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At least 307 records · Page 17Linked to original sources

USGS2023

Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

Alaska, California, Delaware, Florida, Maine, Mary↗

USGS2023

Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub

Objective A central challenge in applied ecology is understanding how organisms are spatially and temporally distributed and how management might be tailored to maintain or restore species distributions. The niche concept is central to understanding species distributions, but the diversity of niche definitions requires that multiple dimensions be considered. For example, the Grinnellian niche concept focuses on environmental conditions that allow species to persist, the Eltonian niche concept stresses the influence of biotic interactions, and the fundamental niche concept considers both abiotic and biotic environmental features to define spaces that organisms could occupy. Methods We combined abiotic ( A ), biotic ( B ), and movement ( M ) information (collectively, BAM model) to map the multidimensional niche of Prairie Chub Macrhybopsis australis , a regionally endemic freshwater fish currently under review for listing under the Endangered Species Act. We estimated A using remotely sensed environmental riverscape variables, B using the spatial distribution of a hybridization zone between Prairie Chub and Shoal Chub M. hyostoma , and M using data from a mark–recapture study. Result The BAM model estimated the spatial extent of multiple niches, including the Grinnellian ( A ; extent = 944 km of river), Eltonian ( B ; 2974 km), and fundamental niche (overlap of A + B ; 645 km) niches. When A , B , and M components were combined, the estimated extent of the Prairie Chub niche was 645 km. Conclusion Our work shows that the realized, multidimensional niche of Prairie Chub includes medium to large rivers with high habitat connectivity in the upper–middle Red River basin upstream of the distribution of Shoal Chub. The current Prairie Chub distribution could be maintained by preventing further habitat fragmentation and maintaining the environmental gradient separating Prairie Chub from Shoal Chub. Expansion of the species distribution may be possible through restoration of longitudinal fluvial connectivity.

North American Journal of Fisheries Management↗

USGS2011

Trophic ecology and gill raker morphology of seven catostomid species in Iowa rivers

Understanding the trophic ecology of closely‐related species is important for providing insight on inter‐specific competition and resource partitioning. Although catostomids often dominate fish assemblages in lotic systems, little research has been conducted on their ecology. This study was developed to provide information on the trophic ecology of catostomids in several Iowa rivers. Food habits, diet overlap, and gill raker morphology were examined for highfin carpsucker Carpiodes velifer , quillback C. cyprinus , river carpsucker C. carpio , golden redhorse Moxostoma erythrurum, shorthead redhorse M. macrolepidotum , silver redhorse M. anisurum, and northern hogsucker Hypentelium nigricans sampled from four Iowa rivers (2009) . Diet overlap among all species was calculated with Morista’s index ( C ). Food habit niche width was quantified with Levin’s index ( B ) and similarity in gill raker morphology was compared with analysis of covariance. Values from Morista’s index suggested significant overlap in the diets of highfin carpsucker and river carpsucker ( C = 0.81), quillback and river carpsucker ( C = 0.66), and shorthead redhorse and silver redhorse ( C = 0.67). Levin’s index indicated that golden redhorse ( B = 0.32), quillback ( B = 0.53), and river carpsucker ( B = 0.41) had the most generalized feeding strategies as their food niche widths were substantially wider than the other species. Gill raker length and spacing were positively correlated with the standard length of the fish for all species (gill raker length: r 2 = 0.67–0.88, P ≤ 0.01; gill raker spacing: r 2 = 0.63–0.73, P ≤ 0.01). Slopes of regression of gill raker length and spacing to standard lengths were significantly (P ≤ 0.05) different among species, indicating that rates of change in gill raker morphology with body length varied among species. Differences in gill raker morphology likely allow catostomids to partition resources and reduce competitive interactions.

Journal of Applied Ichthyology↗

USGS2021

Techniques to improve ecological interpretability of black box machine learning models

Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous USA, as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate–response relationships. Additionally, interaction effects were quantified according to interaction strength (IAS) and Friedman’s H 2 "> H 2 statistic. Interpretable machine learning techniques are useful tools to open the black-box of gradient boosted trees in the environmental sciences. This finding is supported by our case study on the effect of impervious surface on the benthic condition, which agrees with previous results in the literature. Overall, the most important variables were ecoregion, bed stability, watershed area, riparian vegetation and catchment slope. These variables were also present in most identified interaction effects. In conclusion, graphical tools (PDP, ICE, ALE) enable visualization and easier interpretation of GBT but should be supported by analytical statistical measures. Future methodological research is needed to investigate the properties of interaction tests. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗

USGS2024

Strategic restoration planning for land birds in the Colorado River Delta, Mexico

