Predictive performance of <i>Latrodectus variolus</i> and <i>Sphodros niger</i> distribution models assessed through the receiver operating characteristic curve (AUC), correlation coefficients (COR), and true skill statistics (TSS).

<p>Values are the average (± SD) of 100 iterated models.</p

The importance of the six bioclimatic predictors used in habitat range models of <i>Latrodectus variolus</i> and <i>Sphodros niger</i>.

<p>The importance of the six bioclimatic predictors used in habitat range models of <i>Latrodectus variolus</i> and <i>Sphodros niger</i>.</p

Predicting the distribution of poorly-documented species, Northern black widow (<i>Latrodectus variolus</i>) and Black purse-web spider (<i>Sphodros niger</i>), using museum specimens and citizen science data

<div><p>Predicting species distributions requires substantial numbers of georeferenced occurrences and access to remotely sensed climate and land cover data. Reliable estimates of the distribution of most species are unavailable, either because digitized georeferenced distributional data are rare or not digitized. The emergence of online biodiversity information databases and citizen science platforms dramatically improves the amount of information available to establish current and historical distribution of lesser-documented species. We demonstrate how the combination of museum and online citizen science databases can be used to build reliable distribution maps for poorly documented species. To do so, we investigated the distribution and the potential range expansions of two north-eastern North American spider species (Arachnida: Araneae), the Northern black widow (<i>Latrodectus variolus</i>) and the Black purse-web spider (S<i>phodros niger</i>). Our results provide the first predictions of distribution for these two species. We also found that the Northern black widow has expanded north of its previously known range providing valuable information for public health education. For the Black purse-web spider, we identify potential habitats outside of its currently known range, thus providing a better understanding of the ecology of this poorly-documented species. We demonstrate that increasingly available online biodiversity databases are rapidly expanding biogeography research for conservation, ecology, and in specific cases, epidemiology, of lesser known taxa.</p></div

Suitable climatic habitat of <i>Latrodectus variolus</i> predicted from 1990–2016 observation records.

<p>Observation records are shown for both 1960–1989 and 1990–2016.</p

Summary of occurrence data available for <i>Sphodros niger</i> and <i>Latrodectus variolus</i> distribution models, including the sources of data, their period of collection, and sample sizes.

<p>Other sources include private collections, personal observations, and news articles.</p

Suitable climatic habitat of <i>Sphodros niger</i> predicted from 1960–1989 and 1990–2015 observation records.

<p>Observation records are shown for both 1960–1989 and 1990–2015.</p

Occurrences data for three Northeastern-American tree species

Occurrences data for three Northeastern-American tree specie

Scatter plots showing uncertainty arising from an arbitrary selection of AOGCMs.

<p>Scatter plots (a), (b), and (c) show the projected habitat losses and gains obtained under each ensemble forecasting realized with one to nine AOGCMs for <i>Fagus grandifolia</i>, <i>Pinus rigida</i>, and <i>Quercus marilandica</i>, respectively (dashed lines show average projected losses and gains). Scatter plot (d) represents differences between maximum and minimum projected losses (dashed lines) and between maximum and minimum projected gains (solid lines) for <i>Fagus grandifolia</i> (circles), <i>Pinus rigida</i> (squares), and <i>Quercus marilandica</i> (triangles) using one to eight AOGCMs.</p

Projected climate for three biologically-relevant variables.

<p>Graphs show probability density functions of projected climate for mean annual temperature (first column), total annual precipitation (second column) and useful precipitation (third column). The 27 climate change scenarios are plotted as gray lines. The solid and dashed black lines represent the 10^th percentile values (top row), the average values (mid row), and the 90^th percentile values (bottom row) calculated on each cell across the 27 climate change scenarios (solid lines) or the six climate change scenarios selected by the k-means algorithm (dashed lines).</p

Effects of k-means clustering on potential future species distributions.

<p>Maps show differences between the projected climatic habitat distributions (2071–2100) obtained under an ensemble forecasting with the 27 climate change scenarios and an ensemble forecasting with the six climate change scenarios selected by the k-means algorithm for the three tree species.</p