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At least 937 records · Page 52Linked to original sources
USGS2013

Mark-resight abundance estimation under incomplete identification of marked individuals

Often less expensive and less invasive than conventional mark–recapture, so-called 'mark-resight' methods are popular in the estimation of population abundance. These methods are most often applied when a subset of the population of interest is marked (naturally or artificially), and non-invasive sighting data can be simultaneously collected for both marked and unmarked individuals. However, it can often be difficult to identify marked individuals with certainty during resighting surveys, and incomplete identification of marked individuals is potentially a major source of bias in mark-resight abundance estimators. Previously proposed solutions are ad hoc and will tend to underperform unless marked individual identification rates are relatively high (>90%) or individual sighting heterogeneity is negligible. Based on a complete data likelihood, we present an approach that properly accounts for uncertainty in marked individual detection histories when incomplete identifications occur. The models allow for individual heterogeneity in detection, sampling with (e.g. Poisson) or without (e.g. Bernoulli) replacement, and an unknown number of marked individuals. Using a custom Markov chain Monte Carlo algorithm to facilitate Bayesian inference, we demonstrate these models using two example data sets and investigate their properties via simulation experiments. We estimate abundance for grassland sparrow populations in Pennsylvania, USA when sampling was conducted with replacement and the number of marked individuals was either known or unknown. To increase marked individual identification probabilities, extensive territory mapping was used to assign incomplete identifications to individuals based on location. Despite marked individual identification probabilities as low as 67% in the absence of this territorial mapping procedure, we generally found little return (or need) for this time-consuming investment when using our proposed approach. We also estimate rookery abundance from Alaskan Steller sea lion counts when sampling was conducted without replacement, the number of marked individuals was unknown, and individual heterogeneity was suspected as non-negligible. In terms of estimator performance, our simulation experiments and examples demonstrated advantages of our proposed approach over previous methods, particularly when marked individual identification probabilities are low and individual heterogeneity levels are high. Our methodology can also reduce field effort requirements for marked individual identification, thus, allowing potential investment into additional marking events or resighting surveys.

Methods in Ecology and Evolution↗
USGS2023

Positioning aquatic animals with acoustic transmitters

Geolocating aquatic animals with acoustic tags has been ongoing for decades, relying on the detection of acoustic signals at multiple receivers with known positions to calculate a 2D or 3D position, and ultimately recreate the path of an aquatic animal from detections at fixed stations. This method of underwater geolocation is evolving with new software and hardware options available to help investigators design studies and calculate positions using solvers based predominantly on time-difference-of-arrival and time-of-arrival. We provide an overview of the considerations necessary to implement positioning in aquatic acoustic telemetry studies, including how to design arrays of receivers, test performance, synchronize receiver clocks and calculate positions from the detection data. We additionally present some common positioning algorithms, including both the free open-source solvers and the ‘black-box’ methods provided by some manufacturers for calculating positions. This paper is the first to provide a comprehensive overview of methods and considerations for designing and implementing better positioning studies that will support users, and encourage further knowledge advances in aquatic systems.

Methods in Ecology and Evolution↗
USGS2024

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High-resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double-counted plants) error in counts from high-resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post-wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high-resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting ( p ) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Methods in Ecology and Evolution↗
USGS2024

A transferable approach for quantifying benthic fish sizes and densities in annotated underwater images

1. Benthic fishes are a common target of scientific monitoring but are difficult to quantify because of their close association to bottom habitats that are hard to access. Advances in image-acquisition technologies, machine vision, and deep learning have made capturing and quantifying fishes with cameras increasingly feasible. We present a method and open-source software called ‘FishScale’ to estimate benthic fish lengths, numeric abundance, and biomass density in underwater environments assessed with down-looking monocular images. 2. ‘FishScale’ estimates fish abundances and size frequencies from near-nadir monocular images where fish have already been semantically segmented. The software accounts for lens distortion, underwater magnification effects, and fish body curvature to automatically estimate fish lengths and the areas of images where they were captured. Numeric and biomass density are estimated through a deterministic machine vision algorithm that requires a user-provided length-weight relationship for species of interest and calibration images. 3. Results from validation studies show that lengths and weights can be estimated with high accuracy and precision for round goby ( Neogobius melanostomus ) captured in distorted action camera images, and from large-bodied lake trout ( Salvelinus namaycush ) imaged with a machine vision camera. The real-world utility of the approach is demonstrated in a case study estimating round goby abundances and size frequencies along a 10.7-km transect surveyed with an autonomous underwater vehicle in Lake Michigan, USA. 4. Our validation studies demonstrate that the approach estimates benthic and benthopelagic fish lengths and weights with little bias and good accuracy and precision for species with much different body shapes and sizes. The method is applicable to data collected using a variety of nadir imaging approaches with widespread applications to fisheries monitoring and quantification of any species or object for which nadir images and working distances between the camera and feature of interest are available.