Ecological restoration is an essential strategy for mitigating the current biodiversity crisis, yet restoration actions are costly. We used systematic conservation planning principles to design an approach that prioritizes restoration sites for birds and tested it in a riparian forest restoration program in the Colorado River Delta. Restoration goals were to maximize the abundance and diversity of 15 priority birds with a variety of habitat preferences. We built abundance models for priority birds based on the current landscape, and predicted bird distributions and relative abundances under a scenario of complete riparian forest restoration throughout our study area. Then, we used Zonation conservation planning software to rank this restored landscape based on core areas for all priority birds. The locations with the highest ranks represented the highest priorities for restoration and were located throughout the river reach. We optimized how much of the available landscape to restore by simulating restoration of the top 10–90% of ranked sites in 10% intervals. We found that total diversity was maximized when 40% of the landscape was restored, and mean relative abundance was maximized when 80% of the landscape was restored. The results suggest that complete restoration is not optimal for this community of priority birds and restoration of approximately 60% of the landscape would provide a balance between maximum relative abundance and diversity. Subsequent planning efforts will combine our results with an assessment of restoration costs to provide further decision support for the restoration-siting process. Our approach can be applied to any landscape-scale restoration program to improve the return on investment of limited economic resources for restoration.

Colorado River Delta↗

USGS2022

Wetland occupancy by duck broods in cropland-dominated landscapes of the United States Prairie Pothole Region

The Prairie Pothole Region (PPR) is globally important for breeding waterfowl but has been altered via wetland drainage and grassland conversion to accommodate agricultural land use. Thus, understanding the ecology of waterfowl in these highly modified landscapes is essential for their conservation. Brood occurrence is the cumulative outcome of key life-history events including pair formation and territory establishment, nest success, and early brood survival. We applied new technological advances in brood surveying methods to understand brood use of wetlands and how land use and wetland-specific factors influenced brood use of 413 wetlands in crop-dominated landscapes in the PPR of Iowa, Minnesota, North Dakota, and South Dakota, USA, during summers of 2018–2020. Dynamic occupancy models combining information from 2 visits throughout the year revealed no difference among the 4 states or between private and public lands, resulting in a region-wide annual wetland occupancy estimate of 0.41 (95% credible interval [CrI] = 0.26, 0.58). We assessed aquatic invertebrate forage availability, wetland and upland vegetation communities, and various water chemistry metrics in a subset ( n = 225) of these wetlands to evaluate how landscape and wetland-specific factors influenced occupancy. The amount of grassland surrounding wetlands was the only variable to influence occupancy at a landscape scale, while wetland size, invertebrates, fish, and vegetation communities influenced occupancy at finer scales. Closer scrutiny of wetland area revealed occupancy was greater in small wetlands after controlling for total wetland area. Our results indicate the greatest constraint on brood occupancy across crop-dominated landscapes of the PPR in the United States was the occurrence of semipermanent wetlands suitable for brood rearing. Other factors, such as wetland vegetation or surrounding land use, had minor intervening influences on duck brood use and ducks were distributed invariant of wetland ownership or broad spatial processes occurring among states. These results demonstrated wetland conservation and restoration strategies are likely to yield gains in annual duck broods across this vast, altered, and highly modified landscape.

Iowa, Minnesota, North Dakota, South Dakota↗

USGS2018

Assessment of Alaska rain-on-snow events using dynamical downscaling

The ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

Alaska↗

USGS2012

Natural growth and diet of known-age pallid sturgeon (Scaphirhynchus albus) early life stages in the upper Missouri River basin, Montana and North Dakota

Prior to anthropogenic modifications, the historic Missouri River provided ecological conditions suitable for reproduction, growth, and survival of pallid sturgeon Scaphirhynchus albus . However, little information is available to discern whether altered conditions in the contemporary Missouri River are suitable for feeding, growth and survival of endangered pallid sturgeon during the early life stages. In 2004 and 2007, nearly 600 000 pallid sturgeon free embryos and larvae were released in the upper Missouri River and survivors from these releases were collected during 2004–2010 to quantify natural growth rates and diet composition. Based on genetic analysis and known-age at release (1–17 days post-hatch, dph), age at capture (dph, years) could be determined for each survivor. Totals of 23 and 28 survivors from the 2004 and 2007 releases, respectively, were sampled. Growth of pallid sturgeon was rapid (1.91 mm day −1 ) during the initial 13–48 dph, then slowed as fish approached maximum length (120–140 mm) towards the end of the first growing season. The diet of young-of-year pallid sturgeon was comprised of Diptera larvae, Diptera pupae, and Ephemeroptera nymphs. Growth of pallid sturgeon from ages 1–6 years was about 48.0 mm year −1 . This study provides the first assessment of natural growth and diet of young pallid sturgeon in the wild. Results depict pallid sturgeon growth trajectories that may be expected for naturally produced wild stocks under contemporary habitat conditions in the Missouri River and Yellowstone River.

