Geology topics

Nathan M. Tarr

Publications and source records attributed to Nathan M. Tarr.

4 recordsLinked to original sources

USGS2022

Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data

The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random. In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.

Alabama, Georgia, North Carolina, Tennessee, Virgi↗

USGS2019

Gap Analysis Project (GAP) Terrestrial Vertebrate Species Richness Maps for the Conterminous U.S.

The mission of the Gap Analysis Project (GAP) is to support national and regional assessments of the conservation status of vertebrate species and plant communities. This report explains conterminous United States species richness maps created by the U.S. Geological Survey for four major classes in the phylum Chordata: mammals, birds, reptiles, and amphibians. In this work, we focus on terrestrial vertebrate species and the spatial patterns of richness derived from species’ habitat distribution models. We created species’ habitat distribution models for 1,590 species (282 amphibians, 621 birds, 365 mammals, 322 reptiles) and an additional 129 subspecies (2 amphibians, 28 birds, 94 mammals, 5 reptiles) that occur in the conterminous United States. The 1,590 species level models were spatially combined to create the taxa richness maps at a spatial resolution of 30 meters. Based on those maps we identified the maximum species richness for each of the taxa (43 amphibians, 163 birds, 72 mammals, and 54 reptiles) and show variation in richness across the conterminous United States. Because these habitat models remove unsuitable areas within the range of the species, the patterns of richness presented here are different from the coarse-resolution species’ habitat distribution models commonly presented in the literature. These maps provide a new, more spatially refined richness map. In addition, since these models are logically linked to mapped data layers that constitute habitat suitability, this suite of data can provide an intuitive data system for further exploration of biodiversity and implications for change at ecosystem and landscape scales.

Scientific Investigations Report↗

USGS2018

Patterns of species richness hotspots and estimates of their protection are sensitive to spatial resolution

Aim Species richness is a measure of biodiversity often used in spatial conservation assessments and mapped by summing species distribution maps. Commission errors inherent those maps influence richness patterns and conservation assessments. We sought to further the understanding of the sensitivity of hotspot delineation methods and conservation assessments to commission errors, and choice of threshold for hotspot delineation. Location United States. Methods We created range maps and 30‐m and 1‐km resolution habitat maps for terrestrial vertebrates in the United States and generated species richness maps with each dataset. With the richness maps and the GAP Protected Areas Dataset, we created species richness hotspot maps and calculated the proportion of hotspots within protected areas; calculating protection under a range of thresholds for defining hotspots. Our method allowed us to identify the influence of commission errors by comparing hotspot maps. Results Commission errors from coarse spatial grain data and lack of porosity in the range data inflated richness estimates and altered their spatial patterns. Coincidence of hotspots from different data types was low. The 30‐m hotspots were spatially dispersed, and some were very long distances from the hotspots mapped with coarser data. Estimates of protection were low for each of the taxa. The relationship between estimates of hotspot protection and threshold choice was nonlinear and inconsistent among data types (habitat and range) and grain size (30‐m and 1‐km). Main conclusions Coarse mapping methods and grain sizes can introduce commission errors into species distribution data that could result in misidentifications of the regions where hotspots occur and affect estimates of hotspot protection. Hotspot conservation assessments are also sensitive to choice of threshold for hotspot delineation. There is value in developing species distribution maps with high resolution and low rates of commission error for conservation assessments.

Diversity and Distributions↗

USGS2016

Projected gains and losses of wildlife habitat from bioenergy-induced landscape change

Domestic and foreign renewable energy targets and financial incentives have increased demand for woody biomass and bioenergy in the southeastern United States. This demand is expected to be met through purpose-grown agricultural bioenergy crops, short-rotation tree plantations, thinning and harvest of planted and natural forests, and forest harvest residues. With results from a forest economics model, spatially explicit state-and-transition simulation models, and species–habitat models, we projected change in habitat amount for 16 wildlife species caused by meeting a renewable fuel target and expected demand for wood pellets in North Carolina, USA. We projected changes over 40 years under a baseline ‘business-as-usual’ scenario without bioenergy production and five scenarios with unique feedstock portfolios. Bioenergy demand had potential to influence trends in habitat availability for some species in our study area. We found variation in impacts among species, and no scenario was the ‘best’ or ‘worst’ across all species. Our models projected that shrub-associated species would gain habitat under some scenarios because of increases in the amount of regenerating forests on the landscape, while species restricted to mature forests would lose habitat. Some forest species could also lose habitat from the conversion of forests on marginal soils to purpose-grown feedstocks. The conversion of agricultural lands on marginal soils to purpose-grown feedstocks increased habitat losses for one species with strong associations with pasture, which is being lost to urbanization in our study region. Our results indicate that landscape-scale impacts on wildlife habitat will vary among species and depend upon the bioenergy feedstock portfolio. Therefore, decisions about bioenergy and wildlife will likely involve trade-offs among wildlife species, and the choice of focal species is likely to affect the results of landscape-scale assessments. We offer general principals to consider when crafting lists of focal species for bioenergy impact assessments at the landscape scale.

GCB Bioenergy↗