Illinois, Indiana, Michigan, Wisconsin↗
USGS2016

Trade-offs and efficiencies in optimal budget-constrained multispecies corridor networks

Conservation biologists recognize that a system of isolated protected areas will be necessary but insufficient to meet biodiversity objectives. Current approaches to connecting core conservation areas through corridors consider optimal corridor placement based on a single optimization goal: commonly, maximizing the movement for a target species across a network of protected areas. We show that designing corridors for single species based on purely ecological criteria leads to extremely expensive linkages that are suboptimal for multispecies connectivity objectives. Similarly, acquiring the least-expensive linkages leads to ecologically poor solutions. We developed algorithms for optimizing corridors for multispecies use given a specific budget. We applied our approach in western Montana to demonstrate how the solutions may be used to evaluate trade-offs in connectivity for 2 species with different habitat requirements, different core areas, and different conservation values under different budgets. We evaluated corridors that were optimal for each species individually and for both species jointly. Incorporating a budget constraint and jointly optimizing for both species resulted in corridors that were close to the individual species movement-potential optima but with substantial cost savings. Our approach produced corridors that were within 14% and 11% of the best possible corridor connectivity for grizzly bears (Ursus arctos) and wolverines (Gulo gulo) , respectively, and saved 75% of the cost. Similarly, joint optimization under a combined budget resulted in improved connectivity for both species relative to splitting the budget in 2 to optimize for each species individually. Our results demonstrate economies of scale and complementarities conservation planners can achieve by optimizing corridor designs for financial costs and for multiple species connectivity jointly. We believe that our approach will facilitate corridor conservation by reducing acquisition costs and by allowing derived corridors to more closely reflect conservation priorities.

Conservation Biology↗
USGS2021

A framework for allocating conservation resources among multiple threats and actions

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource allocation to address multiple threats when multiple management options are available, including mutually exclusive options. Formulating the decision as a combinatorial optimization problem, our framework takes as inputs the expected impact and cost of each threat for each action (including do nothing) and for each overall budget identifies the optimal action to take for each threat. We compared the optimal solution to an easy to calculate greedy algorithm approximation and a variety of plausible ranking schemes. We applied the framework to management of multiple introduced plant species in Australian alpine areas. We developed a model of invasion to predict the expected impact in 50 years for each species‐action combination that accounted for each species’ current invasion state (absent, localized, widespread); arrival probability; spread rate; impact, if present, of each species; and management effectiveness of each species‐action combination. We found that the recommended action for a threat changed with budget; there was no single optimal management action for each species; and considering more than one candidate action can substantially increase the management plan's overall efficiency. The approximate solution (solution ranked by marginal cost‐effectiveness) performed well when the budget matched the cost of the prioritized actions, indicating that this approach would be effective if the budget was set as part of the prioritization process. The ranking schemes varied in performance, and achieving a close to optimal solution was not guaranteed. Global sensitivity analysis revealed a threat's expected impact and, to a lesser extent, management effectiveness were the most influential parameters, emphasizing the need to focus research and monitoring efforts on their quantification.

Conservation Biology↗
USGS2023

Identifying predictors of translocation success in rare plant species

The fundamental goal of a rare plant translocation is to create self-sustaining populations with the evolutionary resilience to persist in the long-term. Yet most plant translocation syntheses focus on a few factors influencing short-term benchmarks of success (e.g., survival and reproduction). Short-term benchmarks can be misleading when trying to infer future growth and viability because the factors that promote establishment may differ from those required for long-term persistence. We assembled a large ( n = 275) and broadly representative dataset of well-documented and monitored (7.9 years on average) at-risk plant translocations to identify the most important site attributes, management techniques, and species traits for six life-cycle benchmarks and population metrics of translocation success. Using the random forest algorithm, we found that drivers of translocation outcomes varied across timeframes and metrics of success. Management techniques had the greatest relative influence on the attainment of life-cycle benchmarks and short-term population trends while site attributes and species traits were more important for population persistence and longer-term trends. Specifically, large founder sizes increased the potential for reproduction and recruitment into the next generation, while declining habitat quality and the outplanting of species with low seed production led to increased extinction risks and a reduction in potential reproductive output in the long-term, respectively. We also detected novel interactions between some of the most important drivers, such as an increased probability of next-generation recruitment in species with greater seed production rates, but only when coupled with large founder sizes. Since most significant barriers to plant translocation success can be overcome by improving techniques or resolving site-level issues through early intervention and management, we suggest that by combining long-term monitoring with adaptive management, translocation programs can enhance the prospects of achieving long-term success.

