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Remote sensing imagery can provide critical information on the magnitude and extent of damage caused by forest pests and pathogens. However, monitoring short‐term changes in deciduous forest condition caused by defoliating insects is challenging and requires approaches that directly account for seasonal vegetation dynamics. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016–2018 gypsy moth (Lymantria dispar) outbreak in southern New England. Our comparisons revealed that most models made reasonable predictions of changes in canopy condition and egg and larval abundances of L. dispar, indicating a strong correlation between our harmonic‐based estimates of condition change and defoliator activity. The greatest differences in predictive ability were in the spectral domain, with assessments based on Tasseled Cap Greenness, Simple Ratio, and the Enhanced Vegetation Index ranking among the top models, and the commonly used Normalized Difference Vegetation Index consistently exhibiting poorer performance. We also observed notable differences in the magnitude of scores for different baseline periods. Additionally, we found that Landsat‐based condition scores better explained larval abundance than egg mass counts, which have historically been used as a proxy for later‐season larval abundance, indicating that our remote sensing approach may be more accurate and cost‐effective for generating consistent retrospective assessments of L. dispar population abundance in addition to estimates of canopy damage. These findings provide important linkages between spectral changes detected using a harmonic modeling approach and biophysical aspects of defoliator activity, with potential to extend monitoring and prediction to regional or even continental scales.

Repeated moderate severity forest disturbances can cause short- and long-term shifts in ecosystem processes. Prior work has found that stand-replacing disturbances (e.g., clear-cutting) increases streamflow in temperate forests, but streamflow responses to repeated moderate severity disturbances are more equivocal. This study examined a moderate disturbance caused by an unexpected population irruption of the invasive insect Lymantria dispar (common name: gypsy moth) in 2015-2017. This irruption resulted in major but spatially heterogeneous defoliation due to leaf consumption by larvae during the summer growing season, which reduced leaf index area and associated evapotranspiration. Our regional approach in southern New England, USA used data from 88 U.S. Geological Survey (USGS) stream gages to assess whether changes in watershed runoff ratios and instantaneous streamflow characteristics were associated with satellite-derived metrics of forest condition (i.e., defoliation). Using stream gages with at least 15 years of baseline flow data, we calculated subwatershed runoff ratio values for a baseline period and quantified anomalous departures from baseline runoff ratios for the 2015-2017 period of L. dispar defoliation. We found a small linear increase in seasonal runoff ratio anomalies that was associated with defoliation intensity. We also found larger volumes of high and medium instantaneous streamflows compared to baseline flow conditions in watersheds with more intense defoliation. This study provided important insights into the impacts of moderate disturbance on ecohydrology in mesic temperate forests with a methodological approach that assessed the impacts of spatially heterogeneous and repeated moderate disturbance at a regional scale.

Invasive alien species are likely to interact with climate change, thus necessitating management that proactively addresses both global changes. However, invasive species managers’ concerns about the effects of climate change, the degree to which they incorporate climate change into their management, and what stops them from doing so remain unknown. Therefore, we surveyed natural resource managers addressing invasive species across the U.S. about their priorities, concerns, and management strategies in a changing climate. Of the 211 managers we surveyed, most were very concerned about the influence of climate change on invasive species management, but their organizations were significantly less so. Managers reported that lack of funding and personnel limited their ability to effectively manage invasive species, while lack of information limited their consideration of climate change in decision-making. Additionally, managers prioritized research that identifies range-shifting invasive species and native communities resilient to invasions and climate change. Managers also reported that this information would be most effectively communicated through conversations, research summaries, and meetings/symposia. Despite the need for more information, 65% of managers incorporate climate change into their invasive species management through strategic planning, preventative management, changing treatment and control, and increasing education and outreach. These results show the potential for incorporating climate change into management, but also highlight a clear and pressing need for more targeted research, accessible science communication, and two-way dialogue between researchers and managers focused on invasive species and climate change.

