Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling.

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our 'best' model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed

Habitat Modeling of Alien Plant Species at Varying Levels of Occupancy

Distribution models of invasive plants are very useful tools for conservation management. There are challenges in modeling expanding populations, especially in a dynamic environment, and when data are limited. In this paper, predictive habitat models were assessed for three invasive plant species, at differing levels of occurrence, using two different habitat modeling techniques: logistic regression and maximum entropy. The influence of disturbance, spatial and temporal heterogeneity, and other landscape characteristics is assessed by creating regional level models based on occurrence records from the USDA Forest Service’s Forest Inventory and Analysis database. Logistic regression and maximum entropy models were assessed independently. Ensemble models were developed to combine the predictions of the two analysis approaches to obtain a more robust prediction estimate. All species had strong models with Area Under the receiver operator Curve (AUC) of >0.75. The species with the highest occurrence, Ligustrum spp., had the greatest agreement between the models (93%). Lolium arundinaceum had the most disagreement between models at 33% and the lowest AUC values. Overall, the strength of integrative modeling in assessing and understanding habitat modeling was demonstrated

FRONTIERS IN INVASIVE SPECIES DISTRIBUTION MODELING (iSDM): ASSESSING EFFECTS OF ABSENCE DATA, DISPERSAL CONSTRAINTS, STAGE OF INVASION AND SPATIAL DEPENDENCE

Successful management of biological invasions depends heavily on our ability to predict their geographic ranges and potential habitats. Species distribution modeling (SDM) provides a methodological framework to predict spatial distributions of organisms but the unique aspects of modeling invasive species have been largely ignored in previous applications. Here, three unresolved challenges facing invasive species distribution modeling (iSDM) were examined in an effort to increase prediction accuracy and improve ecological understanding of actual and potential distributions of biological invasions. The effects of absence data and dispersal constraints, stage of invasion, and spatial dependence were assessed, using an extensive collection of field-based data on the invasive forest pathogen Phytophthora ramorum. Spatial analyses were based on a range of statistical techniques (generalized linear models, classification trees, maximum entropy, ecological niche factor analysis, multicriteria evaluation) and four groups of environmental parameters that varied in space and time: atmospheric moisture and temperature, topographic variability, abundance and susceptibility of host vegetation, and dispersal pressure. Results show that incorporating data on species absence and dispersal limitations is crucial not only to avoid overpredictions of the actual invaded range in a specific period of time but also for ecologically meaningful evaluation of iSDMs. When dispersal and colonization cannot be estimated explicitly, e.g. via dispersal kernels of propagule pressure, spatial dependence measured as spatial autocorrelation at multiple scales can serve as an important surrogate for dynamic processes that explain ecological mechanisms of invasion. If the goal is to identify habitats at potential risk of future spread, the stage of invasion should be considered because it represents the degree to which an organism is at equilibrium with its environment and limits the extent to which occurrence observations provide a sample of the species ecological niche. This research provides insight into several key principles of the SDM discipline, with implications for practical management of biological invasions

Abundance distributions for tree species in Great Britain: a two-stage approach to modeling abundance using species distribution modeling and random forest

High-quality abundance data are expensive and time-consuming to collect and often highly limited in availability. Nonetheless, accurate, high-resolution abundance distributions are essential for many ecological applications ranging from species conservation to epidemiology. Producing models that can predict abundance well, with good resolution over large areas, has therefore been an important aim in ecology, but poses considerable challenges. We present a two-stage approach to modeling abundance, combining two established techniques. First, we produce ensemble species distribution models (SDMs) of trees in Great Britain at a fine resolution, using much more common presence–absence data and key environmental variables. We then use random forest regression to predict abundance by linking the results of the SDMs to a much smaller amount of abundance data. We show that this method performs well in predicting the abundance of 20 of 25 tested British tree species, a group that is generally considered challenging for modeling distributions due to the strong influence of human activities. Maps of predicted tree abundance for the whole of Great Britain are provided at 1 km2 resolution. Abundance maps have a far wider variety of applications than presence-only maps, and these maps should allow improvements to aspects of woodland management and conservation including analysis of habitats and ecosystem functioning, epidemiology, and disease management, providing a useful contribution to the protection of British trees. We also provide complete R scripts to facilitate application of the approach to other scenarios

Dynamic habitat models reflect interannual movement of cetaceans within the California current ecosystem

