Catherine S. Jarnevich

Improving and integrating data on invasive species collected by citizen scientists

Limited resources make it difficult to effectively document, monitor, and control invasive species across large areas, resulting in large gaps in our knowledge of current and future invasion patterns. We surveyed 128 citizen science program coordinators and interviewed 15 of them to evaluate their potential role in filling these gaps. Many programs collect data on invasive species and are willing to contribute these data to public databases. Although resources for education and monitoring are readily available, groups generally lack tools to manage and analyze data. Potential users of these data also retain concerns over data quality. We discuss how to address these concerns about citizen scientist data and programs while preserving the advantages they afford. A unified yet flexible national citizen science program aimed at tracking invasive species location, abundance, and control efforts could be designed using centralized data sharing and management tools. Such a system could meet the needs of multiple stakeholders while allowing efficiencies of scale, greater standardization of methods, and improved data quality testing and sharing. Finally, we present a prototype for such a system (see www.citsci.org).

Ensemble habitat mapping of invasive plant species.

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis.

A tamarisk habitat suitability map for the continental United States

This paper presents a national-scale map of habitat suitability for tamarisk (Tamarix spp, salt cedar), a high-priority invasive species. We successfully integrate satellite data and tens of thousands of field sampling points through logistic regression modeling to create a habitat suitability map that is 90% accurate. This interagency effort uses field data collected and coordinated through the US Geological Survey and nationwide environmental data layers derived from NASAs MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate the use of the map by ranking the 48 continental US states (and the District of Columbia) based on their absolute, as well as proportional, areas of “highly likely” and “moderately likely” habitat for Tamarix. The interagency effort and modeling approach presented here could be used to map other harmful species, in the US and globally.

What parts of the US mainland are climatically suitable for invasive alien pythons spreading from Everglades National Park

The Burmese Python (Python molurus bivittatus) is now well established in southern Florida and spreading northward. The factors likely to limit this spread are unknown, but presumably include climate or are correlated with climate. We compiled monthly rainfall and temperature statistics from 149 stations located near the edge of the python’s native range in Asia (Pakistan east to China and south to Indonesia). The southern and eastern native range limits extend to saltwater, leaving unresolved the species’ climatic tolerances in those areas. The northern and western limits are associated with cold and aridity respectively. We plotted mean monthly rainfall against mean monthly temperature for the 149 native range weather stations to identify the climate conditions inhabited by pythons in their native range, and mapped areas of the coterminous United States with the same climate today and projected for the year 2100. We accounted for both dry-season aestivation and winter hibernation (under two scenarios of hibernation duration). The potential distribution was relatively insensitive to choice of scenario for hibernation duration. US areas climatically matched at present ranged up the coasts and across the south from Delaware to Oregon, and included most of California, Texas, Oklahoma, Arkansas, Louisiana, Mississippi, Alabama, Florida, Georgia, and South and North Carolina. By the year 2100, projected areas of potential suitable climate extend northward beyond the current limit to include parts of the states of Washington, Colorado, Illinois, Indiana, Ohio, West Virginia, Pennsylvania, New Jersey, and New York. Thus a substantial portion of the mainland US is potentially vulnerable to this ostensibly tropical invader.

Distribution and Abundance of Saltcedar and Russian Olive in the Western United States

Over the past century, two introduced Eurasian trees, saltcedar (Tamarix spp.) and Russian olive (Elaeagnus angustifolia) have become wide spread on western United States of American (U.S.) rivers. This paper reviews the literature on the following five key areas related to their distribution and abundance in the western United States: (1) the history of introduction, planting, and spread of saltcedar and Russian olive; (2) their current distribution; (3) their current abundance; (4) factors controlling their current distribution and abundance; and (5) models that have been developed to predict their future distribution and abundance. Saltcedar and Russian olive are now the third and fourth most frequently occurring woody riparian plants and the second and fifth most abundant species (out of 42 native and non-native species) along rivers in the western United States. Currently there is not a precise estimate of the areas that these species occupy in the entire West. Climatic variables are important determinants of their distribution and abundance. For example, saltcedar is limited by its sensitivity to hard freezes, whereas Russian olive appears to have a chilling requirement for bud break and seed germination, and can presumably survive colder winter temperatures. Either species can be dominant, co-dominant or sub-dominant relative to native species on a given river system. A number of environmental factors such as water availability, soil salinity, degree of streamflow regulation, and fire frequency can influence the abundance of these species relative to native species. Numerous studies suggest that both species have spread on western rivers primarily through a replacement process, whereby stress-tolerant species have moved into expanded niches that are no longer suitable for mesic native pioneer species. Better maps of current distribution and rigorous monitoring of distributional changes though time can help to resolve differences in predictions of potential future spread. An adequate understanding does not yet exist of what fraction of western riparian zones is resistant to dominance by either of these species, what fraction is at risk and could benefit from intervention, and what fraction has been altered to the point that saltcedar or Russian olive are most likely to thrive.

