordingly, it is urgent an urgent task to identify potential high-risk areas of road mortality for turtles across Quebec. This information can serve to prioritize locations for the installation of mitigation measures. Our study identified predictor variables that are related to zones of potential high turtle presence along roads in Quebec. It determined four statistical models that best predict these high-turtle presence zones and identified the scales at which land-cover variables best predict turtle presence. We applied these models to predict turtle presence for all 100-m road segments along Quebec’s road network within the turtle range (1,700,424 road segments between 90 m and 100 m). Using observations from the Banque d’Observations des Reptiles et des Amphibiens au Québec (BORAQ), we identified 2583 road segments with at least one turtle count. We considered 16 environmental variables and four structural road variables in the statistical models. For landcover variables, five buffer distances were considered (between 100 m and 500 m). The four best models (using AIC) include between 11 and 19 predictor variables, and their coefficients have the directions as expected from an ecological perspective. The models are similarly good at making predictions, and their predictions are generally in good agreement. They predict a higher likelihood of turtle presence on road segments located near turtle habitat. The scales at which the landcover variables best predicted turtle presence differed between the landcover types and models. The predictions from the four best models are stored in a geodatabase and presented in an interactive website that navigates the roads of Quebec. One of the website’s maps displays clusters of turtle segments where mortality risk is above certain prediction thresholds. It can inform users about areas where mitigation should be prioritized.