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At least 523 records · Page 29Linked to original sources
USGS2019

Quantifying trends and uncertainty in prehistoric forest composition

Forest ecosystems in eastern North America were in flux over the last several thousand years, well before Euro-American land clearance and the 20th-century onset of anthropogenic climate change. However, the magnitude and uncertainty of prehistoric vegetation change have been difficult to quantify because of the multiple ecological, dispersal, and sedimentary processes that govern the relationship between forest composition and fossil pollen assemblages. Here we extend STEPPS, a Bayesian hierarchical spatio-temporal pollen-vegetation model, to estimate changes in forest composition in the upper Midwestern United States from about 2000 to 200 years ago. Using this approach, we identify areas of statistically and ecologically significant change. Between 2000 and 200 years ago, forest composition significantly changed across broad regions of north-central Wisconsin and Minnesota. Rates of compositional change varied spatially, and can be linked to previously reported events. The single largest change is the infilling of Tsuga canadensis in northern Wisconsin over the past 2000 years. Despite this range in-filling, the range limit of T. canadensis was largely stable, with modest expansion westward. The regional ecotone between temperate hardwood forests and northern mixed hardwood/conifer forests shifted southwestward by 15-20 km in Minnesota and Northwestern Wisconsin. Fraxinus, Ulmus, and other mesic hardwoods expanded in the Big Woods region of southern Minnesota. However, some areas showed no significant change, suggesting high complexity in the spatiotemporal patterns of past forest dynamics. The increasing density of paleoecological data networks and advances in statistical modeling approaches now enables the confident detection of subtle but significant changes in forest composition over the last 2000 years.

Minnesota, Wisconsin↗
USGS2022

Streambank and floodplain geomorphic change and contribution to watershed material budgets

Stream geomorphic change is highly spatially variable but critical to landform evolution, human infrastructure, habitat, and watershed pollutant transport. However, measurements and process models of streambank erosion and floodplain deposition and resulting sediment fluxes are currently insufficient to predict these rates in all perennial streams over large regions. Here we measured long-term lateral streambank and vertical floodplain change and sediment fluxes using dendrogeomorphology in streams around the U.S. Mid-Atlantic, and then statistically modeled and extrapolated these rates to all 74 133 perennial, nontidal streams in the region using watershed- and reach-scale predictors. Measured long-term rates of streambank erosion and floodplain deposition were highly spatially variable across the landscape from the mountains to the coast. Random Forest regression identified that geomorphic change and resulting fluxes of sediment and nutrients, for both streambank and floodplain, were most influenced by urban and agricultural land use and the drainage area of the upstream watershed. Modeled rates for headwater streams were net erosional whereas downstream reaches were on average net depositional, leading to regional cumulative sediment loads from streambank erosion (−5.1 Tg yr −1 ) being nearly balanced by floodplain deposition (+5.3 Tg yr −1 ). Geomorphic changes in stream valleys had substantial influence on watershed sediment, phosphorus, carbon, and nitrogen budgets in comparison to existing predictions of upland erosion and delivery to streams and of downstream sediment loading. The unprecedented scale of these novel findings provides important insights into the balance of erosion and deposition in streams within disturbed landscapes and the importance of geomorphic change to stream water quality and carbon sequestration, and provides vital understanding for targeting management actions to restore watersheds.

Chesapeake Bay watershed↗
USGS2007

Modeling the spatial distribution of landslide-prone colluvium and shallow groundwater on hillslopes of Seattle, WA