Understanding connections between ecosystem nitrogen (N) cycling and invasive insect defoliation could facilitate the prediction of disturbance impacts across a range of spatial scales. In this study we investigated relationships between ecosystem N cycling and tree defoliation during a recent 2015–18 irruption of invasive gypsy moth caterpillars (Lymantria dispar), which can cause tree stress and sometimes mortality following multiple years of defoliation. Nitrogen is a critical nutrient that limits the growth of caterpillars and plants in temperate forests. In this study, we assessed the associations among N concentrations, soil solution N availability and defoliation intensity by L. dispar at the scale of individual trees and forest plots. We measured leaf and soil N concentrations and soil solution inorganic N availability among individual red oak trees (Quercus rubra) in Amherst, MA and across a network of forest plots in Central Massachusetts. We combined these field data with estimated defoliation severity derived from Landsat imagery to assess relationships between plot-scale defoliation and ecosystem N cycling. We found that trees in soil with lower N concentrations experienced more herbivory than trees in soil with higher N concentrations. Additionally, forest plots with lower N soil were correlated with more severe L. dispar defoliation, which matched the tree-level relationship. The amount of inorganic N in soil solution was strongly positively correlated with defoliation intensity and the number of sequential years of defoliation. These results suggested that higher ecosystem N pools might promote the resistance of oak trees to L. dispar defoliation and that defoliation severity across multiple years is associated with a linear increase in soil solution inorganic N., A severe, multi-year gypsy moth population irruption occurred in a temperate New England forest that caused intense leaf herbivory and tree defoliation. We found that trees and forest stands with lower soil nitrogen concentrations experienced more defoliation than those with higher soil nitrogen concentrations. The inorganic nitrogen in soil solution was strongly positively correlated with defoliation intensity and the number of sequential years of defoliation. This could suggest that higher nitrogen pools might promote the resistance of oak trees to defoliation and that defoliation severity across multiple years is associated with a linear increase in soil solution inorganic nitrogen.

Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km2 ) and fine (2 km2 ) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

While landscape models of invasive plant distributions have potential for targeting management areas, we focus on two reasons such models may be limited in their application. First, models to date are biased towards explaining establishment in ruderal habitat but not spread of invasives into adjacent habitat, where they may most impact native plants. Second, models are usually developed across ecoregions or for a single ecoregion, and it is unclear how well models for different regions and spatial extents agree. Our aims were to (1) test how landscape variables explain garlic mustard (Alliaria petiolata) occurrence on forest edges and its incursion into forest understory habitat; (2) compare models constructed for two ecoregions, separately, and both ecoregions together. In 183 sites in two ecoregions in Massachusetts, we recorded whether garlic mustard occurred on the forest edge (edge occurrence) and in the understory (understory incursion). We used logistic regression to relate either edge occurrence or understory incursion to elevation and four variables describing land use surrounding each site: percent open land in 1830, percent developed and agricultural land in 2005, and forest-edge length in 2005. Elevation was negatively associated with edge occurrence within regions but positively associated across regions. Land use from 2005 explained edge occurrence and understory incursion in only one region. These results suggest that mechanisms driving garlic mustard distributions are scale- and region-dependent. Our findings also suggest that region-specific invasive distribution models are necessary, and we caution against implying probability of understory incursion from the probability of edge establishment.

Southern New England is currently experiencing the first major gypsy moth (Lymantria dispar) defoliation event in nearly 30 years. Using a novel approach based on time series of Landsat satellite observations, we generated consistent maps of gypsy moth defoliation for 2015 (first year of the outbreak), 2016 (second year of outbreak), and 2017 (third year of outbreak). Our mapped results demonstrate that the defoliation event continued through the 2017 growing season. Moreover, the affected area more than doubled in extent each year and expanded radially to encompass 4386 km2 of forested area in Rhode Island, eastern Connecticut, and central Massachusetts. The current gypsy moth outbreak is believed to be the result of a series of unusually dry springs in 2014, 2015, and 2016, which suppressed Entomophaga maimaiga, a fungal mortality agent that has historically reduced gypsy moth impacts in this region. The continuation and marked expansion of the outbreak in 2017 despite average spring rainfall suggests that caterpillars were active early in the growing season, and mortality from the fungus likely peaked after significant defoliation had already occurred. Our Landsat time series approach represents an important new source of data on spatial and temporal patterns in gypsy moth defoliation, and continued satellite-based monitoring will be essential for tracking the progress of this and other gypsy moth outbreaks.

