Open AccessUsing a novel model approach to assess the distribution and conservation status of the endangered Baird's tapir
Author(s) -
Schank Cody J.,
Cove Michael V.,
Kelly Marcella J.,
Mendoza Eduardo,
O'Farrill Georgina,
ReynaHurtado Rafael,
Meyer Ni,
Jordan Christopher A.,
GonzálezMaya Jose F.,
Lizcano Diego J.,
Moreno Ricardo,
Dobbins Michael T.,
Montalvo Victor,
SáenzBolaños Carolina,
Jimenez Eduardo Carillo,
Estrada Nereyda,
Cruz Díaz Juan Carlos,
Saenz Joel,
Spínola Manuel,
Carver Andrew,
Fort Jessica,
Nielsen Clayton K.,
Botello Francisco,
Pozo Montuy Gilberto,
Rivero Marina,
de la Torre Jesús Antonio,
BrenesMora Esteban,
GodínezGómez Oscar,
Wood Margot A.,
Gilbert Jessica,
Miller Jennifer A.
Publication year - 2017
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12631
Subject(s) - poisson distribution , endangered species , species distribution , range (aeronautics) , poisson regression , population , land cover , covariate , geography , cartography , statistics , ecology , physical geography , habitat , mathematics , land use , biology , demography , engineering , sociology , aerospace engineering
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 presence‐only ( 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.