A Probabilistic Co-Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns.

Characterizing potential spatial overlap between federally threatened and endangered ("listed") species distributions and registered pesticide use patterns is important for accurate risk assessment of threatened and endangered species. Because accurate range information for such rare species is often limited and agricultural pesticide use patterns are dynamic, simple spatial co-occurrence methods may overestimate or underestimate overlap and result in decisions that benefit neither listed species nor the regulatory process. Here, we demonstrate a new method of co-occurrence analysis that employs probability theory to estimate spatial distribution of rare species populations and areas of pesticide use to determine the likelihood of potential exposure. Specifically, we 1) describe a probabilistic method to estimate pesticide use based on crop production patterns; 2) construct species distribution models for 2 listed insect species whose ranges were previously incompletely described, the rusty-patched bumble bee (Bombus affinis) and the Poweshiek skipperling (Oarisma poweshiek); and 3) develop a probabilistic co-occurrence methodology and assessment framework. Using the principles of the Bayes' theorem, we constructed probabilistic spatial models of pesticide use areas by integrating information from land-cover spatial data, agriculture statistics, and remote-sensing data. We used maximum entropy methods to build species distribution models for 2 listed insects based on species collection and observation records and predictor variables relevant to the species' biogeography and natural history. We further developed novel methods for refinement of these models at spatial scales relevant to US Fish and Wildlife Service (FWS) regulatory priorities (e.g., critical habitat areas). Integrating both probabilistic assessments and focusing on USFWS priority management areas, we demonstrate that spatial overlap (i.e., potential for exposure) is not deterministic but instead a function of both species distribution and land use patterns. Our work serves as a framework to enhance the accuracy and efficiency of threatened and endangered species assessments using a data-driven likelihood analysis of species co-occurrence. Integr Environ Assess Manag 2019;00:1-12. © 2019 SETAC.

A refined ecological risk assessment for California red-legged frog, Delta smelt, and California tiger salamander exposed to malathion.

The California red-legged frog (CRLF), Delta smelt (DS), and California tiger salamander (CTS) are 3 species listed under the United States Federal Endangered Species Act (ESA), all of which inhabit aquatic ecosystems in California. The US Environmental Protection Agency (USEPA) has conducted deterministic screening-level risk assessments for these species potentially exposed to malathion, an organophosphorus insecticide and acaricide. Results from our screening-level analyses identified potential risk of direct effects to DS as well as indirect effects to all 3 species via reduction in prey. Accordingly, for those species and scenarios in which risk was identified at the screening level, we conducted a refined probabilistic risk assessment for CRLF, DS, and CTS. The refined ecological risk assessment (ERA) was conducted using best available data and approaches, as recommended by the 2013 National Research Council (NRC) report "Assessing Risks to Endangered and Threatened Species from Pesticides." Refined aquatic exposure models including the Pesticide Root Zone Model (PRZM), the Vegetative Filter Strip Modeling System (VFSMOD), the Variable Volume Water Model (VVWM), the Exposure Analysis Modeling System (EXAMS), and the Soil and Water Assessment Tool (SWAT) were used to generate estimated exposure concentrations (EECs) for malathion based on worst-case scenarios in California. Refined effects analyses involved developing concentration-response curves for fish and species sensitivity distributions (SSDs) for fish and aquatic invertebrates. Quantitative risk curves, field and mesocosm studies, surface-water monitoring data, and incident reports were considered in a weight-of-evidence approach. Currently, labeled uses of malathion are not expected to result in direct effects to CRLF, DS or CTS, or indirect effects due to effects on fish and invertebrate prey. Integr Environ Assess Manag 2018;14:224-239. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A probabilistic approach for estimating the spatial extent of pesticide agricultural use sites and potential co-occurrence with listed species for use in ecological risk assessments.

A crop footprint refers to the estimated spatial extent of growing areas for a specific crop, and is commonly used to represent the potential "use site" footprint for a pesticide labeled for use on that crop. A methodology for developing probabilistic crop footprints to estimate the likelihood of pesticide use and the potential co-occurrence of pesticide use and listed species locations was tested at the national scale and compared to alternative methods. The probabilistic aspect of the approach accounts for annual crop rotations and the uncertainty in remotely sensed crop and land cover data sets. The crop footprints used historically are derived exclusively from the National Land Cover Database (NLCD) Cultivated Crops and/or Pasture/Hay classes. This approach broadly aggregates agriculture into 2 classes, which grossly overestimates the spatial extent of individual crops that are labeled for pesticide use. The approach also does not use all the available crop data, represents a single point in time, and does not account for the uncertainty in land cover data set classifications. The probabilistic crop footprint approach described herein incorporates best available information at the time of analysis from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) for 5 y (2008-2012 at the time of analysis), the 2006 NLCD, the 2007 NASS Census of Agriculture, and 5 y of NASS Quick Stats (2008-2012). The approach accounts for misclassification of crop classes in the CDL by incorporating accuracy assessment information by state, year, and crop. The NLCD provides additional information to improve the CDL crop probability through an adjustment based on the NLCD accuracy assessment data using the principles of Bayes' Theorem. Finally, crop probabilities are scaled at the state level by comparing against NASS surveys (Census of Agriculture and Quick Stats) of reported planted acres by crop. In an example application of the new method, the probabilistic crop footprint for soybean resulted in national and statewide soybean acreages that are within the error bounds of the average reported NASS yearly soybean acreage over the same time period, whereas the method using only NLCD resulted in an acreage that is over 4 times the survey acreage. When the probabilistic crop footprint for soybean was used in a co-occurrence analysis with listed species locations, the number of potentially proximal species identified was half the number based on the standard NLCD crop footprint method (276 species with the probabilistic crop footprint vs 511 for the conventional method). The probabilistic crop footprint methodology allows for a more comprehensive and representative understanding of the potential pesticide use footprint co-occurrence with endangered species locations for use in effects determinations.

Ecosystem legacy of the introduced N2-fixing tree Robinia pseudoacacia in a coastal forest.

Habitat invasibility has been found to increase dramatically following the alteration of ecosystem properties by a nonnative species. Robinia pseudoacacia, black locust, is a nitrogen-fixing, clonal tree species that aggressively invades open habitats and expands outside of plantations worldwide. Robinia pseudoacacia stands in Cape Cod National Seashore were particularly susceptible to a hurricane in 1991 that caused widespread blowdown and a dramatic reduction in Robinia in some stands. We used this change to investigate the lasting ecological effects of this nonnative species on this upland coastal ecosystem. We established replicate clusters of 20 × 20 m field plots within 50 m of each other that contained native pitch pine (Pinus rigida) and oak (Quercus velutina, Q. alba) forest, living Robinia stands, and stands in which Robinia was eliminated or reduced to less than 5% cover by the hurricane. Net nitrification and extractable soil nitrate concentration differed significantly between stand types, in the order Robinia > former Robinia > pine-oak. Nonnative species cover differed significantly between each stand type, in the order Robinia > former Robinia > pine-oak. Invasion of Robinia pseudoacacia increased soil net nitrification and nitrogen availability and precipitated a change in forest species composition that favored nonnative species. The presence of elevated soil nitrogen and nonnative species persisted at least 14 years after the removal of the original invading tree species, suggesting that the invasion of a tree species left a legacy of altered soil biogeochemistry, a higher number of nonnative species, and greater nonnative species cover.