Multi-spectral imagery from the Landsat family of satellites has been used to map forest properties for decades, but accurate forest type characterizations at a 30-m Landsat resolution have remained an ongoing challenge, especially over large areas. We combined existing Landsat time series algorithms to quantify both harmonic and phenological metrics in a new set of spectral-temporal features that can be produced seamlessly across many Landsat scenes. Harmonic metrics characterize mean annual reflectance and seasonal variability, while phenological metrics quantify the timing of seasonal events. We assessed the performance of spectral-temporal features derived from time series of all available observations (1985–2015) relative to more conventional single date and multi-date inputs. Performance was determined based on agreement with a reference dataset for eight New England forest types at both the pixel and polygon scale. We found that spectral-temporal features consistently and significantly (paired t-test, p ≪ 0.01) outperformed all feature sets derived from individual images and multi-date combinations in all measures of agreement considered. Harmonic features, such as annual amplitude and model fit error, aid in distinguishing deciduous hardwoods from conifer species, while phenology features, like the timing of autumn onset and growing season length, were useful in separating hardwood classes. This study represents an important step toward large-scale forest type mapping using spectral-temporal Landsat features by providing a quantitative assessment of the advantages of harmonic and phenology features derived from time series of Landsat data as compared with more conventional single-date and multi-date classification inputs.

We collected data on mortality of late-instar gypsy moth, Lymantria dispar (L.), from outbreak populations over 4 wk in June 2017 at 10 sites in the New England region of the United States, along with estimated rainfall at these sites. Deposition of airborne conidia of the fungal pathogen, Entomophaga maimaiga Humber, Shimazu & R.S. Soper, was measured at these same sites as well as at seven other locations in New England. We also quantified the geographical distribution of gypsy moth-caused defoliation in New England in 2017 and 2018 from Landsat imagery. Weekly mortality of gypsy moth larvae caused by E. maimaiga correlated with local deposition of conidia from the previous week, but not with rainfall. Mortality from this pathogen reached a peak during the last 2 wk of gypsy moth larval development and always exceeded that caused by LdNPV, the viral pathogen of gypsy moth that has long been associated with gypsy moth outbreaks, especially prior to 1989. Cotesia melanoscela (Ratzeburg) was by far the most abundant parasitoid recovered and caused an average of 12.6% cumulative parasitism, but varied widely among sites. Deposition of E. maimaiga conidia was highly correlated with percent land area defoliated by gypsy moths within distances of 1 and 2 km but was not significantly correlated with defoliation at distances greater than 2 km. This is the first study to relate deposition of airborne conidia of E. maimaiga to mortality of gypsy moths from that agent.

With over 30 years of directly comparable Landsat satellite observations now freely available, and new imagery being added to the Landsat archive every day, Landsat time series analysis affords novel opportunities for ecosystem mapping, environmental monitoring and comparative ecology. This study presents a series of data-driven examples that illustrate the potential of using Landsat time series to further the study of land cover characterization, vegetation phenology and landscape dynamics. Our goal is to showcase how ecosystem properties and dynamics manifest in the Landsat data record, laying the foundation for better integration of remote sensing and ecology using Landsat time series. Our results suggest that time series provide valuable information on ecosystem cover, use and condition that could advance understanding of ecosystem function, resilience and dynamics. We have only just begun to understand how to use the complete record of Landsat observations for the study of ecology, and we hope this work will encourage future studies on quantifying and analyzing relationships between time series data, ecosystems and ecological processes.

Characterizing urban landscapes is important given the present and future projections of global population that favor urban growth. The definition of “urban” on a thematic map has proven to be problematic since urban areas are heterogeneous in terms of land use and land cover. Further, certain urban classes are inherently imprecise due to the difficulty in integrating various social and environmental inputs into a precise definition. Social components often include demographic patterns, transportation, building type and density while ecological components include soils, elevation, hydrology, climate, vegetation and tree cover. In this paper, we adopt a coupled human and natural system (CHANS) integrated scientific framework for characterizing urban landscapes. We implement the framework by adopting a fuzzy sets concept of “urban characterization” since fuzzy sets relate to classes of object with imprecise boundaries in which membership is a matter of degree. For dynamic mapping applications, user-defined classification schemes involving rules combining different social and ecological inputs can lead to a degree of quantification in class labeling varying from “highly urban” to “least urban”. A socio-economic perspective of urban may include threshold values for population and road network density while a more ecological perspective of urban may utilize the ratio of natural versus built area and percent forest cover. Threshold values are defined to derive the fuzzy rules of membership, in each case, and various combinations of rules offer a greater flexibility to characterize the many facets of the urban landscape. We illustrate the flexibility and utility of this fuzzy inference approach called the Fuzzy Urban Index for the Boston Metro region with five inputs and eighteen rules. The resulting classification map shows levels of fuzzy membership ranging from highly urban to least urban or rural in the Boston study region. We validate our approach using two experts assessing accuracy of the resulting fuzzy urban map. We discuss how our approach can be applied in other urban contexts with newly emerging descriptors of urban sustainability, urban ecology and urban metabolism.

