Current and future suitable habitat areas for Nasuella olivacea (Gray, 1865) in Colombia and Ecuador and analysis of its distribution across different land uses

Abstract

Nasuella olivacea is an endemic mammal from the Andes of Ecuador and Colombia. Due to its rarity, aspects about its natural history, ecology and distribution patterns are not well known, therefore, research is needed to generate knowledge about this carnivore and a first step is studying suitable habitat areas. We performed Ecological Niche Models and applied future climate change scenarios (2.6 and 8.5 RCP) to determine the potential distribution of this mammal in Colombia and Ecuador, with current and future climate change conditions; furthermore, we analysed its distribution along several land covers. We found that N. olivacea is likely to be found in areas where no records have been reported previously; likewise, climate change conditions would increase suitable distribution areas. Concerning land cover, 73.4% of N. olivacea potential distribution was located outside Protected Areas (PA), 46.1% in Forests and 40.3% in Agricultural Lands. These findings highlight the need to further research understudied species, furthering our understanding about distribution trends and responses to changing climatic conditions, as well as informig future PA designing. These are essential tools for supporting wildlife conservation plans, being applicable for rare species whose biology and ecology remain unknown.

Roadkill patterns in Latin American birds and mammals

“…Although variation in local abundance or density can explain, in part, the differences in roadkill rates (Caceres, 2011; Delgado‐V, 2007; Medrano‐Vizcaíno, 2015), rare species can also suffer high mortality. The western mountain coati ( Nasuella olivacea ), an elusive carnivore whose ecology is scarcely known (Medrano‐Vizcaíno & Gutiérrez‐Salazar, 2020), was the second most roadkilled species in Envigado‐Colombia (Delgado‐V, 2007).…”

Spotting what’s important: Priority areas, connectivity, and conservation of the Northern Tiger Cat (Leopardus tigrinus) in Colombia

“…For the assessment of the species potential distribution, we generated a SDM based on an ecological niche modeling approach [45] using the maximum entropy algorithm [46]. To define the calibration area of the SDM we obtained an updated polygon layer of the world´s ecoregions [47] as the Mobility area (M) for the species [48] and selected those ecoregions that had at least one record of the species after filtering and we narrowed-down all the inferences to the extent of those ecoregions [49]; all ecoregions selected correspond to those ecoregions associated with the high Andean ecosystems (S1 Fig) . We then obtained bioclimatic variables from Worldclim 1.4 [44], including those that represented best the variation for a species such as Northern Tiger Cat, and that have proved useful for modeling carnivore distributions [50,51]. Specifically, we used seven bioclimatic variables: Mean Annual Temperature (Bio1), Diurnal Mean range (Bio2), Temperature seasonality (Bio4), Annual precipitation (Bio12) and Precipitation seasonality (Bio14), Precipitation of the wettest month (Bio13) and Precipitation of the driest month (Bio14).…”

The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot

“…These 29 species included all primates with over 15 occurrence records (following post-spatial thinning, as described in the Modelling section), as well as the Andean bear (Tremarctos ornatus) [53]. To complement our dataset, we referenced recent publications [50,[54][55][56] to obtain occurrence data for two additional species, namely the olinguito (Bassaricyon neblina) and the western mountain coati (Nasuella olivacea), as their expert maps from BioModelos were still awaiting validation. By using this subset of mammal species, which encompasses a diverse range of environmental roles and requirements, and by incorporating validated occurrence data and expert-made range maps, our study offers a comprehensive assessment of the impact of our geodiversity modelling approach.…”