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Predicting the potential distribution of a non-native species can assist management efforts to mitigate impacts on recipient ecosystems. However, such predictions are lacking for marine species, such as the non-native regal demoiselle, Neopomacentrus cyanomos, that is currently expanding its distribution in the western Atlantic. We used correlative species distribution models with three common algorithms to predict suitable habitat for N. cyanomos in the region. We compared models developed using native, non-native, and global occurrences to differentiate drivers across separate ranges using a suite of 12 environmental characteristics. While final models included an ensemble of variables, the majority ranked the combined effect of temperature variables as a key predictor correlated with the distribution of N. cyanomos. Habitat suitability increased as water temperatures increased beyond 16 °C and where annual thermal ranges were greater than 10 °C at the shallowest depth with substrate within a study cell (~ 9.2 km2 resolution). Habitat suitability also increased where maximum surface temperatures were greater than 27 °C. In the non-native range, the proportion of reef available in each cell was another important variable increasing the suitable habitat for N. cyanomos. Our models predicted high habitat suitability for N. cyanomos throughout the Greater Caribbean, in higher latitudes along North and South American Atlantic coasts, in the eastern Pacific Ocean, and highlights key areas where managers can monitor and target potential removal efforts. The distribution of this non-native species is likely to continue expanding throughout the region with little known about potential implications on native communities. [ABSTRACT FROM AUTHOR]

Invasions by non-native plant species after fire can negatively affect important ecosystem services and lead to invasion-fire cycles that further degrade ecosystems. The relationship between fire and plant invasion is complex, and the risk of invasion varies greatly between functional types and across geographic scales. Here, we examined patterns and predictors of non-native plant invasion following fire across the western United States. We specifically analyzed how the abundance of non-native plants after fire was related to fire characteristics and environmental conditions, such as climate, soil, and topography, in 26,729 vegetation plots from government networks and individual studies. Non-native plant cover was higher in plots measured after wildfires compared to prescribed burns or unburned plots. The post-fire cover of non-native species varied by plant functional type, and only the cover of short-lived (i.e., annual and biennial) forbs and short-lived C3 grasses was significantly higher in burned plots compared to unburned plots. Cool-season short-lived grasses composed most of the non-native post-fire vegetation, with cheatgrass (Bromus tectorum) being the most recorded species in the dataset. Climate variables were the most influential predictors of the cover of non-native short-lived grasses and forbs after fires, with invasion being more common in areas with drier summers and a higher proportion of yearly precipitation falling in October through March. Models using future projected climate for mid (2041–2070) and end (2071–2100) of century showed a potential for increasing post-fire invasion risk at higher elevations and latitudes. These findings highlight priorities for mitigation, monitoring, and restoration efforts to reduce post-fire plant invasion risk across the western United States. [ABSTRACT FROM AUTHOR]

Context: An increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers. Objectives: The large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers. Methods: We synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management. Results: Eight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management. Conclusions: Accessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management. [ABSTRACT FROM AUTHOR]

Predicting where invasive plants are likely to spread and become abundant is critical for informing invasive plant management. Species distribution models are a key tool for informing the geography of invasion risk, but most distribution models are limited by their use of presence data, including no information on invader population abundance. In this study, we ask how habitat suitability varies for different levels of abundance for three invasive plants: stiltgrass (Microstegium vimineum), sericea lespedeza (Lespedeza cuneata), and privet (Ligustrum sinense). For each species, we used an ensemble distribution modeling approach to compare suitability for invasion estimated from subsets of point location data: all presences vs. locations with percent cover ≥ 1%, ≥ 5%, ≥ 10%, ≥ 25%, and ≥ 50%. For all species, the total area predicted as suitable for abundant populations was 32%–68% less than the area predicted as suitable for presence. For stiltgrass and sericea lespedeza, the area suitable for invasion decreased when predicted from higher levels of abundance, whereas for privet, suitable area was similar across abundance levels. Stiltgrass and sericea lespedeza are therefore likely to become highly abundant in a smaller portion of their ranges, while privet could become highly abundant anywhere it can establish at low abundance. Different environmental predictors explained suitability for presence versus abundance, suggesting the environmental niche associated with presence differs from that associated with high population abundance. Analyses of more species and growth forms are still needed, but our results combined with previous studies consistently show that fitting distribution models to point locations with ≥ 5–10% cover refines range maps and can produce a more targeted assessment of invasion risk. [ABSTRACT FROM AUTHOR]

