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At least 1,657 records · Page 92Linked to original sources

USGS2018

Estimating the probability of movement and partitioning seasonal survival in an amphibian metapopulation

Movement of individuals has been described as one of the best studied, but least understood concepts in ecology. The magnitude of movements, routes, and probability of movement have significant application to conservation. Information about movement can inform efforts to model species persistence and is particularly applicable in situations where specific threats (e.g., disease) may depend on the movement of hosts and potential vectors. We estimated the probability of movement (breeding dispersal and permanent emigration) in a metapopulation of 16 breeding sites for boreal toads ( Anaxyrus boreasboreas ). We used a multi‐state mark–recapture approach unique in its complexity (16 sites over 18 yr) to address questions related to these movements and variation in resident survival. We found that individuals had a 1–2% probability of dispersing in a particular year and that approximately 10–20% of marked individuals were transient and observed in the metapopulation only once. Resident survival probabilities differed by season, with 71–90% survival from emergence from hibernation through early post‐breeding and >97% survival from mid‐/late active season through hibernation. Movement‐related probabilities are needed to predict species range expansions and contractions, estimate population and metapopulation dynamics, understand host–pathogen and native–invasive species interactions, and to evaluate the relative effects of proposed management actions.

Colorado↗

USGS2023

Practical guide to measuring wetland carbon pools and fluxes

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.

Wetlands↗

USGS2008

Use of tolerance values to diagnose water-quality stressors to aquatic biota in New England streams

Identification of stressors related to biological impairment is critical to biological assessments. We applied nationally derived tolerance indicator values for four water-quality variables to fish and benthic macroinvertebrate assemblages at 29 sites along an urban gradient in New England. Tolerance indicator values (TIVs), as biologically based predictors of water-quality variables, were determined for dissolved oxygen, nitrite plus nitrate (nitrate), total phosphorus, and water temperature for each site based on observed biological assemblages (TIVO), and for expected assemblages (TIVE). The quotient method, based on a ratio of the TIVs for observed and expected assemblages (tolerance units), was used to diagnose potential water-quality stressors. In addition, the ratio of measured water-quality values to water-quality criteria (water-quality units) was calculated for each water-quality variable to assess measured water-quality stressors. Results from a RIVPACS predictive model for benthic macroinvertebrates and Bray-Curtis dissimilarity for fish were used to classify sites into categories of good or impaired ecological condition. Significant differences were detected between good and impaired sites for all biological tolerance units (fish and benthic macroinvertebrate assemblages averaged) except for nitrate (P = 0.480), and for all water-quality units except for nitrate (P = 0.183). Diagnosis of water-quality stressors at selected sites was, in general, consistent with State-reported causes of impairment. Tolerance units for benthic macroinvertebrate and fish assemblages were significantly correlated for water temperature (P = 0.001, r = 0.63), dissolved oxygen (P = 0.001, r = 0.61), and total phosphorus (P = 0.001, r = 0.61), but not for nitrate (P = 0.059, r = -0.35). Differences between the two assemblages in site-specific diagnosis of water-quality stressors may be the result of differences in nitrate tolerance.

Ecological Indicators↗

USGS2017

Incorporating population viability models into species status assessment and listing decisions under the U.S. Endangered Species Act

Assessment of a species' status is a key part of management decision making for endangered and threatened species under the U.S. Endangered Species Act. Predicting the future state of the species is an essential part of species status assessment, and projection models can play an important role in developing predictions. We built a stochastic simulation model that incorporated parametric and environmental uncertainty to predict the probable future status of the Sonoran desert tortoise in the southwestern United States and North Central Mexico. Sonoran desert tortoise was a Candidate species for listing under the Endangered Species Act, and decision makers wanted to use model predictions in their decision making process. The model accounted for future habitat loss and possible effects of climate change induced droughts to predict future population growth rates, abundances, and quasi-extinction probabilities. Our model predicts that the population will likely decline over the next few decades, but there is very low probability of quasi-extinction less than 75 years into the future. Increases in drought frequency and intensity may increase extinction risk for the species. Our model helped decision makers predict and characterize uncertainty about the future status of the species in their listing decision. We incorporated complex ecological processes (e.g., climate change effects on tortoises) in transparent and explicit ways tailored to support decision making processes related to endangered species.

