Geology topics

Alycia W. Crall

Publications and source records attributed to Alycia W. Crall.

4 recordsLinked to original sources

USGS2015

Citizen science contributes to our knowledge of invasive plant species distributions

Citizen science is commonly cited as an effective approach to expand the scale of invasive species data collection and monitoring. However, researchers often hesitate to use these data due to concerns over data quality. In light of recent research on the quality of data collected by volunteers, we aimed to demonstrate the extent to which citizen science data can increase sampling coverage, fill gaps in species distributions, and improve habitat suitability models compared to professionally generated data sets used in isolation. We combined data sets from professionals and volunteers for five invasive plant species ( Alliaria petiolata , Berberis thunbergii , Cirsium palustre , Pastinaca sativa , Polygonum cuspidatum ) in portions of Wisconsin. Volunteers sampled counties not sampled by professionals for three of the five species. Volunteers also added presence locations within counties not included in professional data sets, especially in southern portions of the state where professional monitoring activities had been minimal. Volunteers made a significant contribution to the known distribution, environmental gradients sampled, and the habitat suitability of P. cuspidatum . Models generated with professional data sets for the other four species performed reasonably well according to AUC values (>0.76). The addition of volunteer data did not greatly change model performance (AUC > 0.79) but did change the suitability surface generated by the models, making them more realistic. Our findings underscore the need to merge data from multiple sources to improve knowledge of current species distributions, and to predict their movement under present and future environmental conditions. The efficiency and success of these approaches require that monitoring efforts involve multiple stakeholders in continuous collaboration via established monitoring networks.

Wisconsin↗

USGS2013

Using habitat suitability models to target invasive plant species surveys

Managers need new tools for detecting the movement and spread of nonnative, invasive species. Habitat suitability models are a popular tool for mapping the potential distribution of current invaders, but the ability of these models to prioritize monitoring efforts has not been tested in the field. We tested the utility of an iterative sampling design (i.e., models based on field observations used to guide subsequent field data collection to improve the model), hypothesizing that model performance would increase when new data were gathered from targeted sampling using criteria based on the initial model results. We also tested the ability of habitat suitability models to predict the spread of invasive species, hypothesizing that models would accurately predict occurrences in the field, and that the use of targeted sampling would detect more species with less sampling effort than a nontargeted approach. We tested these hypotheses on two species at the state scale ( Centaurea stoebe and Pastinaca sativa ) in Wisconsin (USA), and one genus at the regional scale ( Tamarix ) in the western United States. These initial data were merged with environmental data at 30-m 2 resolution for Wisconsin and 1-km 2 resolution for the western United States to produce our first iteration models. We stratified these initial models to target field sampling and compared our models and success at detecting our species of interest to other surveys being conducted during the same field season (i.e., nontargeted sampling). Although more data did not always improve our models based on correct classification rate (CCR), sensitivity, specificity, kappa, or area under the curve (AUC), our models generated from targeted sampling data always performed better than models generated from nontargeted data. For Wisconsin species, the model described actual locations in the field fairly well (kappa = 0.51, 0.19, P < 0.01), and targeted sampling did detect more species than nontargeted sampling with less sampling effort (χ 2 ) = 47.42, P < 0.01). From these findings, we conclude that habitat suitability models can be highly useful tools for guiding invasive species monitoring, and we support the use of an iterative sampling design for guiding such efforts.

Wisconsin↗

USGS2010

Bringing modeling to the masses: A web based system to predict potential species distributions

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Future Internet↗

USGS2006

Show me the numbers: What data currently exist for non-native species in the USA?

Non-native species continue to be introduced to the United States from other countries via trade and transportation, creating a growing need for early detection and rapid response to new invaders. It is therefore increasingly important to synthesize existing data on non-native species abundance and distributions. However, no comprehensive analysis of existing data has been undertaken for non-native species, and there have been few efforts to improve collaboration. We therefore conducted a survey to determine what datasets currently exist for non-native species in the US from county, state, multi-state region, national, and global scales. We identified 319 datasets and collected metadata for 79% of these. Through this study, we provide a better understanding of extant non-native species datasets and identify data gaps (ie taxonomic, spatial, and temporal) to help guide future survey, research, and predictive modeling efforts.

Frontiers in Ecology and the Environment↗