Context: Anticipating where an invasive species could become abundant can help guide prevention and control efforts aimed at reducing invasion impacts. Information on potential abundance can be combined with information on the current status of an invasion to guide management towards currently uninvaded locations where the threat of invasion is high. Objectives: We aimed to support management by developing predictive maps of potential cover for cheatgrass (Bromus tectorum), a problematic invader that can transform plant communities. We integrated our predictions of potential abundance with mapped estimates of current cover to quantify invasion potential within lightly invaded areas. Methods: We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios. We developed a species-specific set of covariates and validated model performance using spatially and temporally independent data. Results: Potential cheatgrass abundance was higher in areas that had burned, at low elevations, and when fall germination conditions were more favorable. Our results highlight the extensive areas across the Great Basin where cheatgrass abundance could increase to levels that can alter fire behavior and cause other ecological impacts. Conclusions: We predict potential cheatgrass abundance to quantify relative invasion risk. Our model results provide high and low scenarios of cheatgrass abundance to guide resource allocation and planning efforts across shrubland ecosystems of the Great Basin that remain relatively uninvaded. Combining information on an invasive species' current and potential abundance can yield spatial predictions to guide resource allocation and management action. [ABSTRACT FROM AUTHOR]

Context: Developing species distribution models (SDMs) to detect invasive species cover and evaluate habitat suitability are high priorities for land managers.Objectives: We tested SDMs fit with different variable combinations to provide guidelines for future invasive species model development based on transferability between landscapes.Methods: Generalized linear model, boosted regression trees, multivariate adaptive regression splines, and Random Forests were fit with location data for high cheatgrass (Bromus tectorum) cover in situ for two post-burn sites independently using topographic indices, spectral indices derived from multiple dates of Landsat 8 satellite imagery, or both. Models developed for one site were applied to the other, using independent cheatgrass cover data from the respective ex situ site to test model transferability.Results: Fitted models were statistically robust and comparable when fit with at least 200 cover plots in situ and transferred to the ex situ site. Only the Random Forests models were robust when fit with a small number of cover plots in situ.Conclusions: Our study indicated spectral indices can be used in SDMs to estimate species cover across landscapes (e.g., both within the same Landsat scene and in an adjacent Landsat scene). Important considerations for transferability include the model employed, quantity of cover data used to train/test the models, and phenology of the species coupled with the timing of imagery. The results also suggest that when cover data are limited, SDMs fit with topographic indices are sufficient for evaluating cheatgrass habitat suitability in new post-disturbance landscapes; however, spectral indices can provide a more robust estimate for detection based on local phenology. [ABSTRACT FROM AUTHOR]

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions. [ABSTRACT FROM AUTHOR]

lternative energy development is increasing, potentially leading to negative impacts on wildlife populations already stressed by other factors. Resource managers require a scientifically based methodology to balance energy development and species conservation, so we investigated modeling habitat suitability using Maximum Entropy to develop maps that could be used with other information to help site energy developments. We selected one species of concern, the Lesser Prairie-Chicken (LPCH; Tympanuchus pallidicinctus) found on the southern Great Plains of North America, as our case study. LPCH populations have been declining and are potentially further impacted by energy development. We used LPCH lek locations in the state of Kansas along with several environmental and anthropogenic parameters to develop models that predict the probability of lek occurrence across the landscape. The models all performed well as indicated by the high test area under the curve (AUC) scores (all >0.9). The inclusion of anthropogenic parameters in models resulted in slightly better performance based on AUC values, indicating that anthropogenic features may impact LPCH lek habitat suitability. Given the positive model results, this methodology may provide additional guidance in designing future survey protocols, as well as siting of energy development in areas of marginal or unsuitable habitat for species of concern. This technique could help to standardize and quantify the impacts various developments have upon at-risk species. [ABSTRACT FROM AUTHOR]