Landslides in partially saturated colluvium on Seattle, WA, hillslopes have resulted in property damage and human casualties. We developed statistical models of colluvium and shallow-groundwater distributions to aid landslide hazard assessments. The models were developed using a geographic information system, digital geologic maps, digital topography, subsurface exploration results, the groundwater flow modeling software VS2DI and regression analyses. Input to the colluvium model includes slope, distance to a hillslope-crest escarpment, and escarpment slope and height. We developed different statistical relations for thickness of colluvium on four landforms. Groundwater model input includes colluvium basal slope and distance from the Fraser aquifer. This distance was used to estimate hydraulic conductivity based on the assumption that addition of finer-grained material from down-section would result in lower conductivity. Colluvial groundwater is perched so we estimated its saturated thickness. We used VS2DI to establish relations between saturated thickness and the hydraulic conductivity and basal slope of the colluvium. We developed different statistical relations for three groundwater flow regimes. All model results were validated using observational data that were excluded from calibration. Eighty percent of colluvium thickness predictions were within 25% of observed values and 88% of saturated thickness predictions were within 20% of observed values. The models are based on conditions common to many areas, so our method can provide accurate results for similar regions; relations in our statistical models require calibration for new regions. Our results suggest that Seattle landslides occur in native deposits and colluvium, ultimately in response to surface-water erosion of hillstope toes. Regional groundwater conditions do not appear to strongly affect the general distribution of Seattle landslides; historical landslides were equally dispersed within and outside of the area potentially affected by regional groundwater conditions.

Earth Surface Processes and Landforms↗
USGS2025

Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020

Frequency analysis on annual peak streamflow (hereinafter, peak flow) is essential to water-resources management applications, including critical structure design (for example, bridges and culverts) and floodplain mapping. Nonstationarity is a statistical property of a peak-flow series such that the distributional properties (the mean, variance, or skew) change either gradually (monotonic trend) or abruptly (shift, step change or change point) through time. Not incorporating or accounting for observed nonstationarity into peak-flow frequency analysis might result in a poor representation of the true probability of large floods and thus misrepresent the actual flood risks to life and property. This report summarizes how hydroclimatic variability might affect the temporal and spatial distributions of peak-flow data in the State of Montana (and northern Wyoming) and is part of a larger study to document peak-flow nonstationarity and hydroclimatic changes across a nine-State region consisting of Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flows. This study was completed in cooperation with the Montana Department of Natural Resources and Conservation. The purpose of this report is to characterize temporal and spatial patterns of nonstationarity in peak flows and hydroclimatology in Montana and northern Wyoming. In this evaluation, peak-flow, daily streamflow, and model-simulated gridded climatic data were examined for monotonic trends, change points, and other statistical properties that might indicate changing climatic and environmental conditions. This report includes background information on the study area, the history of U.S. Geological Survey peak-flow data collection and frequency analysis in Montana, and the review of research relating to hydroclimatic variability and change in Montana. This study might help provide a framework for addressing potential nonstationarity issues in peak-flow frequency updates that commonly are completed by the U.S. Geological Survey in cooperation with other agencies throughout the Nation. The analytical structure of this study includes analyses of monotonic trends and change points in numerous hydroclimatic variables in assigned 30-, 50-, 75-, and 100-year analysis periods. For Montana and part of Wyoming, the 30-, 50-, 75, and 100-year analyses included 157, 70, 48, and 12 streamgages, respectively. For those streamgages, nonstationarities were analyzed in the following variables: (1) climatic variables, including annual and seasonal (winter, spring, summer, and fall) temperature and precipitation; (2) daily streamflow variables, including the annual center of volume duration, annual center of volume median, and peaks over threshold with a mean of four events per year; and (3) annual peak-flow variables, including peak-flow timing and magnitude. A likelihood approach was used to express statistical confidence and assign the nonstationarity results as likely upward or downward (highest statistical confidence), somewhat likely upward or downward (less statistical confidence), or about as likely as not (little statistical confidence; hereinafter, neutral). For the nonstationarity analyses of the climatic, daily streamflow, and peak-flow variables, the results are presented in detail and discussed with respect to statewide patterns and geographic variability. For each of the 30-, 50-, and 75-year analyses, peak-flow change-point and monotonic trend analyses were compiled for streamgages classified with likely downward or likely upward trends. For those streamgages, the associated basin characteristics and nonstationarity results for peak-flow timing, daily streamflow, and climatic variables were investigated and statistically compared to discern associations among other variables that might contribute to the peak-flow nonstationarity results. The 50- and 75-year peak-flow nonstationarities identified in this study are mostly downward, in association with mostly upward temperature and potential evapotranspiration:precipitation monotonic trends. For the 50-, 75-, and 100-year analyses, the peak-flow change points are predominantly downward and are concentrated in the 1970s and 1980s, which indicates general consistency among the longer trend periods. These findings are in association with substantial research documenting globally rising temperature and atmospheric greenhouse gas concentrations that might be largely attributed to anthropogenic activities. Anthropogenic effects might represent long-term (on the order of several decades to more than a century) climate changes that might happen within highly variable natural climate fluctuations. Several paleo studies in the north-central United States have indicated that hydroclimatic extremes (that is, low- and high-streamflow conditions) before European settlement have been outside of extremes since the 1900s. Depending on the interactions of anthropogenic effects and natural climate variability, extreme high-streamflow conditions might occur in the future, even in the presence of long-term downward peak-flow trends.

