Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north

SAFARIS: a spatial analytic framework for pest forecast systems

Non-native pests and diseases pose a risk of economic and environmental damage to managed and natural U.S. forests and agriculture. The U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Plant Protection and Quarantine (PPQ) protects the health of U.S. agriculture and natural resources against invasive pests and diseases through efforts to prevent the entry, establishment, and spread of non-native pests and diseases. Because each pest or disease has its own idiosyncratic characteristics, analyzing risk is highly complex. To help PPQ better respond to pest and disease threats, we developed the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS), an integrated system designed to provide a seamless environment for producing predictive models. SAFARIS integrates pest biology information, climate and non-climate data drivers, and predictive models to provide users with readily accessible and easily customizable tools to analyze pest and disease risks. The phenology prediction models, spread forecasting models, and other climate-based analytical tools in SAFARIS help users understand which areas are suitable for establishment, when surveys would be most fruitful, and aid in other analyses that inform decision-making, operational efforts, and rapid response. Here we introduce the components of SAFARIS and provide two use cases demonstrating how pest-specific models developed with SAFARIS tools support PPQ in its mission. Although SAFARIS is designed to address the needs of PPQ, the flexible, web-based framework is publicly available, allowing any user to leverage the available data and tools to model pest and disease risks.

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type

Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.

Iterative Models for Early Detection of Invasive Species across Spread Pathways

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

A SNP test to identify Africanized honeybees via proportion of 'African' ancestry

The honeybee, Apis mellifera, is the world's most important pollinator and is ubiquitous in most agricultural ecosystems. Four major evolutionary lineages and at least 24 subspecies are recognized. Commercial populations are mainly derived from subspecies originating in Europe (75-95%). The Africanized honeybee is a New World hybrid of A. m. scutellata from Africa and European subspecies, with the African component making up 50-90% of the genome. Africanized honeybees are considered undesirable for bee-keeping in most countries, due to their extreme defensiveness and poor honey production. The international trade in honeybees is restricted, due in part to bans on the importation of queens (and semen) from countries where Africanized honeybees are extant. Some desirable strains from the United States of America that have been bred for traits such as resistance to the mite Varroa destructor are unfortunately excluded from export to countries such as Australia due to the presence of Africanized honeybees in the USA. This study shows that a panel of 95 single nucleotide polymorphisms, chosen to differentiate between the African, Eastern European and Western European lineages, can detect Africanized honeybees with a high degree of confidence via ancestry assignment. Our panel therefore offers a valuable tool to mitigate the risks of spreading Africanized honeybees across the globe and may enable the resumption of queen and bee semen imports from the Americas.