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.

Invasive species information networks: collaboration at multiple scales for prevention, early detection, and rapid response to invasive alien species

Abstract Accurate analysis of present distributions and effective modeling of future distributions of invasive alien species (IAS) are both highly dependent on the availability and accessibility of occurrence data and natural history information about the species. Invasive alien species monitoring and detection networks (such as the Invasive Plant Atlas of New England and the Invasive Plant Atlas of the MidSouth) generate occurrence data at local and regional levels within the United States, which are shared through the US National Institute of Invasive Species Science. The Inter-American Biodiversity Information Networks Invasives Information Network (I3N), facilitates cooperation on sharing invasive species occurrence data throughout the Western Hemisphere. The I3N and other national and regional networks expose their data globally via the Global Invasive Species Information Network (GISIN). International and interdisciplinary cooperation on data sharing strengthens cooperation on strategies and responses to invasions. However, limitations to effective collaboration among invasive species networks leading to successful early detection and rapid response to invasive species include: lack of interoperability; data accessibility; funding; and technical expertise. This paper proposes various solutions to these obstacles at different geographic levels and briefly describes success stories from the invasive species information networks mentioned above. Using biological informatics to facilitate global information sharing is especially critical in invasive species science, as research has shown that one of the best indicators of the invasiveness of a species is whether it has been invasive elsewhere. Data must also be shared across disciplines because natural history information (e.g. diet, predators, habitat requirements, etc.) about a species in its native range is vital for effective prevention, detection, and rapid response to an invasion. Finally, it has been our experience that sharing information, including invasive species dispersal mechanisms and rates, impacts, and prevention and control strategies, enables resource managers and decision-makers to mount a more effective response to biological invasions

Caveats for correlative species distribution modeling

Abstract Correlative species distribution models are becoming commonplace in the scientific literature and public outreach products, displaying locations, abundance, or suitable environmental conditions for harmful invasive species, threatened and endangered species, or species of special concern. Accurate species distribution models are useful for efficient and adaptive management and conservation, research, and ecological forecasting. Yet, these models are often presented without fully examining or explaining the caveats for their proper use and interpretation and are often implemented without understanding the limitations and assumptions of the model being used. We describe common pitfalls, assumptions, and caveats of correlative species distribution models to help novice users and end users better interpret these models. Four primary caveats corresponding to different phases of the modeling process, each with supporting documentation and examples, include: (1) all sampling data are incomplete and potentially biased; (2) predictor variables must capture distribution constraints; (3) no single model works best for all species, in all areas, at all spatial scales, and over time; and (4) the results of species distribution models should be treated like a hypothesis to be tested and validated with additional sampling and modeling in an iterative process.

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-m2 resolution for Wisconsin and 1-km2 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 (chi2 = 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.

Improving National-Scale Invasion Maps: Tamarisk in the Western United States

ABSTRACT. New invasions, better field data, and novel spatial-modeling techniques often drive the need to revisit previous maps and models of invasive species. Such is the case with the at least 10 species of Tamarix, which are invading riparian systems in the western United States and expanding their range throughout North America. In 2006, we developed a National Tamarisk Map by using a compilation of presence and absence locations with remotely sensed data and statistical modeling techniques. Since the publication of that work, our database of Tamarix distributions has grown significantly. Using the updated database of species occurrence, new predictor variables, and the maximum entropy (Maxent) model, we have revised our potential Tamarix distribution map for the western United States. Distance-to-water was the strongest predictor in the model (58.1%), while mean temperature of the warmest quarter was the second best predictor (18.4%). Model validation, averaged from 25 model iterations, indicated that our analysis had strong predictive performance (AUC = 0.93) and that the extent of Tamarix distributions is much greater than previously thought. The southwestern United States had the greatest suitable habitat, and this result differed from the 2006 model. Our work highlights the utility of iterative modeling for invasive species habitat modeling as new information becomes available.