The livelihood of coastal communities is directly linked to the health of intertidal and marine ecosystems. Coastal zones and marine areas are rapidly changing and highly vulnerable to impacts from climate change, accelerating human development, and over-fishing. Quality of life and the benefits of coastal development can be greatly enhanced, and costs minimized, by projecting and comparing alternative policy outcomes before management decisions are made. This article describes the Marine Integrated Decision Analysis System (MIDAS), an interactive decision support tool designed to assist the users and managers of Belize's system of marine management areas (MMAs) in understanding the interactions among various ecological, socioeconomic, and governance conditions in a spatially explicit manner. MIDAS can simulate and visualize the likely effects of alternative management strategies and therefore provides an important tool for evaluating potential scenarios. We developed two MIDAS modules to address specific...

The article Incorporating climate change into invasive species management: insights from managers, written by Evelyn M. Beaury

Mangrove forests dominate many tropical coastlines and are one of the most bio-diverse and productive environments on Earth. However, little is known of the large-scale dynamics of mangrove canopies and how they colonize intertidal areas. Here we focus on a fringe mangrove forest located in the Mekong River Delta, Vietnam; a fast prograding shoreline where mangroves are encroaching tidal flats. The spatial and temporal evolution of the mangrove canopy is studied using a time series of Landsat images spanning two decades as well as Shuttle Radar Topography Mission (SRTM) elevation data. Our results show that fast mangrove expansion is followed by an increase in Normalized Difference Vegetation Index (NDVI) in the newly established canopy. We observe three different dynamics of the mangrove fringe: in the southwest part of the fringe, near a deltaic distributary where the fringe boundary is linear, the canopy expands uniformly on the tidal flats with a high colonization rate and high NDVI values. In the northeast part of the fringe, near another distributary, the canopy expands at a much lower rate with low NDVI values. In the fringe center, far from the river mouths, the fringe boundary is highly irregular and mangroves expansion in characterized by sparse vegetated patches displaying low NDVI values. We ascribe these different dynamics to wave action and southwest longshore transport triggered by energetic northeasterly monsoons during winter. We further link the large-scale dynamics of the fringe to small-scale physical disturbances (waves, erosion and deposition) that might prevent the establishment of mangrove seedlings. Based on these results, we include mangrove encroachment in an already published conceptual model of progradation of the Mekong River Delta. We conclude that high NDVI values and a constantly linear vegetation–water interface are indicative of stable mangrove canopies undergoing fast expansion, probably triggered by sediment availability at the shore. Our results can be applied more generally to mangrove forests growing in minerogenic and high tidal range environments with high sediment inputs. Copyright © 2016 John Wiley & Sons, Ltd.

This article describes the design and implementation of Marine Integrated Decision Analysis System (MIDAS), a spatial decision support system (SDSS). MIDAS allows users to select values for a set of governance, ecological and socioeconomic variables. Outcomes resulting from interactions between these variables are then modeled to demonstrate the effectiveness of management in marine managed areas (MMAs) in Belize, Panama, and other areas. MIDAS is written in Java SDK and Runtime Environment (JRE; version 6 update 5) that enables portability across computer platforms. The article describes the utility of MIDAS in three contexts: (I) to assist managers, policy makers, and various stakeholders in understanding the critical determining factors (CDFs) for MMA success, (2) to enhance multi-stakeholder analysis and promote consensus, and (3) to provide a free and intuitive interface with a capability to incorporate user valuations of factors affecting marine management and to generate reports and maps concerning risk. MIDAS has been tested and validated by a variety of users in three workshops.

Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar) outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS) products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators.