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USGS2019

The state of the world’s mangrove forests: Past, present, and future

Intertidal mangrove forests are a dynamic ecosystem experiencing rapid changes in extent and habitat quality over geological history, today and into the future. Climate and sea level have drastically altered mangrove distribution since their appearance in the geological record ∼75 million years ago (Mya), through to the Holocene. In contrast, contemporary mangrove dynamics are driven primarily by anthropogenic threats, including pollution, overextraction, and conversion to aquaculture and agriculture. Deforestation rates have declined in the past decade, but the future of mangroves is uncertain; new deforestation frontiers are opening, particularly in Southeast Asia and West Africa, despite international conservation policies and ambitious global targets for rehabilitation. In addition, geological and climatic processes such as sea-level rise that were important over geological history will continue to influence global mangrove distribution in the future. Recommendations are given to reframe mangrove conservation, with a view to improving the state of mangroves in the future.

Annual Review of Environment and Resources↗

USGS2025

Thirty years of the U.S. National Land Cover Database: Impacts and future direction

The National Land Cover Database (NLCD), developed through the Multi-Resolution Land Characteristics Consortium, was initiated 30 years ago and has continually provided critical, Landsat-based landcover and land-change information for the United States. Originally launched to address the lack of national-scale, moderate-resolution land-cover data, NLCD has evolved from the pioneering 1992 dataset into a comprehensive, annually updated product suite. Key innovations include the introduction of impervious surface mapping, forest canopy mapping, standardized Landsat mosaics, national-scale accuracy assessments, continual evolution of deep learning and artificial intelligence methodologies, and a transition toward operational, change-focused monitoring. The NLCD has become an essential resource for scientific research, land management, and policy development, with extensive adoption across federal, state, and local agencies; academia; and the private sector. The NLCD data underpin a wide array of applications, including biodiversity conservation, urban planning, hydrology, human health studies, and natural hazard assessment. As new global and high-resolution commercial land-cover products emerge, the NLCD continues to distinguish itself through its temporal depth, federal backing, and thematic consistency. Moving forward, the NLCD will maintain its niche as the leading, moderate-resolution, long-term land-cover and land-change dataset for the United States, ensuring continued support for broad national applications while complementing higher-resolution and global-mapping efforts.

Photogrammetric Engineering & Remote Sensing↗

USGS2005

Grazing by the intertidal gastropod Melampus coffeus greatly increases mangrove leaf litter degradation rates

Melampus coffeus , a pulmonate gastropod, forages for mangrove leaf litter at low tide and climbs tree trunks to avoid inundation during high tide. Unlike many grazers, these snails can assimilate mangrove leaf material. At Boca Ceiga Bay, Florida, densities of adult snails were high (>100 snails m –2 ) throughout a 130 m wide intertidal zone. A mark–recapture study indicated that over the course of 1 mo 48.6 ± 6.1% of snails returned to their initial tree during high tides, while those that did relocate moved 6.66 ± 0.60 m and were recorded from 33 new trees at high tide. A field experiment comparing tethered leaves with small and large litter bags showed that snail grazing greatly increased the rate of red ( Rhizophora mangle ) and black ( Avicennia germinans ) leaf litter breakdown. Grazing by M. coffeus resulted in 90% weight loss in <4 wk ( A. germinans ) and 7 wk ( R. mangle ), compared to slower breakdown in litter bags (12 to 26 wk, A. germinans ; >26 wk, R. mangle ). Another experiment showed greater leaf litter accumulation on the forest floor in plots where M. coffeus was excluded. The decomposition coefficient ( k ) was 10× greater when M. coffeus grazing was allowed than k for any studies that enclosed leaves in litterbags. During our experiment, M. coffeus consumed an estimated 40.5% of mangrove leaf fall, and 19.8% of leaf litter was exported as particulate or dissolved material. This M. coffeus population can produce an estimated 3 × 10 6 larvae m –2 yr –1 , which suggests that the larval pathway is an important conduit of mangrove leaf resources to the estuarine food web.

Florida↗

USGS2021

Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud.

