Assessing the Spatial Scale Effect of Anthropogenic Factors on Species Distribution

<div><p>Patch context is a way to describe the effect that the surroundings exert on a landscape patch. Despite anthropogenic context alteration may affect species distributions by reducing the accessibility to suitable patches, species distribution modelling have rarely accounted for its effects explicitly. We propose a general framework to statistically detect the occurrence and the extent of such a factor, by combining presence-only data, spatial distribution models and information-theoretic model selection procedures. After having established the spatial resolution of the analysis on the basis of the species characteristics, a measure of anthropogenic alteration that can be quantified at increasing distance from each patch has to be defined. Then the distribution of the species is modelled under competing hypotheses: H₀, assumes that the distribution is uninfluenced by the anthropogenic variables; H₁, assumes the effect of alteration at the species scale (resolution); and H₂, H₃ … H_n add the effect of context alteration at increasing radii. Models are compared using the Akaike Information Criterion to establish the best hypothesis, and consequently the occurrence (if any) and the spatial scale of the anthropogenic effect. As a study case we analysed the distribution data of two insular lizards (one endemic and one naturalised) using four alternative hypotheses: no alteration (H₀), alteration at the species scale (H₁), alteration at two context scales (H₂ and H₃). H₂ and H₃ performed better than H₀ and H₁, highlighting the importance of context alteration. H₂ performed better than H₃, setting the spatial scale of the context at 1 km. The two species respond differently to context alteration, the introduced lizard being more tolerant than the endemic one. The proposed approach supplies reliably and interpretable results, uses easily available data on species distribution, and allows the assessing of the spatial scale at which human disturbance produces the heaviest effects.</p> </div

Variation partitioning diagrams for the H₂ hypothesis for the two species.

<p>Circles represent variation explained by each factor (climate and topography, alteration, patch context). Numbers correspond to the percentage of variation associated to each circle subpart (pure, two-factor intersection, three factors intersection). The percentage associated to intersecting areas has not to be interpreted as interaction, but as a variation indifferently assignable to one or more factors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067573#B65" target="_blank">65</a>]. Values smaller than 0.01% are not shown.</p

BAM diagram.

<p>Simplified version of the Biotic-Abiotic-Movement diagram from Sauge et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067573#B16" target="_blank">16</a>]. “G” represents the geographic space. “B” is the part of G that presents the correct set of biotic conditions: in this simplified version B is assumed not to constrain species distribution [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067573#B3" target="_blank">3</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067573#B16" target="_blank">16</a>]. “A” is the part of G that holds suitable abiotic conditions. “M” represents the sub-area of G that has been accessible and explored by the species. The intersection between M and A defines the species distribution (G₀). G_I is the area that is potentially suitable, but has not been accessible to the species.</p

Response curve for the anthropogenic variable of the best model (H₂).

<p>A: marginal response curves for the variable “alteration” (suitability is obtained from the full model keeping constant all the variables but alteration). B: single variable response curves for the same variable (suitability is obtained from a model including only the variable “alteration”). C: marginal response curves for PC-fine (patch context alteration). D: single variable response curves for the same variable. Solid lines indicate the mean of ten cross validated models; dashed lines represent the minimum and maximum range of the response curves.</p

Maps of the competing SDM.

<p>A, B, C, D represent the models under hypotheses respectively H₀, H₁, H₂ and H₃ for <i>P</i><i>. siculus</i>. E, F, G, H represent the same hypotheses for <i>P</i><i>. tiliguerta</i>. Occurrences are also shown in separate maps.</p

Demographic and clinical characteristics of participants.

<p>Demographic and clinical characteristics of participants.</p

Games and objectives for each cognitive domain.

<p>Games and objectives for each cognitive domain.</p

Feasibility outcome measures, taken and adapted from Verhelst et al. (2017) [33].

<p>Feasibility outcome measures, taken and adapted from Verhelst et al. (2017) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199001#pone.0199001.ref033" target="_blank">33</a>].</p

CONSORT flowchart of participant enrollment, inclusion, and involvement.

<p>CONSORT flowchart of participant enrollment, inclusion, and involvement.</p

Screenshots of Lumosity Cognitive Training^TM games (http://www.lumosity.com).

<p>Legend. Top to bottom, left to right: Disillusion, Lost in Migration, Tidal Treasures, Speed Match, Raindrops.</p