This modeling project was funded by the Navy, Commander, U.S. Pacific Fleet (U.S. Navy), the Bureau of Ocean Energy Management (BOEM), and by the National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS), Southwest Fisheries Science Center (SWFSC). The 2018 survey was conducted as part of the Pacific Marine Assessment Program for Protected Species (PacMAPPS), a collaborative effort between NOAA Fisheries, the U.S. Navy, and BOEM to collect data necessary to produce updated abundance estimates for cetaceans in the CCE study area. BOEM funding was provided via Interagency Agreement (IAA) M17PG00025, and Navy funding via IAA N0007018MP4C560, under the Mexican permit SEMARNAT/SGPA/DGVS/013212/18. The methods used to derive uncertainty estimates were developed as part of “DenMod: Working Group for the Advancement of Marine Species Density Surface Modeling” funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, and managed by the U.S. Navy’s Living Marine Resources (LMR) program under Contract No. N39430-17-C-1982. Other permits included INEGI: Oficio núm. 400./331/2018, INEGI.GMA 1.03 SAGARPA de Oficio B00.02.04.1530/2018 NMFS Permit No. 19091.The distribution of wide-ranging cetacean species often cross national or jurisdictional boundaries, which creates challenges for monitoring populations and managing anthropogenic impacts, especially if data are only available for a portion of the species’ range. Many species found off the U.S. West Coast are known to have continuous distributions into Mexican waters, with highly variable abundance within the U.S. portion of their range. This has contributed to annual variability in design-based abundance estimates from systematic shipboard surveys off the U.S. West Coast, particularly for the abundance of warm temperate species such as striped dolphin, Stenella coeruleoalba, which increases off California during warm-water conditions and decreases during cool-water conditions. Species distribution models (SDMs) can accurately describe shifts in cetacean distribution caused by changing environmental conditions, and are increasingly used for marine species management. However, until recently, data from waters off the Baja California peninsula, México, have not been available for modeling species ranges that span from Baja California to the U.S. West Coast. In this study, we combined data from 1992–2018 shipboard surveys to develop SDMs off the Pacific Coast of Baja California for ten taxonomically diverse cetaceans. We used a Generalized Additive Modeling framework to develop SDMs based on line-transect surveys and dynamic habitat variables from the Hybrid Coordinate Ocean Model (HYCOM). Models were developed for ten species: long- and short-beaked common dolphins (Delphinus delphis delphis and D. d. bairdii), Risso’s dolphin (Grampus griseus), Pacific white-sided dolphin (Lagenorhynchus obliquidens), striped dolphin, common bottlenose dolphin (Tursiops truncatus), sperm whale (Physeter macrocephalus), blue whale (Balaenoptera musculus), fin whale (B. physalus), and humpback whale (Megaptera novaeangliae). The SDMs provide the first fine-scale (approximately 9 x 9 km grid) estimates of average species density and abundance, including spatially-explicit measures of uncertainty, for waters off the Baja California peninsula. Results provide novel insights into cetacean ecology in this region as well as quantitative spatial data for the assessment and mitigation of anthropogenic impacts.Publisher PDFPeer reviewe

Editorial: Bridging the gap between policy and science in assessing the health status of marine ecosystems

Human activities,both established and emerging, increasingly affect the provision of marine ecosystem services that deliver societal and economic benefits. Monitoring the status of marine ecosystems and determining how human activities change their capacity to sustain benefits for society requires an evidence-based Integrated Ecosystem Assessment approach that incorporates knowledge of ecosystem functioning and services).Although,there are diverse methods to assess the status of individual ecosystem components, none assesses the health of marine ecosystems holistically, integrating information from multiple ecosystem components. Similarly,while acknowledging the availability of several methods to measure single pressures and assess their impacts, evaluation of cumulative effects of multiple pressures remains scarce.Therefore,an integrative assessment requires us to first understand the response of marine ecosystems to human activities and their pressures and then develop innovative, cost-effective monitoring tools that enable collection of data to assess the health status of large marine areas. Conceptually, combining this knowledge of effective monitoring methods with cost-benefit analyses will help identify appropriate management measures to improve environmental status economically and efficiently. The European project DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status) specifically addressed these topics in order to support policymakers and managers in implementing the European Marine Strategy Framework Directive. Here, we synthesize our main innovative findings, placing these within the context of recent wider research, and identifying gaps and the major future challenges

Bridging the gap between policy and science in assessing the health status of marine ecosystems

Human activities, both established and emerging, increasingly affect the provision of marine ecosystem services that deliver societal and economic benefits. Monitoring the status of marine ecosystems and determining how human activities change their capacity to sustain benefits for society requires an evidence-based Integrated Ecosystem Assessment approach that incorporates knowledge of ecosystem functioning and services). Although, there are diverse methods to assess the status of individual ecosystem components, none assesses the health of marine ecosystems holistically, integrating information from multiple ecosystem components. Similarly, while acknowledging the availability of several methods to measure single pressures and assess their impacts, evaluation of cumulative effects of multiple pressures remains scarce. Therefore, an integrative assessment requires us to first understand the response of marine ecosystems to human activities and their pressures and then develop innovative, cost-effective monitoring tools that enable collection of data to assess the health status of large marine areas. Conceptually, combining this knowledge of effective monitoring methods with cost-benefit analyses will help identify appropriate management measures to improve environmental status economically and efficiently. The European project DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status) specifically addressed t hese topics in order to support policy makers and managers in implementing the European Marine Strategy Framework Directive. Here, we synthesize our main innovative findings, placing these within the context of recent wider research, and identifying gaps and the major future challenges

Conserving large mammals on small islands: A case study on one of the world’s most understudied pigs, the Togean islands babirusa

Conserving large mammals on small islands poses a great challenge, given their high resource demand within the limited space available. The endangered Togean Islands babirusa (Babyrousa togeanensis) is one of these species, with a distribution range limited to four small islands in the Togean Archipelago, Indonesia. Despite being listed as endangered, very little information is available on the distribution and ecology of this species. To address this critical knowledge gap, we here report the first field-based ecological study of the Togean Islands babirusa across its entire distribution range. Following a stratified random sampling procedure, we distributed camera traps at 103 stations across four islands to collect data on the species distribution from July-October 2022. We performed an occupancy modeling analysis to assess the species’ habitat use, with various habitat features estimated through remote sensing and field measurements as covariates. We found that forest and mangrove availability over a large area positively influenced babirusa habitat selection. Babirusas only made use of agricultural areas when large forest areas were available nearby. Our results highlight the benefits of redesigning the national park area to accommodate babirusa habitat requirements, specifically by reassigning the non-forested park areas (about 30% of the park area) to non-protected forests currently outside the park boundary (about 50% of total forested area). Our case study exemplifies key challenges associated with conserving large mammals on small islands and highlights the importance of following an adaptive management approach, which in this case implies shifting 30% of the current protected area