Montana, Wyoming↗
USGS2026

Survey protocols drive heterogeneity in occupancy estimates that cannot be standardized using post hoc analyses

Site occupancy models are widely used to estimate species distributions from presence–nondetection data, yet for mobile animals, the true proportion of sites occupied varies in time. Thus, true occupancy and estimates generated from different protocols are affected by temporal characteristics of survey design. Here we tested whether we could standardize occupancy estimates across different sampling protocols with a post hoc statistical test. Using an individual-based movement model of Wood Thrush ( Hylocichla mustelina ), we simulated point count surveys across 3000 populations under 90 spatially and temporally variable sampling protocols. For each simulation, we fit a standard occupancy model, then used minute-level detections to truncate surveys and generate occupancy estimates across increasing survey lengths. We fit asymptotic models to these series to estimate intercepts (instantaneous occupancy) and asymptotes (longer-term occupancy) and evaluated how well they approximated true instantaneous, daily and seasonal occupancy. We were unable to recover instantaneous or seasonal occupancy from any approach. Both standard occupancy models and asymptotic regression (the top-performing asymptotic model) most closely approximated daily occupancy; although asymptotic regression produced slightly less biased estimates, it showed substantially greater variability, resulting in greater overall error. Our results show that post hoc analytical approaches cannot, at present, reliably standardize occupancy estimates across heterogeneous survey designs. Instead, ensuring biologically informative and comparable estimates will depend on standardized survey protocols. Practical implication : Monitoring programs using presence–nondetection data should prioritize consistent survey protocols when estimating occupancy for mobile species, with a focus on daily occupancy as the most attainable and useful metric. Although the highest accuracy and precision were achieved under the most intensive survey protocols, for point counts of Wood Thrush and similar species, four consecutive 15–20 min surveys provide near-optimal precision and can be completed in a single visit, making them well suited for estimating relative occupancy under logistical constraints. Three 20-min surveys spaced ~24 h apart closely approximate daily occupancy while maintaining reasonable precision. Developing similar protocols for other species and survey methods will be critical to ensuring comparable occupancy estimates across space and time.

Ecological Solutions and Evidence↗
USGS2026

Variability and consistency in wildfire susceptibility: Insights from a national compilation

Background Wildfire risk in the United States is rising and remains a land management priority. The quantitative wildfire risk assessment (QWRA) framework integrates fuels, topography, weather and values at risk to estimate the potential change in value from wildfire. Within this, response functions (RFs) represent how values respond to fire intensity. These are often based on expert judgment, but variation across assessments is unclear. Aims This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts. Methods We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale. Key results RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input. Conclusions Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs. Interpretations Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.