Understanding the potential spread of invasive species is essential for land managers to prevent their establishment and restore impacted habitat. Habitat suitability modeling provides a tool for researchers and managers to understand the potential extent of invasive species spread. Our goal was to use habitat suitability modeling to map potential habitat of the riparian plant invader, Russian olive ( Elaeagnus angustifolia). Russian olive has invaded riparian habitat across North America and is continuing to expand its range. We compiled 11 disparate datasets for Russian olive presence locations ( n = 1,051 points and 139 polygons) in the western US and used Maximum entropy (Maxent) modeling to develop two habitat suitability maps for Russian olive in the western United States: one with coarse-scale water data and one with fine-scale water data. Our models were able to accurately predict current suitable Russian olive habitat (Coarse model: training AUC = 0.938, test AUC = 0.907; Fine model: training AUC = 0.923, test AUC = 0.885). Distance to water was the most important predictor for Russian olive presence in our coarse-scale water model, but it was only the fifth most important variable in the fine-scale model, suggesting that when water bodies are considered on a fine scale, Russian olive does not necessarily rely on water. Our model predicted that Russian olive has suitable habitat further west from its current distribution, expanding into the west coast and central North America. Our methodology proves useful for identifying potential future areas of invasion. Model results may be influenced by locations of cultivated individuals and sampling bias. Further study is needed to examine the potential for Russian olive to invade beyond its current range. Habitat suitability modeling provides an essential tool for enhancing our understanding of invasive species spread. [ABSTRACT FROM AUTHOR]

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 ). [ABSTRACT FROM AUTHOR]

Land managers need cost-effective and informative tools for non-native plant species management. Many local, state, and federal agencies adopted mapping systems designed to collect comparable data for the early detection and monitoring of non-native species. We compared mapping information to statistically rigorous, plot-based methods to better understand the benefits and compatibility of the two techniques. Mapping non-native species locations provided a species list, associated species distributions, and infested area for subjectively selected survey sites. The value of this information may be compromised by crude estimates of cover and incomplete or biased estimations of species distributions. Incorporating plot-based assessments guided by a stratified-random sample design provided a less biased description of non-native species distributions and increased the comparability of data over time and across regions for the inventory, monitoring, and management of non-native and native plant species. [ABSTRACT FROM AUTHOR]

Ecological forecasts of the extent and impacts of invasive species can inform conservation management decisions. Such forecasts are hampered by ecological uncertainties associated with non-analog conditions resulting from the introduction of an invader to an ecosystem. We developed a state-and-transition simulation model tied to a fire behavior model to simulate the spread of buffelgrass (Cenchrus ciliaris) in Saguaro National Park, AZ, USA over a 30-year period. The simulation models forecast the potential extent and impact of a buffelgrass invasion including size and frequency of fire events and displacement of saguaro cacti and other native species. Using simulation models allowed us to evaluate how model uncertainties affected forecasted landscape outcomes. We compared scenarios covering a range of parameter uncertainties including model initialization (landscape susceptibility to invasion) and expert-identified ecological uncertainties (buffelgrass patch infill rates and precipitation). Our simulations showed substantial differences in the amount of buffelgrass on the landscape and the size and frequency of fires for dry years with slow patch infill scenarios compared to wet years with fast patch infill scenarios. We identified uncertainty in buffelgrass patch infill rates as a key area for research to improve forecasts. Our approach could be used to investigate novel processes in other invaded systems. [ABSTRACT FROM AUTHOR]