Sonoran Desert↗

USGS2025

Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales

The boreal ecosystem has experienced significant changes over recent decades as wildfires become more frequent, intense, and severe. As streams are highly prevalent and ecologically relevant, understanding interactions among wildfire and hydrologic patterns is important for effective aquatic ecosystem management. This study used a Bayesian mixture model to classify streamflow regimes from modeled streamflow data for 32,730 stream reaches (totaling 295,880 km) across the Yukon and Kuskokwim basins and the Northwestern Boreal Ecosystem in Alaska, USA, and Yukon Territory, Canada. We assessed time since burn and calculated the total length of stream (km) within burn perimeters for each streamflow class from 1985 to 2015. Additionally, we used field observations (2018–2022) to compare streamflow regimes in four burned and four unburned headwater streams (drainage basins ≤150 km 2 ) in interior Alaska. Modeled stream reaches were grouped into twenty-two classes and reduced to eleven metaclasses based on similarities in streamflow statistics. These metaclasses formed two broad groups: 1) large rivers with lower variability and strong seasonal signals, and 2) mid- to small-sized tributaries with high variability, frequent high flow events, and weaker seasonal signals. The stream length burned analysis indicated an average increase of 47 km per year with first- and second-order streams experiencing more frequent fire. Empirical streamflow metrics from headwater stream gages revealed additional differences in streamflow patterns between burned and unburned streams. This streamflow classification establishes a baseline for understanding boreal stream responses to wildfire, detecting climate-induced regime shifts, and facilitating management and conservation of important boreal aquatic species.

Alaska↗

USGS2022

Climate disequilibrium dominates uncertainty in long-term projections of primary productivity

Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.

Ecology Letters↗

USGS2024

Climate change scenarios for air and water temperatures in the upper San Francisco Estuary: Implications for thermal regimes and Delta Smelt

Climate projections and their effects in the San Francisco Estuary have been evaluated as part of the US Geological Survey’s CASCaDE2 project. Understanding the ecological effects of climate change can help manage and maintain the ecological health and productivity of the San Francisco Estuary. In this study, we assessed downscaled air temperature data from 10 global climate models (GCMs) under two representative concentration pathway (RCP) trajectories for greenhouse gas concentrations for three regions of the San Francisco Estuary: Sacramento–San Joaquin Delta, Suisun and Grizzly bays, and Suisun Marsh. We also used previously derived regression models to estimate future water temperatures at 16 locations in the upper San Francisco Estuary. We used a thermal regime approach to summarize water temperature projections to investigate changes to the thermal regime of the upper San Francisco Estuary, and used the Delta Smelt (Hypomesus transpacificus) to demonstrate the effects that a warming climate may have on the habitat needs of this fish species. Our results suggested there were no major differences in the extent of air-temperature warming among the three regions. Annual average air temperatures were projected to increase approximately 2.0 °C and 4.7 °C by the end of the century for the low and high RCP scenarios, respectively. We found timing, frequency, and magnitude metrics varied by period and RCP scenario, while duration and variability metrics varied by space for water-temperature thermal regimes. For example, the spawning window for Delta Smelt (thermal-regime duration metric) is projected to expand in the future, with spawning starting earlier for both RCP scenarios for most sites. Although our thermal-regime analysis focused on the life history of Delta Smelt, similar approaches could be used to assess climate-change threats to a wide array of native and invasive terrestrial and aquatic species found in San Francisco Estuary.

California↗

USGS2014

Model distribution of Silver Chub ( Macrhybopsis storeriana ) in western Lake Erie

Silver Chub ( Macrhybopsis storeriana ) was once a common forage fish in Lake Erie but has declined greatly since the 1950s. Identification of optimal and marginal habitats would help conserve and manage this species. We developed neural networks to use broad-scale habitat variables to predict abundance classes of Silver Chub in western Lake Erie, where its largest remaining population exists. Model performance was good, particularly for predicting locations of habitat with the potential to support the highest and lowest abundances of this species. Highest abundances are expected in waters >5 m deep; water depth and distance to coastal habitats were important model features. These models provide initial tools to help conserve this species, but their resolution can be improved with additional data and consideration of other ecological factors.

American Midland Naturalist↗

USGS2010

Variation in δ 13 C and δ 15 N diet–vibrissae trophic discrimination factors in a wild population of California sea otters