Conservation Biology↗
USGS2024

Advancing at-risk species recovery planning in an era of rapid ecological change with a transparent, flexible, and expert-engaged approach

In the face of unprecedented ecological changes, the conservation community needs strategies to recover species at risk of extinction. On the Island of Maui, we collaborated with species experts and managers to assist with climate-resilient recovery planning for 36 at-risk native plant species by identifying priority areas for the management of recovery populations. To do this, we developed a tailored spatial conservation prioritization (SCP) approach distinguished by its emphasis on transparency, flexibility, and expert (TFE) engagement. Our TFE SCP approach consisted of 2 iterative steps: first, the generation of multiple candidate conservation footprints (i.e., prioritization solutions) with a flexible greedy algorithm that reflects conservation practitioners’ priorities and, second, the selection of an optimal conservation footprint based on the consideration of trade-offs in expert-agreed criteria among footprints. This process maximized buy-in by involving conservation practitioners and experts throughout, from setting goals to reviewing optimization data, defining optimization rules, and designating planning units meaningful to practitioners. We minimized the conservation footprint area necessary to meet recovery goals while incorporating species-specific measures of habitat suitability and climate resilience and retaining species-specific information for guiding recovery efforts. Our approach reduced the overall necessary conservation area by 36%, compared with selecting optimal recovery habitats for each species separately, and still identified high-quality habitat for individual species. Compared with prioritizr (an existing SCP tool), our approach identified a conservation area of equal size but with higher quality habitat. By integrating the strengths of existing techniques in a flexible and transparent design, our approach can address natural resource management constraints and provide outputs suitable for local recovery planning, consequently enhancing engagement and buy-in from conservation practitioners and experts. It demonstrates a step forward in making conservation planning more responsive to real-world complexities and helps reduce barriers to implementation for local conservation practitioners.

Hawaii↗
USGS2025

Climate change refugia hotspots for priority species: A case study in East Africa

Natural resource managers and policymakers need actionable climate data to guide conservation decisions. Conserving climate change refugia, areas relatively buffered from contemporary climate change, is increasingly considered an effective strategy for adaptation. Despite tropical species facing heightened vulnerability to climate change, the tropics remain underserved in climate adaptation research. We coproduced with Tanzanian partners the first comprehensive assessment of climate change refugia across Tanzania through extensive consultation, in-person conversations, and field visits to priority ecosystems, ensuring our analysis addressed local conservation needs and decision-making contexts. We developed species distribution models for 33 terrestrial animal species using maximum entropy and boosted regression tree algorithms. We projected future suitable habitats for SSP126 and SSP585, for 2011–2040 and 2071–2100, using GFDL Earth System and the UK Earth System models. More than half under SSP126 and 79% of focal species under SSP585 lost their suitable habitat by 2100. Serengeti National Park, Northern Highlands Forest Reserve, and the Eastern Arc Mountains emerged as key climate change refugia, while other protected areas, including Kigosi and Ugalla River National Parks, had no climate change refugia. This assessment provides actionable insights for Tanzania's conservation prioritization while identifying critical research gaps in western and montane ecosystems.

Conservation Science and Practice↗
USGS2022

Biogeography of freshwater mussels (Bivalvia: Unionida) in Texas and implications on conservation biology