International Journal of Wildland Fire↗
USGS2020

Capturing spatiotemporal patterns in presence-absence data to inform monitoring and sampling designs for the threatened Dakota skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States

Declines among species of insect pollinators, especially butterflies, has garnered attention from scientists and managers. Often these declines have spurred governments to declare some species as threatened or endangered. We used existing presence–absence data from surveys for the threatened Dakota skipper Hesperia dacotae (Skinner) to build statistical maps of species presence that could be used to inform future monitoring designs. We developed a hierarchical Bayesian modeling approach to estimate the spatial distribution and temporal trend in Dakota skipper probability of presence. Our model included a spatial random effect and fixed effects for the proportion of two grassland habitat types: those on well-drained soils and those on poorly drained soils; as well as the topographic slope. The results from this model were then used to assess sampling strategies with two different monitoring objectives: locating new Dakota skipper colonies or monitoring the proportion of historically (pre-2000) extant colonies. Our modeling results suggested that the distribution of Dakota skippers followed the distribution of remnant grasslands and that probabilities of presence tended to be higher in topographically diverse grasslands with well-drained soils. Our analysis also showed that the probability of presence declined throughout the northern Great Plains range. Our simulations of the different sampling designs suggested that new detections were expected when sampling where Dakota skippers likely occurred historically, but this may lead to a tradeoff with monitoring existing sites. Prior information about the extant sites may help to ameliorate this tradeoff.

Iowa, Minnesota, North Dakota, South Dakota↗
USGS2022

Estimating species misclassification with occupancy dynamics and encounter rates: A semi-supervised, individual-level approach

1. Large-scale, long-term biodiversity monitoring is essential to conservation, land management, and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false positive/negative detection, and misclassification, where a species is thought to have been encountered but not correctly identified. Previous methods assume an imperfect classifier produces species-level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including `unknown', morphospecies designations, and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic ratess, occurrence, and species richness. 2. Here we present a joint classification-occupancy model that accounts for species non-detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain `morphospecies' designations, and makes use of individual specimens with known species identities in a semi-supervised setting. We compare the performance of our model to a classification-only model that discards information about occupancy and encounter rate. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, near Boulder, CO, USA. We also use simulations to evaluate model performance through validation metrics where varying fractions of the data are confirmed. 3. The model supported imperfect classifier accuracy and favored certain true species classifications strongly for some morphospecies. The model outperformed (e.g., precision) the reduced model that discarded occupancy information, and these differences were most pronounced for abundant species. 4. Spatial and temporal dynamics from modeled occupancy and encounter rates may inform species misclassification probability, but this idea has not yet been tested. Our statistical framework explores this opportunity, and can be applied to datasets with imperfect species detection and classification, limited verification data, and non-species classifications.

Methods in Ecology and Evolution↗
USGS2020

Disentangling the effects of multiple fires on spatially interspersed sagebrush (Artemisia spp.) communities

Questions Relative to a landscape with a mosaic of two sagebrush community types and increasing fire frequency, we asked: (a) do vegetation characteristics vary significantly with number of times burned for each sagebrush community; (b) how do vegetation responses to different fire frequencies compare between the two sagebrush communities? Location Columbia Plateau Ecoregion, Washington, USA. Methods We sampled vegetation across a landscape that burned three times over a 10‐year period in two sagebrush community types that are interspersed on unique land forms: big sagebrush ( Artemisia tridentata ) communities that occur on small “mounds” and scabland sagebrush ( A. rigida ) communities that occur on surrounding “flats.” Spatially overlapping fires permitted a balanced sampling design to assess unburned and once‐, twice‐, and thrice‐burned locations for each land form/community type. We utilized a suite of statistical analyses to determine differences among plant functional groups and biomass among unburned/burned strata by land form and compared results between land forms. Results Big sagebrush and scabland sagebrush communities responded uniquely to multiple fires, due to different fuel loadings, fire severities, succession and invasion dynamics. Big sagebrush experienced nearly complete shrub loss and conversion from exotic‐invaded shrubland to exotic annual grassland after only one fire. In contrast, scabland sagebrush retained a minor shrub component and higher relative cover of native herbaceous species, even after three fires. Both communities retained cover of native perennial grasses, including shallow‐ and deep‐rooted species, likely reflecting decreasing fire intensity with number of times burned. Conclusions Despite different community‐level responses, increasing fire frequency is transforming the entire landscape to a non‐native/native grassland mix. Quantifying unique ecosystem responses to altered wildfire regimes is critical to understanding the relative resilience of communities to disturbance and their resistance to exotic species invasion (and community type conversion). Management actions may help to maintain spatial heterogeneity of ecosystems and fire‐tolerant native species.