The ability to quantify dietary inputs using stable isotope data depends on accurate estimates of isotopic differences between a consumer (c) and its diet (d), commonly referred to as trophic discrimination factors (TDFs) and denoted by Δ c-d . At present, TDFs are available for only a few mammals and are usually derived in captive settings. The magnitude of TDFs and the degree to which they vary in wild populations is unknown. We determined δ 13 C and δ 15 N TDFs for vibrissae (i.e., whiskers), a tissue that is rapidly becoming an informative isotopic substrate for ecologists, of a wild population of sea otters for which individual diet has been quantified through extensive observational study. This is one of the very few studies that report TDFs for free-living wild animals feeding on natural diets. Trophic discrimination factors of 2.2 ± 0.7 for δ 13 C and 3.5 ± 0.6 for δ 15 N (mean ± SD) were similar to those reported for captive carnivores, and variation in individual δ 13 C TDFs was negatively but significantly related to sea urchin consumption. This pattern may relate to the lipid-rich diet consumed by most sea otters in this population and suggests that it may not be appropriate to lipid-extract prey samples when using the isotopic composition of keratinaceous tissues to examine diet in consumers that frequently consume lipid-rich foods, such as many marine mammals and seabirds. We suggest that inherent variation in TDFs should be included in isotopically based estimates of trophic level, food chain length, and mixing models used to quantify dietary inputs in wild populations; this practice will further define the capabilities and limitations of isotopic approaches in ecological studies.

Ecological Applications↗

USGS2015

Winter habitat associations of blackbirds and starlings wintering in the south-central United States

Birds can cause extensive crop damage in the United States. In some regions, depredating species comprise a substantial portion of the total avian population, emphasizing their importance both economically and ecologically. We used the National Audubon Society Christmas Bird Count data from the south-central United States and mixed-effects models to identify habitat factors associated with population trend and abundance for 5 species: red-winged blackbird ( Agelaius phoeniceus ), common grackle ( Quiscalus quiscula ), rusty blackbird ( Euphagus carolinus ), Brewer’s blackbird ( Euphagus cyanocephalus ), and European starling ( Sturnus vulgaris ). Overall, we found positive associations between bird abundance and agricultural land-cover for all species. Relationships between abundance and other land-cover types were species-specific, often with contrasting relationships among species. Likewise, we found no consistent patterns among abundance and climate. Of the 5 species, only red-winged blackbirds had a significant population trend in our study area, increasing annually by 2.4%. There was marginal evidence to suggest population increases for rusty blackbirds, whereas all other species showed no trend in population size within our study area. Our study provides managers who are interested in limiting crop damage in the south-central United States with novel information on habitat associations in the region that could be used to improve management and control actions.

Alabama, Arkansas, Kansas, Louisiana, Mississippi,↗

USGS2020

Assessing the risks posed by SARS-CoV-2 in and via North American bats — Decision framing and rapid risk assessment

The novel β-coronavirus, SARS-CoV-2, may pose a threat to North American bat populations if bats are exposed to the virus through interaction with humans, if the virus can subsequently infect bats and be transmitted among them, and if the virus causes morbidity or mortality in bats. Further, if SARS-CoV-2 became established in bat populations, it could possibly serve as a source for new infection in humans, domesticated animals, or other wild animals. Wildlife management agencies in the United States are concerned about these potential risks and have begun to issue guidance regarding work that brings humans into contact with bats, but decision making is difficult because of the high degree of uncertainty about many of the relevant processes that could lead to virus transmission and establishment. The risk assessment described in this report was undertaken to provide management agencies with an understanding of the likelihood that the various steps in the causal pathways would lead to SARS-CoV-2 infection of North American bats from people. This assessment focused on the active season for bats in the temperate zone of North America (April 15 through November 15), and used Myotis lucifugus (little brown bats) as a surrogate species. At the time of this work (April 2020), no empirical data about the effects of SARS-CoV-2 on North American bats were available, so a formal process of expert judgment was used to elicit estimates of the underlying parameters. Twelve experts in bat ecology, epidemiology, virology, and wildlife disease from the United States, United Kingdom, and Australia participated in the elicitation. A Monte Carlo simulation model was used to integrate the parameter estimates elicited from the experts and to predict the likelihood of exposure and infection in bats through a series of transmission pathways, with particular attention to capturing uncertainty in the predictions. Given the current state of knowledge as expressed by the expert panel, the results of this assessment indicate that there is a non-negligible risk of transmission of SARS-CoV-2 from humans to bats. For example, if a research scientist were shedding SARS-CoV-2 virus while handling bats under the field protocols used in North America prior to the COVID-19 pandemic, the risk model indicates that 50 percent (uncertainty, 15–84 percent) of those bats could be exposed to virus, and 17 percent (uncertainty, 3–51 percent) could become infected. Use of personal protective equipment, especially a respirator, is expected to reduce the exposure risk. The expert panel estimated that exposure risk from research scientists could be reduced 94–96 percent (uncertainty, 86–99 percent) through proper use of appropriate N95 respirators (a type of mechanical filter worn over the nose and mouth), dedicated clothing (such as Tyvek coveralls), and gloves. Should any North American bats become infected with SARS-CoV-2, the expert panel estimated that there is an approximately 33-percent chance the virus could spread within a bat population. This study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, identified several critical uncertainties that could affect the estimate of risks associated with SARS-CoV-2 entering bat populations—notably, the underlying probability that a human would be shedding virus while working with bats, the likelihood of the virus replicating in bat tissue, and the likelihood of transmission of the virus within bat populations. Ongoing empirical work during May–October 2020 may shed light on these issues. Follow-up work is needed to better understand the probability of transmission of SARS-CoV-2 to bats from the general public; the manner in which the probabilities of exposure, infection, and transmission would differ during hibernation compared to the breeding season; and the likelihood of important effects, like morbidity and mortality in bats, the possibility of zoonosis from a North American bat reservoir, and effects of and on other wildlife.

