Modeling the spatial structure of the endemic mara (Dolichotis patagonum) across modified landscapes
- Published
- Accepted
- Subject Areas
- Biogeography, Conservation Biology, Ecology, Population Biology
- Keywords
- distribution and abundance, Dolichotis patagonum, natural and anthropic factors, spatial models, Patagonia, Península Valdés
- Copyright
- © 2018 Antun et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Modeling the spatial structure of the endemic mara (Dolichotis patagonum) across modified landscapes. PeerJ Preprints 6:e27380v1 https://doi.org/10.7287/peerj.preprints.27380v1
Abstract
Across modified landscapes anthropic factors can affect habitat selection by animals and consequently their abundance and distribution patterns. The study of the spatial structure of wild populations is crucial to gain knowledge on species’ response to habitat quality, and a key for the design and implementation of conservation actions. This is particularly important for a low-density and widely distributed species such as the mara (Dolichotis patagonum), a large rodent endemic of Argentina across the Monte and Patagonian drylands where extensive sheep ranching predominates. We aimed to assess the spatial variation in the abundance of maras and to identify the natural and anthropic factors influencing the observed patterns in Península Valdés, a representative landscape of Patagonia. We conducted ground surveys during the austral autumn from 2015 to 2017. We built density surface models to account for the variation in mara abundance, and obtained a map of mara density at a resolution of 4 km2. We estimated an overall density of 0.93 maras.km-2 for the prediction area of 3476 km2. The location of ranch buildings, indicators of human presence, had a strong positive effect on the abundance of maras, while the significant contribution of the geographic longitude suggested that mara density increases with higher rainfall. Although human presence favored mara abundance, presumably by providing protection against predators, it is likely that the association could bring negative consequences for maras and other species. The use of spatial models allowed us to provide the first estimate of mara abundance at a landscape scale and its spatial variation at a high resolution. Our approach can contribute to the assessment of mara population abundance and the factors shaping its spatial structure elsewhere across the species range, all crucial attributes to identify and prioritize conservation actions.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Analysis of the variation coefficient of the Normalized Vegetation Index (CV NDVI)
The variation coefficient of the Normalized Vegetation Index (CV NDVI) was calculated in each pixel - of 250 m resolution - from MODIS MOD13Q1 satellite images of the period 2010 to 2014, available at https://lpdaac.usgs.gov. The average values of the CV NDVI in each segment (1.8 x 2 km2 see in the main paper the section “Density surface model (DSM)”) and in each cell (4 km2) of the prediction grid (see in the main paper the section “Abundance and variance estimation”) were calculated. The map of the spatial variation of the CV NDVI was constructed (Fig. SI.1.1a) and the boundaries of the vegetation units of Península Valdés - defined by Bertiller et al. (2017; Fig. SI.1.1b) - were superimposed (Fig. SI.1.1a). Then, the mean NDVI CV was calculated in each vegetation unit (Table SI.1.1). The behavior of the variable in each stratum was visualized by the 'box-plot' chart (Fig. SI.1.2), while the significant differences were evaluated by means of Wilcoxon rank sum test (Table SI.1.1).
Concurvity measures between smooth terms
As we described in the article, we evaluated concurvity measures between smooth terms throughout the model fitting procedure. Here we presented the pairwise concurvity measures by three related indices (worst, observed and estimated) for the base model of the Tweedie response distribution (Tables SI.2.1, SI.2.2 and SI.2.3), and for the final model selected (Tables SI.2.4, SI.2.5 and SI.2.6).
Spatial autocorrelation in the residuals
Spatial autocorrelation in the residuals was evaluated using the ‘dsm.cor’ function of the‘dsm’ package. As described in the article, the correlogram show a small amount of spatial autocorrelation in the residuals (Fig. SI3.1). The confidence interval increased in width as the number of lags increased.
Count data: number of observations, group size and segment coordinates
Segment data
Location and variables that define each segment