Aim Biogeography seeks to identify and explain the spatial distributions of species and has become an important tool used by conservationists to protect and manage aquatic organisms. Texas, located in the southwestern United States, is home to 52 species of freshwater mussels, 9 of which are endemic to Texas and 7 that are endemic to Texas and neighboring states or countries. There have been two major attempts to classify this fauna into biogeographical provinces; however, both efforts relied on limited distribution information and outdated taxonomy. To address both issues, we set out to delineate biogeographic provinces for freshwater mussels in Texas by using a comprehensive distributional dataset of >28,000 records and molecular information. Location Southwestern United States. Methods We compiled community and molecular data for 48 of the 52 freshwater mussel species that occur in Texas. We performed algorithmic hierarchal cluster analysis (HCA) and nonmetric multidimensional scaling (NMDS) based on Euclidean distance to identify biogeographic groupings. We conducted a similar analysis using molecular sequence data for our target species. Results Based on the results from community and molecular data, we identified seven biogeographic provinces for freshwater mussels in Texas: Great Plains, Mississippi Embayment, Sabine-Neches, Trinity-San Jacinto, Central Texas, Rio Grande and Coastal. However, the Coastal and Great Plains provinces were not included in our analysis and were recognized based on previous work. Main conclusions Our approach integrating community and molecular datasets provides a comprehensive assessment of the biogeography of freshwater mussels in Texas, which serves as a model for future biogeographic studies. Our findings also shed light on the ecological, evolutionary and geologic processes shaping freshwater mussel communities in Texas, which is important for the conservation of remaining biodiversity in the state.

Texas↗
USGS2024

Predictor importance in habitat suitability models for invasive terrestrial plants

Aim Due to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs. Location The contiguous United States. Methods We explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness. Results Human modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest-performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non-woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness. Main Conclusions This study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.

Diversity and Distributions↗
USGS2015

geoknife: Reproducible web-processing of large gridded datasets

Geoprocessing of large gridded data according to overlap with irregular landscape features is common to many large-scale ecological analyses. The geoknife R package was created to facilitate reproducible analyses of gridded datasets found on the U.S. Geological Survey Geo Data Portal web application or elsewhere, using a web-enabled workflow that eliminates the need to download and store large datasets that are reliably hosted on the Internet. The package provides access to several data subset and summarization algorithms that are available on remote web processing servers. Outputs from geoknife include spatial and temporal data subsets, spatially-averaged time series values filtered by user-specified areas of interest, and categorical coverage fractions for various land-use types.

Ecography↗
USGS2022

Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus)

An essential goal in conservation biology is delineating population units that maximize the probability of species persisting into the future and adapting to future environmental change. However, future-facing conservation concerns are often addressed using retrospective patterns that could be irrelevant. We recommend a novel landscape genomics framework for delineating future “Geminate Evolutionary Units” (GEUs) in a focal species: (1) identify loci under environmental selection, (2) model and map adaptive conservation units that may spawn future lineages, (3) forecast relative selection pressures on each future lineage, and (4) estimate their fitness and likelihood of persistence using geo-genomic simulations. Using this process, we delineated conservation units for the Yosemite toad ( Anaxyrus canorus ), a U.S. federally threatened species that is highly vulnerable to climate change. We used a genome-wide dataset, redundancy analysis, and Bayesian association methods to identify 24 candidate loci responding to climatic selection ( R 2 ranging from 0.09 to 0.52), after controlling for demographic structure. Candidate loci included genes such as MAP3K5, involved in cellular response to environmental change. We then forecasted future genomic response to climate change using the multivariate machine learning algorithm Gradient Forests. Based on all available evidence, we found three GEUs in Yosemite National Park, reflecting contrasting adaptive optima: YF-North (high winter snowpack with moderate summer rainfall), YF-East (low to moderate snowpack with high summer rainfall), and YF-Low-Elevation (low snowpack and rainfall). Simulations under the RCP 8.5 climate change scenario suggest that the species will decline by 29% over 90 years, but the highly diverse YF-East lineage will be least impacted for two reasons: (1) geographically it will be sheltered from the largest climatic selection pressures, and (2) its standing genetic diversity will promote a faster adaptive response. Our approach provides a comprehensive strategy for protecting imperiled non-model species with genomic data alone and has wide applicability to other declining species.