Washington↗
USGS1988

Thermochronology of economic mineral deposits: Dating the stages of mineralization at Panasqueira, Portugal, by high-precision 40Ar/ 39Ar age spectrum techniques on muscovite

40 Ar/ 39 Ar age spectrum dates for 13 muscovites have been used to reconstruct the thermal history (thermochronology) of the Panasqueira, Portugal, tin-tungsten deposit, a deposit spatially associated with a belt of Hercynian plutons. Muscovite samples with an age difference as small as 2.2 m.y. (0.7% of the age) are statistically distinct. Statistics are even better for comparison of multiple samples from separate events; that is, a difference of 0.9 m.y. (0.3%) can be resolved in this approximately 300-m.y.-old deposit. The major tin and tungsten ore-forming stages, which are the oxide-silicate stage, the main sulfide stage, and greisenization, occurred between 296.3 + or - 0.8 (1 Sigma ) and 291.6 + or - 0.8 m.y. (1 Sigma ). The first substage of the oxide-silicate stage was a short-lived thermal pulse at 296.3 + or - 0.6 m.y.; the fluids responsible may have emanated from the known granite cupola. The main sulfide stage was active at 294.5 + or - 0.9 m.y. as a slightly longer lived pulse with oldest evidence for this stage (295.8 + or - 0.6 m.y.) coming from areas farthest away from the known cupola and youngest evidence (293.5 + or - 0.8 m.y.) closest to the cupola. A second substage of the oxide-silicate stage occurred as a short-lived thermal pulse at 292.9 + or - 0.7 m.y., synchronous with greisenization of the cupola and alteration of the silica cap at 292.1 + or - 0.4 m.y. The duration of activity of the oxide-silicate stage, the main sulfide stage, greisenization, and alteration of the silica cap based on the ages of all 13 muscovites was greater than 4.2 + or - 0.5 m.y. (1 Sigma ). Minor argon loss from all dated muscovites occurred during later reheating, probably during the longer lived pyrrhotite alteration stage. A single center, the known cupola, had a prolonged role and was the source for main sulfide stage, oxide-silicate stage II, greisenization, and alteration of the silica cap and possibly oxide-silicate stage I and the pyrrhotite alteration stage; however, a separate source for these latter two stages cannot be ruled out.This study is an example of a new and powerful application of 40 Ar/ 39 Ar age spectrum dating of muscovite. Because of the high precision demonstrated in this study, it is now possible to establish time constraints necessary for solving some of the long-standing problems in economic geology. Beyond this, the unique geologic situation of Panasqueira has allowed us to quantify the thermal characteristics of muscovite. Published fluid inclusion data have been used to estimate a muscovite argon closure temperature of approximately 325 degrees C during rapid cooling or short reheating and a temperature of approximately 270 degrees C during slow cooling or extended reheating. Argon-loss patterns displayed by all dated muscovites resulted from reheating after original closure; the mechanism for this argon loss appears to have been argon transport by volume diffusion. Thus, 40 Ar/ 39 Ar age spectrum dating of muscovite can be used to evaluate thermal conditions controlling argon diffusion as well as age, duration, and number of episodes of mineralization.