North America↗

USGS2021

Contrasting geographic patterns of ignition probability and burn severity in the Mojave Desert

The extent and frequency of fire has increased in many arid systems over the last century, with a large proportion of area in some regions undergoing transitions to novel conditions. Portions of the Mojave Desert in southwestern North America have undergone such transitions, most often from woody to herbaceous-dominated systems. These transitions have often been attributed to the proliferation of invasive annual grasses that promote more frequent fire, but recent evidence indicates that transitions can also occur independent of fire frequency if burn severity is high. In addition, high probability of ignition (i.e., potentially high fire frequency) and high burn severity may not always be geographically related. Therefore, our goals were to: (1) map potential burn severity, fire frequency, and probability of ignition across the Mojave; and, (2) evaluate spatial association among predicted burn severity, fire frequency and probability of ignition. We first mapped perimeters of 250 wildfires > 405 ha that occurred from 1972 to 2010, then extracted data on fire frequency (number of times burned from 1972 to 2010), burn severity (the difference Normalized Burn Ratio), and 15 predictor variables representing physiography, climate, ignition, and vegetation. Maximum entropy was used to predict probability of ignition and Random Forest models were used to predict dNBR and fire frequency. Areas with high burn severity and high ignition probability had opposite spatial trends; areas with high burn severity were predicted to predominantly be in the northwest part of the region whereas areas with high ignition probability were predicted to be in the northeast. The models indicate the existence of a number of spatially structured but temporally dynamic fire regimes throughout the Mojave Desert. Two prevalent and ecologically significant regimes include one with frequent fires of low to moderate severity and another with infrequent fire of high severity. Areas with high fire frequency are currently limited in extent (<1% total area). However, cover of invasive grasses can remain high decades after a burn of high or moderate severity, so grass-fire cycles could develop in areas where there may be expectations of infrequent fire as well as those with relatively high fire frequency.