California↗
USGS2019

Biogeographic freshwater fish pattern legacy revealed despite rapid socio-economic changes in China

Understanding drivers of freshwater fish assemblages is critically important for biodiversity conservation strategies, especially in rapidly developing countries, which often have environmental protections lagging behind economic development. The influences of natural and human factors in structuring fish assemblages and their relative contributions are likely to change given the increasing magnitude of human activities. To discriminate natural and human drivers of fish diversity and assemblage patterns in developing countries with rapid socio-economic development, a dataset of 908 freshwater fish species and 13 metrics including three categories of both natural (i.e., biogeographic) and human drivers (i.e., economic growth, inland fisheries) in China were analysed with machine learning algorithms (i.e., self-organizing map, random forest). Here, we found that biogeographic drivers explained 21.8% of the observed fish assemblage patterns in China and remained stronger predictors when compared to human drivers (i.e., 15.6%, respectively). Freshwater fish species richness was positively correlated to rainfall, air temperature, surface water area and inland fisheries production but negatively correlated with urbanization. In addition, the strong structuring effects of climatic variables on Chinese fish richness patterns suggested that the fish assemblages could be particularly vulnerable to climate change. Our results showed that natural biogeographic factors still dominate in driving freshwater fish assemblage patterns despite increased human disturbances on aquatic ecosystems in a rapidly developing country. These findings consequently suggested that we should consider both natural (e.g., climate) and human (e.g., urbanization, inland fisheries) factors when establishing aquatic conservation strategies and priorities for developing countries that are experiencing rapid socio-economic changes.

Fish and Fisheries↗
USGS2019

Quantifying contributions to tournament catches among resident, stocked, and hybrid black basses (Micropterus spp.)

Millions of Florida bass, Micropterus floridanus Lesueur, are stocked annually into populations of largemouth bass, Micropterus salmoides Lacepède, to increase trophy fish abundance. However, little effort has related the role that resultant hybrids make to angler catches. Largemouth bass were sampled from an important recreational fishery subject to extensive Florida bass stocking to address the hypothesis that anglers capture Florida bass, largemouth bass and hybrids at rates equivalent to their overall abundance in the population. Fin clips obtained from tournament angling events ( n = 348) and boat-mounted electrofishing sampling ( n = 219) were screened at 38 species-diagnostic markers and individuals were assigned to genealogical classes using Bayesian clustering algorithms. No significant differences were identified between angler and electrofishing catches providing evidence that hybridised individuals stemming from a long-term stocking programme may constitute an important contribution to tournament angling catch.

Tennessee↗
USGS2016

A synthesis of radial growth patterns preceding tree mortality

Tree mortality is a key factor influencing forest functions and dynamics, but our understanding of the mechanisms leading to mortality and the associated changes in tree growth rates are still limited. We compiled a new pan-continental tree-ring width database from sites where both dead and living trees were sampled (2970 dead and 4224 living trees from 190 sites, including 36 species), and compared early and recent growth rates between trees that died and those that survived a given mortality event. We observed a decrease in radial growth before death in ca. 84% of the mortality events. The extent and duration of these reductions were highly variable (1–100 years in 96% of events) due to the complex interactions among study species and the source(s) of mortality. Strong and long-lasting declines were found for gymnosperms, shade- and drought-tolerant species, and trees that died from competition. Angiosperms and trees that died due to biotic attacks (especially bark-beetles) typically showed relatively small and short-term growth reductions. Our analysis did not highlight any universal trade-off between early growth and tree longevity within a species, although this result may also reflect high variability in sampling design among sites. The intersite and interspecific variability in growth patterns before mortality provides valuable information on the nature of the mortality process, which is consistent with our understanding of the physiological mechanisms leading to mortality. Abrupt changes in growth immediately before death can be associated with generalized hydraulic failure and/or bark-beetle attack, while long-term decrease in growth may be associated with a gradual decline in hydraulic performance coupled with depletion in carbon reserves. Our results imply that growth-based mortality algorithms may be a powerful tool for predicting gymnosperm mortality induced by chronic stress, but not necessarily so for angiosperms and in case of intense drought or bark-beetle outbreaks.

Global Change Biology↗
USGS2021

Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients

Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush ( Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of −20% to +27%), declines in perennial C 3 grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C 4 grasses. The largest declines in big sagebrush, perennial C 3 grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture-limited versus temperature-limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.

Global Change Biology↗
USGS2021

Assessment of NMR logging for estimating hydraulic conductivity in glacial aquifers

Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K . Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with 1 H, the time‐varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct‐push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger‐Doll Research (SDR) NMR‐ K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower‐ K ( K < 10 −4 m/s) intervals. We also evaluated the applicability of prior SDR model calibrations. We found the NMR calibration parameters varied with K , suggesting the SDR model does not incorporate all the properties of the pore space that control K . Thus, the expected range of K in an aquifer may need to be considered during calibration of NMR‐ K models. This study is the first step toward establishing NMR logging as an effective method for estimating K in glacial aquifers.

Groundwater↗