Montana;North Dakota↗

USGS2017

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2020

Spatial and temporal diving behavior of non-breeding common murres during two summers of contrasting ocean conditions

Successful foraging of marine predators depends on environmental conditions, which also influence prey availability. Neutral or negative El Niño Southern Oscillation and Pacific Decadal Oscillation ocean conditions during the summer of 2013 and strongly positive conditions during the summer of 2015 in the northern California Current System provided a case study to evaluate a marine predator's response to anomalously warm conditions. We used satellite transmitters with saltwater switches to track movements and estimate dive behavior among non-breeding common murres (Uria aalge) off Oregon prior to and during a marine heatwave. We quantified differences in space-use between years, applied linear mixed models to determine environmental influences (e.g. sea surface temperature, surface salinity, chlorophyll a, ocean depth, and calendar date) on dive frequency and dive duration, and contrasted dive activity between time of day, year, and sexes. The majority of birds dispersed away from capture locations, which were situated near the southern range limit of their population. In both years, murres used the Salish Sea and the Columbia River plume; however, murres spent more time foraging in the Columbia River plume and in continental slope habitat during the marine heatwave of 2015. During 2015, dive frequency was reduced, and dive durations were almost twice as long during daytime indicating deeper or more dispersed prey. Increased dive frequency was positively associated with temperature, chlorophyll a, and crepuscular periods. Cluster analysis of dive activity and the top-ranked predictive dive duration model revealed associations between longer-duration dives and decreased dive frequency, marine slope habitat, and cooler ocean temperatures. Murres were relatively inactive throughout the night and we found no sex differences in dive activity. Changes in common murre foraging tactics were associated with ocean warming and revealed selectivity in spatial and temporal use of foraging habitats. Productive marine features including the Columbia River plume provided refuge for murres during apparently poor ocean conditions associated with the marine heatwave. Identifying refuge areas used by highly mobile species experiencing varying ocean conditions is critical for adaptive marine spatial planning that can accommodate a changing ocean climate.

California↗

USGS2021

Using fecal DNA and closed-capture models to estimate feral horse population size

Accurate population estimates provide the foundation for managing feral horses ( Equus caballus ferus ) across the western United States. Certain feral horse populations are protected by the Wild and Free-Roaming Horses and Burros Act of 1971 and managed by the Bureau of Land Management (BLM) or the United States Forest Service on designated herd management areas (HMAs) or wild horse territories, respectively. Horses are managed to achieve an appropriate management level (AML), which represents the number of horses determined by BLM to contribute to a thriving natural ecological balance and avoid deterioration of the range. To achieve AML for each HMA, BLM resource managers need accurate and precise population estimates. We tested the use of non-invasive fecal samples in a genetic capture-recapture framework to estimate population size in a closed horse population at the Little Book Cliffs HMA, Colorado, USA, with a known size of 153 individuals. We collected 1,957 samples over 3 independent sampling periods in 2014 and amplified them at 8 microsatellite loci. We applied mark-recapture models to determine population size using 954 samples that amplified at all 8 loci. We subsampled and reanalyzed our dataset to simulate different data collection protocols and evaluated effects on accuracy and precision of estimates using N-mixture modeling, full likelihood closed-capture modeling, and capwire single-occasion modeling that used data from all 3 sampling periods. Our model results were accurate and precise for analyses that used data from all 3 occasions; however, capwire single-occasion modeling was not accurate when we analyzed each sampling period separately. For all subsampling analysis scenarios, reducing sample size decreased precision, whether by reducing number of field staff, field days, or geographic areas surveyed on each period. Reducing spatial coverage of the survey area did not result in accurate population estimates and only marginally lowered the number of samples that would need to be collected to maintain accuracy. Because laboratory analysis contributes the greatest expense for this method ($80 U.S./sample), reducing fecal sample size is advantageous. Our results demonstrate that non-invasive sampling combined with good survey design and careful genetic and capture-recapture analyses can provide an alternative method to estimate the number of feral horses in a closed population. This method may be especially appropriate in situations where aerial inventories are not practical or accurate because of low sighting conditions. But the higher costs associated with laboratory sample analyses may reduce the method's feasibility compared to helicopter surveys.