Arizona, California, Nevada, Utah↗

USGS2011

Growth characteristics and Otolith analysis on Age-0 American Shad

Oregon, Washington↗

USGS2011

Growth characteristics and otolith analysis on age-0 American shad

Otolith microstructure analysis provides useful information on the growth history of fish (Campana and Jones 1992, Bang and Gronkjaer 2005). Microstructure analysis can be used to construct the size-at-age growth trajectory of fish, determine daily growth rates, and estimate hatch date and other ecologically important life history events (Campana and Jones 1992, Tonkin et al. 2008). This kind of information can be incorporated into bioenergetics modeling, providing necessary data for estimating prey consumption, and guiding the development of empirically-based modeling scenarios for hypothesis testing. For example, age-0 American shad co-occur with emigrating juvenile fall Chinook salmon originating from Hanford Reach and the Snake River in the lower Columbia River reservoirs during the summer and early fall. The diet of age-0 American shad appears to overlap with that of juvenile fall Chinook salmon (Chapter 1, this reoprt), but juvenile fall Chinook salmon are also known to feed on age-0 American shad in the reservoirs (USGS unpublished data). Abundant, energy-dense age-0 American shad may provide juvenile fall Chinook salmon opportunities for rapid growth during the time period when large number of age-0 American shad are available. Otolith analysis of hatch dates and the growth curve of age-0 American shad could be used to identify when eggs, larvae, and juveniles of specific size classes are temporally available as food for fall Chinook salmon in the lower Columbia River reservoirs. This kind of temporally and spatially explicit life history information is important to include in bioenergetics modeling scenarios. Quantitive estimates of prey consumption could be used with spatially-explicit estimates of prey abundance to construct a quantitative assessment of the age-0 American shad impact on a reservoir food web. Analysis of the age-0 American shad growth trajectory or individual growth records may show evidence of differential growth rates over time that may be linked to environmental conditions such as water temperature (Leach and Houde 1999, Meekan et al. 2003), size-selective mortality (Folkvord et al. 1997), developmental changes in metabolic rate (Bang and Gronkjaer 2005, Bochdanksy et al. 2005), feeding ability (Schmitt and Holbrook 1984, Luecke 1986, Johnson and Dropkin 1995, Johnson and Dropkin 1996), and intra- and inter-specific competition (Crecco and Savoy 1987, Marchand and Boisclair 1998, Gadomski and Wagner 2009). For example, environmental conditions associated with John Day reservoir may eliminate or reduce the availability of many aquatic and terrestrial insect prey types (Rondorf et al. 1990). Many juvenile fishes, including age-0 American shad and juvenile fall Chinook salmon may be foraging on limited insect prey in John Day Reservoir (Gadomski and Wagner 2009). Because larger insect prey has higher energy densities than most zooplankton prey, and insect availability may be limited in John Day reservoir, the growth of American shad may be constrained once fish grow to a size where they could exploit larger, more energy-dense insect prey (Mayer and Wahl 1997). Similarly, as age-0 American shad grow, they are able to forage on larger zooplankton with higher energy densities than smaller individuals of the same species, or other smaller-bodied zooplankton species (Schael et al. 1991, Mayer and Wahl 1997). Intra- and inter-specific demand for larger-bodied and higher energy zooplankton prey may reduce the availability of these prey items (Tabor et al. 1996). Constrained growth increments on the otolith microstructure of juvenile American shad or other planktivorous fish could help identify important interactions between fishes that may be linked to the year class strength of age-0 American shad and prey partitioning in John Day reservoir. The objective of this study was to determine time of hatch and size-at-age of age-0 American shad in lower Columbia River reservoirs for use with the American shad and fall Chinook salmon bioenergetic models. Size-at-age data on age-0 American shad can be used to generate quantitative estimates of prey consumption with the American shad bioenergetics model. Otolith microstructure analysis was used to provide reference points on the temporal availability of early life stages and sizes of American shad in the reservoir (Limburg 1996a,b, Limburg et al. 1999). Additional analyses on the age-0 American shad growth trajectory in John Day reservoir may reveal differential growth patterns during the early life history of these fish that are linked to developmental differences between individual fish, transient environmental conditions, or food web constraints (Limburg 1996a).

Idaho, Montana, Oregon, Washington, Wyoming↗

USGS2017

Bioenergetics models to estimate numbers of larval lampreys consumed by smallmouth bass in Elk Creek, Oregon

Nonnative fishes have been increasingly implicated in the decline of native fishes in the Pacific Northwest. Smallmouth Bass Micropterus dolomieu were introduced into the Umpqua River in southwest Oregon in the early 1960s. The spread of Smallmouth Bass throughout the basin coincided with a decline in counts of upstream-migrating Pacific Lampreys Entosphenus tridentatus . This suggested the potential for ecological interactions between Smallmouth Bass and Pacific Lampreys, as well as freshwater-resident Western Brook Lampreys Lampetra richardsoni . To evaluate the potential effects of Smallmouth Bass on lampreys, we sampled diets of Smallmouth Bass and used bioenergetics models to estimate consumption of larval lampreys in a segment of Elk Creek, a tributary to the lower Umpqua River. We captured 303 unique Smallmouth Bass (mean: 197 mm and 136 g) via angling in July and September. We combined information on Smallmouth Bass diet and energy density with other variables (temperature, body size, growth, prey energy density) in a bioenergetics model to estimate consumption of larval lampreys. Larval lampreys were found in 6.2% of diet samples, and model estimates indicated that the Smallmouth Bass we captured consumed 925 larval lampreys in this 2-month study period. When extrapolated to a population estimate of Smallmouth Bass in this segment, we estimated 1,911 larval lampreys were consumed between July and September. Although the precision of these estimates was low, this magnitude of consumption suggests that Smallmouth Bass may negatively affect larval lamprey populations.

Oregon↗

USGS2019

Spatial capture–recapture for categorically marked populations with an application to genetic capture–recapture

Recently introduced unmarked spatial capture–recapture (SCR), spatial mark–resight (SMR), and 2‐flank spatial partial identity models (SPIMs) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single‐flank photographs. Unmarked SCR, SMR, and SPIM share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations because a sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is related to two key ecological concepts, population density and home range size, which we quantify using a proposed Identity Diversity Index (IDI). We show that latent and partial identity SCR models produce imprecise and biased density estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate categorical, partially identifying covariates, which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this “categorical SPIM” via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher‐applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2‐flank SPIM, or other latent identity SCR models.

Ecosphere↗

USGS2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697 µ molCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Alaska↗

USGS2014

Evaluating coastal landscape response to sea-level rise in the northeastern United States: approach and methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

Open-File Report↗