Economic Geology↗
USGS2013

Salmonids, stream temperatures, and solar loading--modeling the shade provided to the Klamath River by vegetation and geomorphology

The U.S. Geological Survey is studying approaches to characterize the thermal regulation of water and the dynamics of cold water refugia. High temperatures have physiological impacts on anadromous fish species. Factors affecting the presence, variability, and quality of thermal refugia are known, such as riverine and watershed processes, hyporheic flows, deep pools and bathymetric factors, thermal stratification of reservoirs, and other broader climatic considerations. This research develops a conceptual model and methodological techniques to quantify the change in solar insolation load to the Klamath River caused by riparian and floodplain vegetation, the morphology of the river, and the orientation and topographic characteristics of its watersheds. Using multiple scales of input data from digital elevation models and airborne light detection and ranging (LiDAR) derivatives, different analysis methods yielded three different model results. These models are correlated with thermal infrared imagery for ground-truth information at the focal confluence with the Scott River. Results from nonparametric correlation tests, geostatistical cross-covariograms, and cross-correlograms indicate that statistical relationships between the insolation models and the thermal infrared imagery exist and are significant. Furthermore, the use of geostatistics provides insights to the spatial structure of the relationships that would not be apparent otherwise. To incorporate a more complete representation of the temperature dynamics in the river system, other variables including the factors mentioned above, and their influence on solar loading, are discussed. With similar datasets, these methods could be applied to any river in the United States—especially those listed as temperature impaired under Section 303(d) of the Clean Water Act—or international riverine systems. Considering the importance of thermal refugia for aquatic species, these methods can help investigate opportunities for riparian restoration, identify problematic reaches unlikely to provide good habitat, and simulate changes to solar loading estimates from alternative landscape configurations.

California↗
USGS2010

Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals

Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat ( Felis silvestris ) from cheek-rub lure sticks, extracted DNA from the samples, and identified each animals' genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture-recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km 2 (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home-range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap- and individual-level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture-recapture models will improve population assessments, especially for rare and elusive animals.

Conservation Biology↗
USGS2021

Snowpack signals in North American tree rings

Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn has intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually-resolved snowpack reconstructions from regional to high (<12 km) spatial scales in western North America, and by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.

Environmental Research Letters↗
USGS2016

Estimating black bear density in New Mexico using noninvasive genetic sampling coupled with spatially explicit capture-recapture methods