Remote Sensing↗

USGS2014

Songbirds as sentinels of mercury in terrestrial habitats of eastern North America

Mercury (Hg) is a globally distributed environmental contaminant with a variety of deleterious effects in fish, wildlife, and humans. Breeding songbirds may be useful sentinels for Hg across diverse habitats because they can be effectively sampled, have well-defined and small territories, and can integrate pollutant exposure over time and space. We analyzed blood total Hg concentrations from 8,446 individuals of 102 species of songbirds, sampled on their breeding territories across 161 sites in eastern North America [geometric mean Hg concentration = 0.25 μg/g wet weight (ww), range <0.01–14.60 μg/g ww]. Our records span an important time period—the decade leading up to implementation of the USEPA Mercury and Air Toxics Standards, which will reduce Hg emissions from coal-fired power plants by over 90 %. Mixed-effects modeling indicated that habitat, foraging guild, and age were important predictors of blood Hg concentrations across species and sites. Blood Hg concentrations in adult invertebrate-eating songbirds were consistently higher in wetland habitats (freshwater or estuarine) than upland forests. Generally, adults exhibited higher blood Hg concentrations than juveniles within each habitat type. We used model results to examine species-specific differences in blood Hg concentrations during this time period, identifying potential Hg sentinels in each region and habitat type. Our results present the most comprehensive assessment of blood Hg concentrations in eastern songbirds to date, and thereby provide a valuable framework for designing and evaluating risk assessment schemes using sentinel songbird species in the time after implementation of the new atmospheric Hg standards.

Ecotoxicology↗

USGS2024

Simulating past and future fire impacts on Mediterranean ecosystems

Worldwide, large wildfires are becoming increasingly common, leading to economic damages and threatening ecosystems and human health. Under future climate change, more frequent fire disturbance may push ecosystems into non-forested alternative stable states. Fire-prone ecosystems such as those in the Mediterranean Basin are expected to be particularly vulnerable, but the position of tipping points is unclear. We compare long-term palaeoecological data from Sardinia with output from a process-based dynamic vegetation model to investigate the mechanisms controlling the complex interactions between fire, climate, and vegetation in the past and the future. Our results show that past vegetation changes from Erica -shrublands to mixed evergreen-broadleaved Quercus ilex -dominated forests were driven by a climate-induced fire regime shift. By simulating vegetation dynamics under varying fire regimes, we could reproduce Holocene vegetation trajectories and mechanistically identify tipping points. Without an immediate reduction of greenhouse gas emissions, we simulate future expansion of fire-prone Mediterranean maquis and increasing fire occurrence. Similarly, high anthropogenic ignition frequencies and plantations of non-native, highly flammable trees could induce a shift to fire-adapted Erica shrublands. However, our simulations indicate that if global warming can be kept below 2°C, Quercus ilex forests will be able to persist and effectively reduce fire occurrences and impacts, making them a valuable restoration target in Mediterranean ecosystems. Synthesis . By combining long-term records of ecosystem change with a dynamic vegetation model, we show that past climate-driven fire regime shifts were the main driver of vegetation change, creating alternative stable states that persisted over centuries. Projected future climate change exceeding Holocene variability leads to pronounced vegetation changes and increased fire risks in our simulations, requiring new fire management strategies to maintain current ecosystem services.

Journal of Ecology↗

USGS2017

Mapping tree density in forests of the southwestern USA using Landsat 8 data

The increase of tree density in forests of the American Southwest promotes extreme fire events, understory biodiversity losses, and degraded habitat conditions for many wildlife species. To ameliorate these changes, managers and scientists have begun planning treatments aimed at reducing fuels and increasing understory biodiversity. However, spatial variability in tree density across the landscape is not well-characterized, and if better known, could greatly influence planning efforts. We used reflectance values from individual Landsat 8 bands (bands 2, 3, 4, 5, 6, and 7) and calculated vegetation indices (difference vegetation index, simple ratios, and normalized vegetation indices) to estimate tree density in an area planned for treatment in the Jemez Mountains, New Mexico, characterized by multiple vegetation types and a complex topography. Because different vegetation types have different spectral signatures, we derived models with multiple predictor variables for each vegetation type, rather than using a single model for the entire project area, and compared the model-derived values to values collected from on-the-ground transects. Among conifer-dominated areas (73% of the project area), the best models (as determined by corrected Akaike Information Criteria (AICc)) included Landsat bands 2, 3, 4, and 7 along with simple ratios, normalized vegetation indices, and the difference vegetation index ( R 2 values for ponderosa: 0.47, piñon-juniper: 0.52, and spruce-fir: 0.66). On the other hand, in aspen-dominated areas (9% of the project area), the best model included individual bands 4 and 2, simple ratio, and normalized vegetation index ( R 2 value: 0.97). Most areas dominated by ponderosa, pinyon-juniper, or spruce-fir had more than 100 trees per hectare. About 54% of the study area has medium to high density of trees (100–1000 trees/hectare), and a small fraction (4.5%) of the area has very high density (>1000 trees/hectare). Our results provide a better understanding of tree density for identifying areas in need of treatment and planning for more effective treatment. Our analysis also provides an integrated method of estimating tree density across complex landscapes that could be useful for further restoration planning.

