Supporting proactive management in the context of climate change: Prioritizaing range-shifting invasive plants based on impact

Non-native, invasive plants are projected to shift their ranges with climate change, creating hotspots of risk where a multitude of novel species may soon establish and spread. The Northeast U.S. is one such hotspot. However, because monitoring for novel species is costly, these range-shifting invasive plants need to be prioritized. Preventing negative impacts is a key goal of management, thus, comparing the potential impacts of range-shifting invasive species could inform this prioritization. Here, we adapted the Environmental Impacts Classification for Alien Taxa (EICAT) protocol to evaluate potential impacts of 100 invasive plants that could establish either currently or by 2050 in the states of New York, Massachusetts, Connecticut, or Rhode Island. We searched Web of Science for each species and identified papers reporting ecological, economic, human health, or agricultural impacts. We scored ecological impacts from 1 (‘minimal concern’) to 4 (‘major’) and socio-ecological impacts as present or absent. We evaluated 865 impact studies and categorized 20 species as high-impact, 36 as medium-impact, and 26 as low-impact. We further refined high-impact invasive species based on whether major impacts affect ecosystems found in Northeast U.S. and identified five high-priority species: Anthriscus caucalis, Arundo donax, Avena barbata, Ludwigia grandiflora, and Rubus ulmifolius. Additional research is needed for 18 data-deficient species, which had no studies reporting impacts. Identifying and prioritizing range-shifting invasive plants provides a unique opportunity for early detection and rapid response that targets future problem species before they can establish and spread. This research illustrates the feasibility of using impacts assessments on range-shifting invasive species in order to inform proactive policy and management

Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates

Background Although increasingly sophisticated environmental measures are being applied to species distributions models, the focus remains on using climatic data to provide estimates of habitat suitability. Climatic tolerance estimates based on expert knowledge are available for a wide range of plants via the USDA PLANTS database. We aim to test how climatic tolerance inferred from plant distribution records relates to tolerance estimated by experts. Further, we use this information to identify circumstances when species distributions are more likely to approximate climatic tolerance. Methods We compiled expert knowledge estimates of minimum and maximum precipitation and minimum temperature tolerance for over 1800 conservation plant species from the ‘plant characteristics’ information in the USDA PLANTS database. We derived climatic tolerance from distribution data downloaded from the Global Biodiversity and Information Facility (GBIF) and corresponding climate from WorldClim. We compared expert-derived climatic tolerance to empirical estimates to find the difference between their inferred climate niches (ΔCN), and tested whether ΔCN was influenced by growth form or range size. Results Climate niches calculated from distribution data were significantly broader than expert-based tolerance estimates (Mann-Whitney p values \u3c\u3c 0.001). The average plant could tolerate 24 mm lower minimum precipitation, 14 mm higher maximum precipitation, and 7° C lower minimum temperatures based on distribution data relative to expert-based tolerance estimates. Species with larger ranges had greater ΔCN for minimum precipitation and minimum temperature. For maximum precipitation and minimum temperature, forbs and grasses tended to have larger ΔCN while grasses and trees had larger ΔCN for minimum precipitation. Conclusion Our results show that distribution data are consistently broader than USDA PLANTS experts’ knowledge and likely provide more robust estimates of climatic tolerance, especially for widespread forbs and grasses. These findings suggest that widely available expert-based climatic tolerance estimates underrepresent species’ fundamental niche and likely fail to capture the realized niche

Using Expert Knowledge to Satisfy Data Needs:Mapping Invasive Plant Distributions in the Western United States