During the 2004–2005 to 2015–2016 hunting seasons, the New Mexico Department of Game and Fish (NMDGF) estimated black bear abundance (Ursus americanus) across the state by coupling density estimates with the distribution of primary habitat generated by Costello et al. (2001). These estimates have been used to set harvest limits. For example, a density of 17 bears/100 km2 for the Sangre de Cristo and Sacramento Mountains and 13.2 bears/100 km2 for the Sandia Mountains were used to set harvest levels. The advancement and widespread acceptance of non-invasive sampling and mark-recapture methods, prompted the NMDGF to collaborate with the New Mexico Cooperative Fish and Wildlife Research Unit and New Mexico State University to update their density estimates for black bear populations in select mountain ranges across the state. We established 5 study areas in 3 mountain ranges: the northern (NSC; sampled in 2012) and southern Sangre de Cristo Mountains (SSC; sampled in 2013), the Sandia Mountains (Sandias; sampled in 2014), and the northern (NSacs) and southern Sacramento Mountains (SSacs; both sampled in 2014). We collected hair samples from black bears using two concurrent non-invasive sampling methods, hair traps and bear rubs. We used a gender marker and a suite of microsatellite loci to determine the individual identification of hair samples that were suitable for genetic analysis. We used these data to generate mark-recapture encounter histories for each bear and estimated density in a spatially explicit capture-recapture framework (SECR). We constructed a suite of SECR candidate models using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We used Akaike’s Information Criterion corrected for small sample size (AICc) to rank and select the most supported model from which we estimated density. We set 554 hair traps, 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 M, 358 F) individuals; the sex ratio for each study area was approximately equal. Our density estimates varied within and among mountain ranges with an estimated density of 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) for the NSC, 19.74 bears/100 km2 (95% CI: 13.77 – 28.30) in the SSC, 25.75 bears/100 km2 (95% CI: 13.22 – 50.14) in the Sandias, 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) in the NSacs, and 16.55 bears/100 km2 (95% CI: 11.64 – 23.53) in the SSacs. Overall detection probability for hair traps and bear rubs, combined, was low across all study areas and ranged from 0.00001 to 0.02. We speculate that detection probabilities were affected by failure of some hair samples to produce a complete genotype due to UV degradation of DNA, and our inability to set and check some sampling devices due to wildfires in the SSC. Ultraviolet radiation levels are particularly high in New Mexico compared to other states where NGS methods have been used because New Mexico receives substantial amounts of sunshine, is relatively high in elevation (1,200 m – 4,000 m), and is at a lower latitude. Despite these sampling difficulties, we were able to produce density estimates for New Mexico black bear populations with levels of precision comparable to estimated black bear densities made elsewhere in the U.S. Our ability to generate reliable black bear density estimates for 3 New Mexico mountain ranges is attributable to our use of a statistically robust study design and analytical method. There are multiple factors that need to be considered when developing future SECR-based density estimation projects. First, the spatial extent of the population of interest and the smallest average home range size must be determined; these will dictate size of the trapping array and spacing necessary between hair traps. The number of technicians needed and access to the study areas will also influence configuration of the trapping array. We believe shorter sampling occasions could be implemented to reduce degradation of DNA due to UV radiation; this might help increase amplification rates and thereby increase both the number of unique individuals identified and the number of recaptures, improving the precision of the density estimates. A pilot study may be useful to determine the length of time hair samples can remain in the field prior to collection. In addition, researchers may consider setting hair traps and bear rubs in more shaded areas (e.g., north facing slopes) to help reduce exposure to UV radiation. To reduce the sampling interval it will be necessary to either hire more field personnel or decrease the number of hair traps per sampling session. Both of these will enhance detection of long-range movement events by individual bears, increase initial capture and recapture rates, and improve precision of the parameter estimates. We recognize that all studies are constrained by limited resources, however, increasing field personnel would also allow a larger study area to be sampled or enable higher trap density. In conclusion, we estimated the density of black bears in 5 study areas within 3 mountains ranges of New Mexico. Our estimates will aid the NMDGF in setting sustainable harvest limits. Along with estimates of density, information on additional demographic rates (e.g., survival rates and reproduction) and the potential effects that climate change and future land use may have on the demography of black bears may also help inform management of black bears in New Mexico, and may be considered as future areas for research.

New Mexico↗
USGS2021

Evaluating natural experiments in ecology: Using synthetic controls in assessments of remotely sensed land treatments

Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks that rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large‐scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational data sets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include (1) a discrete set of treated and untreated units, (2) a known date of treatment intervention, and (3) time series response data that include both pre‐ and post‐treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no‐action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large‐scale brush‐clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long‐term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.

Ecological Applications↗
USGS2021

Evaluation of a two-season banding program to estimate and model migratory bird survival

The management of North American waterfowl is predicated on long-term, continental scale banding implemented prior to the hunting season (i.e., July–September) and subsequent reporting of bands recovered by hunters. However, single-season banding and encounter operations have a number of characteristics that limit their application to estimating demographic rates and evaluating hypothesized limiting factors throughout the annual cycle. We designed and implemented a 2-season banding program for American black ducks ( Anas rubripes ), mallards ( A . platyrhynchos ), and hybrids in eastern North America to evaluate potential application to annual life cycle conservation and sport harvest management. We assessed model fit and compared estimates of annual survival among data types (i.e., pre-hunting season only [July–September], post-hunting season only [January–March], and 2-season [pre- and post-hunting season]) to evaluate model assumptions and potential application to population modeling and management. There was generally high agreement between estimates of annual survival derived using 2-season and pre-season only data for all age and sex cohorts. Estimates of annual survival derived from post-season banding data only were consistently higher for adult females and juveniles of both sexes. We found patterns of seasonal survival varied by species, age, and to a lesser extent, sex. Hunter recovered birds exhibited similar spatial distributions regardless of banding season suggesting banded samples were from the same population. In contrast, Goodness-Of-Fit tests suggest this assumption was statistically violated in some regions and years. We conclude that estimates of seasonal and annual survival for black ducks and mallards based on the 2-season banding program are valid and accurate based on model fit statistics, similarity in survival estimates across data and models, and similarities in the distribution of recoveries. The 2-season program provides greater precision and insight into the survival process and will improve the ability of researchers and managers to test competing hypotheses regarding population regulation resulting in more effective management.