New Mexico↗

USGS2022

Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru

Data availability remains a principal factor limiting the use of species distribution models (SDMs) as tools for wildlife conservation and management of rare species. Although data collected in systematic and rigorous fashion are preferable, available data for most species of conservation interest are usually low in both quality and number. Here we show that combining records published in peer-reviewed journals and gray literature sources (e.g., theses, government, and NGO reports) with unpublished records obtained by personal communications from relevant stakeholders affect the predicted distribution of spectacled bears ( Tremarctos ornatus ) in Peru. We built SDMs using generalized linear models, random forest, and Maxent, first using a dataset that only included published records, and second with a dataset using both published and unpublished records. All models were replicated ten times with random subsets with controlled sample size. Models that combined published and unpublished spectacled bear records had a better performance, irrespective of with SDM method used, increasing the connectivity of the species’ range, and increasing the overall predicted distribution area than models that only included published records. This was because unpublished records added key new localities, reducing spatial sampling biases. Our study shows that the inclusion of commonly disregarded data such as opportunistic records, reports from natural park rangers, student theses, and data-deficient small studies can make an important contribution to the overall ecological knowledge of rare and difficult-to-study species such as the spectacled bear.

Mammal Research↗

USGS2003

Recent and historical distributions of Canada lynx in Maine and the Northeast

The contiguous United States population of Canada lynx ( Lynx canadensis Kerr) is listed as threatened under the federal Endangered Species Act. However, the historic distribution of lynx in the Northeast is poorly understood. We used museum records, bibliographic records, and interviews to reconstruct the past distribution of lynx in Maine, which is at the current southern limit of the species' distribution in the eastern United States. We found a total of 118 records, representing at least 509 lynx in Maine. Lynx were observed throughout Maine, 1833–1912, with the exception of coastal areas. After 1913, lynx were most common in the forests of western and northern Maine, and absent to rare along the coast, but had not returned to southern Maine by 1999. Thirty-nine kittens representing at least 21 litters were distributed throughout northern and western Maine, 1864–1999. Populations apparently fluctuated, and in some years 200–300 lynx were harvested in Maine. Prior to the 1900s, lynx were much more widely distributed in the Northeast, ranging from Pennsylvania north into Quebec. Because Canada lynx have had a long presence in northern New England, and at times were relatively common, this species merits serious consideration in conservation planning in this region.

Northeast United States↗

USGS1987

Stratification of a cityscape using census and land use variables for inventory of building materials

A cityscape (or any landscape) can be stratified into environmental units using multiple variables of information. For the purposes of sampling building materials, census and land use variables were used to identify similar strata. In the Metropolitan Statistical Area of a cityscape, the census tract is the smallest unit for which census data are summarized and digitized boundaries are available. For purposes of this analysis, census data on total population, total number of housing units, and number of singleunit dwellings were aggregated into variables of persons per square kilometer and proportion of housing units in single-unit dwellings. The level 2 categories of the U.S. Geological Survey's land use and land cover data base were aggregated into variables of proportion of residential land with buildings, proportion of nonresidential land with buildings, and proportion of open land. The cityscape was stratified, from these variables, into environmental strata of Urban Central Business District, Urban Livelihood Industrial Commercial, Urban Multi-Family Residential, Urban Single Family Residential, Non-Urban Suburbanizing, and Non-Urban Rural. The New England region was chosen as a region with commonality of building materials, and a procedure developed for trial classification of census tracts into one of the strata. Final stratification was performed by discriminant analysis using the trial classification and prior probabilities as weights. The procedure was applied to several cities, and the results analyzed by correlation analysis from a field sample of building materials. The methodology developed for stratification of a cityscape using multiple variables has application to many other types of environmental studies, including forest inventory, hydrologic unit management, waste disposal, transportation studies, and other urban studies. Multivariate analysis techniques have recently been used for urban stratification in England. ?? 1987 Annals of Regional Science.