Lack of knowledge about the distributions of plant and animal species can severely hamper management efforts. For invasive plants, distribution and abundance data can inform early detection and rapid response (EDRR) programs aimed at treating initial infestations. These data can be used to create invasion risk models at landscape and regional scales. Further, regional maps of invasive plant abundance are useful for communicating the scope of the invasive species problem to the public and policymakers. Here, we present a set of regional distribution maps for 10 problematic invasive plants in the western United States, created from the expert knowledge of weed managers in over 300 counties. Invasive plant experts identified infestations on paper, and the results were digitized into a regional GIS. Over 40% of requests were returned, resulting in maps with good spatial coverage and distribution data suitable for assessing invasive plant abundance across the western United States. Cheatgrass (Bromus tectorum) and Canada thistle (Cirsium arvense) were the most abundant and widespread of the surveyed species; however, the high concentrations and broad spatial extents of other invasive plants, such as hounds tongue (Cynoglossum officinale), white top (Lepidium draba), and Dalmatian toadflax (Linaria dalmatica), highlight the ongoing problems invasive species pose for western ecosystems, rangelands, and croplands. These results reinforce the critical role that regional mapping efforts can play in assessing and communicating invasion risk. This study suggests that knowledge about plant invasions exists locally and that experts are willing to participate in regional efforts to compile that information

Regional Invasive Species & Climate Change Management Challenge: Prioritizing range-shifting invasive plants High-impact species coming to the Northeast

Prevention of new invasions is a cost-effective way to manage invasive species and is most effective when emerging invaders are identified and prioritized before they arrive. Climate change is projected to bring nearly 100 new invasive plants to the Northeast. However, these plants are likely to have different types of impacts, making some a higher concern than others. Here, we summarize the results of original RISCC research that identifies high priority, range-shifting invasive plants based on their potential impacts

Postoffer Pre-Placement Screening for Carpal Tunnel Syndrome in Newly Hired Manufacturing Workers

OBJECTIVE: We determined the predictive validity of a post-offer pre-placement (POPP) screen using nerve conduction velocity studies (NCV) to identify future cases of carpal tunnel syndrome (CTS). METHODS: A cohort of 1648 newly hired manufacturing production workers underwent baseline NCS, and were followed for 5 years. RESULTS: There was no association between abnormal POPP NCV results and incident CTS. Varying NCV diagnostic cut-offs did not improve predictive validity. Workers in jobs with high hand/wrist exposure showed greater risk of CTS than those in low exposed jobs (Relative Risk 2.82; 95% CI 1.52, 5.22). CONCLUSIONS: POPP screening seems ineffective as a preventive strategy for CTS

Frequency of invasive plant occurrence is not a suitable proxy for abundance in the Northeast United States

Measuring and predicting invasive plant abundance is critical for understanding impacts on ecosystems and economies. Although spatial abundance datasets remain rare, occurrence datasets are increasingly available across broad regional scales. We asked whether the frequency of these point occurrences can be used as a proxy for abundance of invasive plants. We compiled both occurrence and abundance data for 13 regionally important invasive plants in the northeast United States from herbarium records and several contributed distribution datasets. We integrated all available abundance information based on infested area, stem count, percent cover, or qualitative descriptions into abundance rankings ranging from 0 (absent) to 4 (highly abundant). Within equal-area grid cells of 800 m, we counted numbers of occurrence points and used ordinal regression to test whether higher densities of occurrence points increased the odds of a higher abundance ranking. We compiled a total of 86,854 occurrence points in 34,596 grid cells, of which 26,114 points (30%) within 11,976 cells (35%) had some form of abundance information. Eleven of the 13 species had a slight but significantly positive odds ratio; that is, more occurrence points of a species increased the odds that the species was abundant within the grid cell. However, the predictive ability of the models was poor (κ \u3c 0.2) for the majority of species. Additionally, most grid cells contained only one or two occurrence points, making it impossible to infer abundance in all but a few locations. These results suggest that currently available occurrence datasets do not effectively represent abundance, which could explain why many distribution models based on occurrence data are poor predictors of abundance. Increased efforts to consistently collect and report invasive species abundance, ideally estimating both infested area and average cover, are strongly needed for regional-scale assessments of potential abundance and associated impact