Ecological Applications↗
USGS2019

Assessment of uncertainty in multi-model means of downscaled south Florida precipitation for projected (2019-2099) climate

South Florida resource management, particularly the Everglades restoration effort, is beginning to consider projections of precipitation from multiple climate models for decision-making. Because precipitation changes can significantly affect the Everglades ecosystem, characterization of precipitation projection uncertainty is important for resource management decisions, and reduction of uncertainty is desired for better decision-making. Though uncertainty of precipitation projections has been characterized for many regions, uncertainty has not been sufficiently quantified for south Florida. This study builds upon prior results for projected Florida precipitation by adding recent climate model simulations, seasonal and spatial information, and uncertainty quantification and reduction. We identify the multi-model mean change in south Florida precipitation and characterize the uncertainty of 37 statistically downscaled Coupled Model Intercomparison Project Phase 5 models. For 2019−45, there is a likely (over 60% of ensemble members) increase in south Florida annual mean precipitation owing to a likely to very likely (near 90% of ensemble members) increase in dry season (November, December, January) precipitation, while wet season (June, July, August) shows a more likely than not (over 50% of ensemble members) decrease in precipitation in southern region and increase in precipitation in northern region. As south Florida agencies are on the verge of including precipitation projections in their upcoming planning horizon, this information will aid south Florida practitioners in decisions influenced by future rainfall.

South Florida↗
USGS2018

Demographic characteristics of an avian predator, Louisiana Waterthrush (Parkesia motacilla), in response to its aquatic prey in a Central Appalachian USA watershed impacted by shale gas development

We related Louisiana Waterthrush ( Parkesia motacilla ) demographic response and nest survival to benthic macroinvertebrate aquatic prey and to shale gas development parameters using models that accounted for both spatial and non-spatial sources of variability in a Central Appalachian USA watershed. In 2013, aquatic prey density and pollution intolerant genera (i.e., pollution tolerance value <4) decreased statistically with increased waterthrush territory length but not in 2014 when territory densities were lower. In general, most demographic responses to aquatic prey were variable and negatively related to aquatic prey in 2013 but positively related in 2014. Competing aquatic prey covariate models to explain nest survival were not statistically significant but differed annually and in general reversed from negative to positive influence on daily survival rate. Potential hydraulic fracturing runoff decreased nest survival both years and was statistically significant in 2014. The EPA Rapid Bioassessment protocol (EPA) and Habitat Suitability Index (HSI) designed for assessing suitability requirements for waterthrush were positively linked to aquatic prey where higher scores increased aquatic prey metrics, but EPA was more strongly linked than HSI and varied annually. While potential hydraulic fracturing runoff in 2013 may have increased Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness, in 2014 shale gas territory disturbance decreased EPT richness. In 2014, intolerant genera decreased at the territory and nest level with increased shale gas disturbance suggesting the potential for localized negative effects on waterthrush. Loss of food resources does not seem directly or solely responsible for demographic declines where waterthrush likely were able to meet their foraging needs. However collective evidence suggests there may be a shale gas disturbance threshold at which waterthrush respond negatively to aquatic prey community changes. Density-dependent regulation of their ability to adapt to environmental change through acquisition of additional resources may also alter demographic response.

West Virginia↗