The Annals of Regional Science↗

USGS1993

Acid Rain

Acid deposition, or acid rain as it is more commonly referred to, has become a widely publicized environmental issue in the U.S. over the past decade. The term usually conjures up images of fish kills, dying forests, "dead" lakes, and damage to monuments and other historic artifacts. The primary cause of acid deposition is emission of S0 2 and NO x to the atmosphere during the combustion of fossil fuels. Oxidation of these compounds in the atmosphere forms strong acids - H 2 SO 4 and HNO 3 - which are returned to the Earth in rain, snow, fog, cloud water, and as dry deposition. Although acid deposition has only recently been recognized as an environmental problem in the U.S., it is not a new phenomenon (Cogbill & Likens 1974). As early as the middle of the 17th century in England, the deleterious effects of industrial emissions on plants, animals, and humans, and the atmospheric transport of pollutants between England and France had become issues of concern (Evelyn 1661, Graunt 1662). It is interesting that well over three hundred years ago in England, recommendations were made to move industry outside of towns and build higher chimneys to spread the pollution into "distant parts." Increasing the height of smokestacks has helped alleviate local problems, but has exacerbated others. In the U.S. the height of the tallest smokestack has more than doubled, and the average height of smokestacks has tripled since the 1950s (Patrick et al 1981). This trend occurred in most industrialized nations during the 20th century and has had the effect of transforming acid rain from a local urban problem into a problem of global scale.

Annual Review of Earth and Planetary Sciences↗

USGS2017

Delta-Flux: An eddy covariance network for a climate-smart Lower Mississippi Basin

Networks of remotely monitored research sites are increasingly the tool used to study regional agricultural impacts on carbon and water fluxes. However, key national networks such as the National Ecological Observatory Network and AmeriFlux lack contributions from the Lower Mississippi River Basin (LMRB), a highly productive agricultural area with opportunities for soil carbon sequestration through conservation practices. The authors describe the rationale to create the new Delta-Flux network, which will coordinate efforts to quantify carbon and water budgets at seventeen eddy covariance flux tower sites in the LMRB. The network structure will facilitate climate-smart management strategies based on production-scale and continuous measurements of carbon and water fluxes from the landscape to the atmosphere under different soil and water management conditions. The seventeen instrumented field sites are expected to monitor fluxes within the most characteristic landscapes of the target area: row-crop fields, pasture, grasslands, forests, and marshes. The network participants are committed to open collaboration and efficient regionalization of site-level findings to support sustainable agricultural and forestry management and conservation of natural resources.

Arkansas, Louisiana, Mississippi↗

USGS2010

Attributes for MRB_E2RF1 Catchments by Major River Basins in the Conterminous United States: NLCD 2001 Tree Canopy

This tabular data set represents the mean percent tree canopy from the Canopy Layer of the National Land Cover Dataset 2001 (LaMotte and Wieczorek, 2010), compiled for every MRB_E2RF1 catchment of Major River Basins (MRBs, Crawford and others, 2006). The source data set represents tree canopy percentage for the conterminous United States for 2001. The Canopy Layer of the National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). The MRB_E2RF1 catchments are based on a modified version of the U.S. Environmental Protection Agency's (USEPA) ERF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002; Brakebill and others, 2011). Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering New England and Mid-Atlantic (MRB1), South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB5), the Rio Grande, Colorado, and the Great basin (MRB6), the Pacific Northwest (MRB7) river basins, and California (MRB8).

Data Series↗

USGS2010

Attributes for MRB_E2RF1 Catchments by Major River Basins in the Conterminous United States: NLCD 2001 Imperviousness

This tabular data set represents the mean percent impervious surface from the Imperviousness Layer of the National Land Cover Dataset 2001, (LaMotte and Wieczorek, 2010), compiled for every MRB_E2RF1 catchment of selected Major River Basins (MRBs, Crawford and others, 2006). The source data set represents imperviousness for the conterminous United States for 2001. The Imperviousness Layer of the National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). The MRB_E2RF1 catchments are based on a modified version of the U.S. Environmental Protection Agency's (USEPA) ERF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002;Brakebill and others, 2011). Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering New England and Mid-Atlantic (MRB1), South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB5), the Rio Grande, Colorado, and the Great basin (MRB6), the Pacific Northwest (MRB7) river basins, and California (MRB8).

