Localized hotspots drive continental geography of abnormal amphibians on U.S. wildlife refuges.

Amphibians with missing, misshapen, and extra limbs have garnered public and scientific attention for two decades, yet the extent of the phenomenon remains poorly understood. Despite progress in identifying the causes of abnormalities in some regions, a lack of knowledge about their broader spatial distribution and temporal dynamics has hindered efforts to understand their implications for amphibian population declines and environmental quality. To address this data gap, we conducted a nationwide, 10-year assessment of 62,947 amphibians on U.S. National Wildlife Refuges. Analysis of a core dataset of 48,081 individuals revealed that consistent with expected background frequencies, an average of 2% were abnormal, but abnormalities exhibited marked spatial variation with a maximum prevalence of 40%. Variance partitioning analysis demonstrated that factors associated with space (rather than species or year sampled) captured 97% of the variation in abnormalities, and the amount of partitioned variance decreased with increasing spatial scale (from site to refuge to region). Consistent with this, abnormalities occurred in local to regional hotspots, clustering at scales of tens to hundreds of kilometers. We detected such hotspot clusters of high-abnormality sites in the Mississippi River Valley, California, and Alaska. Abnormality frequency was more variable within than outside of hotspot clusters. This is consistent with dynamic phenomena such as disturbance or natural enemies (pathogens or predators), whereas similarity of abnormality frequencies at scales of tens to hundreds of kilometers suggests involvement of factors that are spatially consistent at a regional scale. Our characterization of the spatial and temporal variation inherent in continent-wide amphibian abnormalities demonstrates the disproportionate contribution of local factors in predicting hotspots, and the episodic nature of their occurrence

Data from: Localized hotspots drive continental geography of abnormal amphibians on U.S. wildlife refuges

Amphibians with missing, misshapen, and extra limbs have garnered public and scientific attention for two decades, yet the extent of the phenomenon remains poorly understood. Despite progress in identifying the causes of abnormalities in some regions, a lack of knowledge about their broader spatial distribution and temporal dynamics has hindered efforts to understand their implications for amphibian population declines and environmental quality. To address this data gap, we conducted a nationwide, 10-year assessment of 62,947 amphibians on U.S. National Wildlife Refuges. Analysis of a core dataset of 48,081 individuals revealed that consistent with expected background frequencies, an average of 2% were abnormal, but abnormalities exhibited marked spatial variation with a maximum prevalence of 40%. Variance partitioning analysis demonstrated that factors associated with space (rather than species or year sampled) captured 97% of the variation in abnormalities, and the amount of partitioned variance decreased with increasing spatial scale (from site to refuge to region). Consistent with this, abnormalities occurred in local to regional hotspots, clustering at scales of tens to hundreds of kilometers. We detected such hotspot clusters of high-abnormality sites in the Mississippi River Valley, California, and Alaska. Abnormality frequency was more variable within than outside of hotspot clusters. This is consistent with dynamic phenomena such as disturbance or natural enemies (pathogens or predators), whereas similarity of abnormality frequencies at scales of tens to hundreds of kilometers suggests involvement of factors that are spatially consistent at a regional scale. Our characterization of the spatial and temporal variation inherent in continent-wide amphibian abnormalities demonstrates the disproportionate contribution of local factors in predicting hotspots, and the episodic nature of their occurrence

SpeciesNames

This is a link file from the species codes used in the other data files to the scientific and common names for each species

Core Dataset

File Information The Core_Dataset.csv file includes information on the 675 collection events included in most statistical analyses presented in our PLoS One paper. See methods section of the paper for the steps by which we created the "Core Dataset" from the "All_Collections" dataset. Information on each column is included in the ReadMe file

All Collections

File Information The All_Collections.csv data set includes information on each of the 1,477 individual collection events included in this effort. Information on each column is included in the ReadMe file

Blank Data Sheets Used For Project

This excel file contains multiple tabs so that users may print the data sheets we used for the project. Additional information on how to use the sheets to collect data is included in our Field SOPs document

Differences in temporal variation between normal sites and hotspot clusters, measured by Taylor’s power law analysis.

<p>Sites in hotspot clusters (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077467#pone.0077467.s019" target="_blank">Text S1</a>) are coded with open red circles, non-hotspot sites are coded with black filled circles. The dashed red line is the regression of expected versus observed site-level variance for hotspot cluster sites following the equation ln(V_obs)=ln(<i>A</i>)+<i>b</i>ln(V_bin), where the expected variance was calculated with the following formula: V_bin=[<i>np</i>(1-<i>p</i>)], where <i>p</i> is the mean proportion abnormal at a site, and <i>n</i> is the group size, or number of frogs sampled at each site (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077467#pone.0077467.s019" target="_blank">Text S1</a>). Observed variance (V_obs) was calculated from repeated site visits using standard methods for estimating sample variance. For sites in hotspot clusters, the regression parameter estimates were ln(<i>A</i>)=0.84±0.36 (SEM) and <i>b</i>=1.48±0.28 (SEM). The slope of this line was significantly greater than one (t=1.73, with 39 df, p=0.046), implying significant aggregation, i.e., temporal variation dependent on the mean abnormality prevalence (<i>p</i>). For non-hotspot sites (solid black regression line) the parameter estimates for this relationship were ln(<i>A</i>)=0.81±0.18 (SEM) and <i>b</i>=0.98±0.18 (SEM). This slope was not significantly different than one (t=0.096, with 92 df, p=0.462) implying random temporal variation.</p