Data Series↗

USGS1997

Acid Rain

Book chapter↗

USGS2016

Two Holocene paleofire records from Peten, Guatemala: Implications for natural fire regime and prehispanic Maya land use

Although fire was arguably the primary tool used by the Maya to alter the landscape and extract resources, little attention has been paid to biomass burning in paleoenvironmental reconstructions from the Maya lowlands. Here we report two new well-dated, high-resolution records of biomass burning based on analysis of macroscopic fossil charcoal recovered from lacustrine sediment cores. The records extend from the early Holocene, through the full arc of Maya prehistory, the Colonial, and post-Colonial periods (~ 9000 cal yr BP to the present). (Hereafter BP) The study sites, Lago Paixban and Lago Puerto Arturo, are located in northern Peten, Guatemala. Results provide the first quantitative analysis from the region demonstrating that frequent fires have occurred in the closed canopy forests since at least the early Holocene (~ 9000 BP), prior to occupation by sedentary agriculturalists. Following the arrival of agriculture around 4600 BP, the system transitioned from climate controlled to anthropogenic control. During the Maya period, changes in fire regime are muted and do not appear to be driven by changes in climate conditions. Low charcoal influx and fire frequency in the Earliest Preclassic period suggest that land use strategies may have included intensive agriculture much earlier than previously thought. Preliminary results showing concentrations of soot/black-carbon during the middle and late Preclassic periods are lower than modern background values, providing intriguing implications regarding the efficiency of Maya fuel consumption.

Peten↗

USGS2025

Spatial occupancy patterns of the endangered northern long‐eared bat in New England

Aim White-nose syndrome has caused severe declines in eastern North American cave bats, leading to the federal listing of the northern long-eared bat ( Myotis septentrionalis ) as endangered in the United States and Canada. This has heightened the importance of long-term monitoring to inform species status assessments. We employed a combination of long-term repeated and single-season acoustic survey data to assess the regional presence, spatial distribution, occupancy, and detection probability of northern long-eared bats. Location New England, United States. Methods We analysed acoustic data from 2357 detector sites, aggregated by year, using Bayesian single-species occupancy models. We investigated the influence of habitat characteristics, climatic variables, and year (2015–2022) on occupancy and the effects of weather conditions and survey month (May to August) on detection probability. Spatial random effects were included to address residual spatial autocorrelation, with a 1-km resolution chosen based on significant positive autocorrelation observed in a non-spatial model. Results Occupancy was highest on steep, forested hillsides with minimal anthropogenic development, higher in warmer regions, particularly along coastlines and on offshore islands, and declined across survey years. Including a 1-km spatial random effect reduced residual autocorrelation and suggests northern long-eared bats utilise resources at small to medium landscape scales. Detection probability was highest earlier in the maternity season, but declined when monthly precipitation or temperature exceeded average conditions. Conclusions Conservation efforts that focus on steep, forested hillsides in warmer regions with low anthropogenic development could be beneficial. Our analysis supports the use of spatial random effects at a 1-km 2 scale, highlighting the importance of survey designs that capture ecological variation at species-specific resolutions. Additionally, early-season acoustic surveys conducted during favourable weather conditions may improve monitoring effectiveness. Acoustic sampling and spatial occupancy modelling offer powerful tools for monitoring remnant populations of northern long-eared bats and guiding conservation practices.

Connecticut, Maine, Massachusetts, New Hampshire, ↗

USGS2005

Determinants of wood thrush nest success: A multi-scale, model selection approach

We collected data on 212 wood thrush (Hylocichla mustelina) nests in central New York from 1998 to 2000 to determine the factors that most strongly influence nest success. We used an information-theoretic approach to assess and rank 9 models that examined the relationship between nest success (i.e., the probability that a nest would successfully fledge at least 1 wood thrush offspring) and habitat conditions at different spatial scales. We found that 4 variables were significant predictors of nesting success for wood thrushes: (1) total core habitat within 5 km of a study site, (2) distance to forest-field edge, (3) total forest cover within 5 km of the study site, and (4) density and variation in diameter of trees and shrubs surrounding the nest. The coefficients of these predictors were all positive. Of the 9 models evaluated, amount of core habitat in the 5-km landscape was the best-fit model, but the vegetation structure model (i.e., the density of trees and stems surrounding a nest) was also supported by the data. Based on AIC weights, enhancement of core area is likely to be a more effective management option than any other habitat-management options explored in this study. Bootstrap analysis generally confirmed these results; core and vegetation structure models were ranked 1, 2, or 3 in over 50% of 1,000 bootstrap trials. However, bootstrap results did not point to a decisive model, which suggests that multiple habitat factors are influencing wood thrush nesting success. Due to model uncertainty, we used a model averaging approach to predict the success or failure of each nest in our dataset. This averaged model was able to correctly predict 61.1% of nest outcomes.

Journal of Wildlife Management↗