Colorado↗

USGS2015

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Journal of Animal Ecology↗

USGS2006

Predicting woodrat ( Neotoma ) responses to anthropogenic warming from studies of the palaeomidden record

Aim The influence of anthropogenic climate change on organisms is an area of great scientific concern. Increasingly there is recognition that abrupt climate transitions have occurred over the late Quaternary; studies of these shifts may yield insights into likely biotic responses to contemporary warming. Here, we review research undertaken over the past decade investigating the response of Neotoma (woodrats) body size and distribution to climate change over the late Quaternary (the last 40,000 years). By integrating information from woodrat palaeomiddens, historical museum specimens and field studies of modern populations, we identify potential evolutionary responses to climate change occurring over a variety of temporal and spatial scales. Specifically, we characterize climatic thresholds in the past that led to local species extirpation and/or range alterations rather than in situ adaptation, and apply them to anticipate potential biotic responses to anthropogenic climate change. Location Middens were collected at about 55 sites scattered across the western United States, ranging from about 34 to 46° N and about 104 to 116° W, respectively. Data for modern populations were drawn from studies conducted in Death Valley, California, Missoula, Montana and the Sevilleta LTER site in central New Mexico. Methods We analysed faecal pellets from midden series collected at numerous cave sites across the western United States. From these we estimated body mass using techniques validated in earlier studies. We compared body size fluctuations at different elevations in different regions and integrated these results with studies investigating temperature–body size tradeoffs in modern animals. We also quantify the rapidity of the size changes over the late Quaternary to estimate the evolutionary capacity of woodrats to deal with predicted rates of anthropogenic climate change over the next century. Results We find remarkable similarities across the geographical range to late Quaternary climate change. In the middle of the geographical range woodrats respond in accordance to Bergmann's rule: colder climatic conditions select for larger body size and warmer conditions select for smaller body size. Patterns are more complicated at range boundaries, and local environmental conditions influence the observed response. In general, woodrat body size fluctuates with approximately the same amplitude and frequency as climate; there is a significant and positive correlation between woodrat body size and generalized climate proxies (such as ice core records). Woodrats have achieved evolutionary rates of change equal to or greater than those needed to adapt in situ to anthropogenic climate change. Main conclusions In situ body size evolution is a likely outcome of climate change, and such shifts are part of a normal spectrum of adaptation. Woodrats appear to be subject to ongoing body size selection in response to fluctuating environmental conditions. Allometric considerations suggest that these shifts in body size lead to substantial changes in the physiology, life history and ecology of woodrats, and on their direct and indirect interactions with other organisms in the ecosystem. Our work highlights the importance of a finely resolved and long-term record in understanding biotic responses to climatic shifts.

Journal of Biogeography↗

USGS2017

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

International Journal of Applied Earth Observation↗

USGS2015

Tidal marsh susceptibility to sea-level rise: importance of local-scale models

Increasing concern over sea-level rise impacts to coastal tidal marsh ecosystems has led to modeling efforts to anticipate outcomes for resource management decision making. Few studies on the Pacific coast of North America have modeled sea-level rise marsh susceptibility at a scale relevant to local wildlife populations and plant communities. Here, we use a novel approach in developing an empirical sea-level rise ecological response model that can be applied to key management questions. Calculated elevation change over 13 y for a 324-ha portion of San Pablo Bay National Wildlife Refuge, California, USA, was used to represent local accretion and subsidence processes. Next, we coupled detailed plant community and elevation surveys with measured rates of inundation frequency to model marsh state changes to 2100. By grouping plant communities into low, mid, and high marsh habitats, we were able to assess wildlife species vulnerability and to better understand outcomes for habitat resiliency. Starting study-site conditions were comprised of 78% (253-ha) high marsh, 7% (30-ha) mid marsh, and 4% (18-ha) low marsh habitats, dominated by pickleweed Sarcocornia pacifica and cordgrass Spartina spp. Only under the low sea-level rise scenario (44 cm by 2100) did our models show persistence of some marsh habitats to 2100, with the area dominated by low marsh habitats. Under mid (93 cm by 2100) and high sea-level rise scenarios (166 cm by 2100), most mid and high marsh habitat was lost by 2070, with only 15% (65 ha) remaining, and a complete loss of these habitats by 2080. Low marsh habitat increased temporarily under all three sea-level rise scenarios, with the peak (286 ha) in 2070, adding habitat for the endemic endangered California Ridgway’s rail Rallus obsoletus obsoletus . Under mid and high sea-level rise scenarios, an almost complete conversion to mudflat occurred, with most of the area below mean sea level. Our modeling assumed no marsh migration upslope due to human levee and infrastructure preventing these types of processes. Other modeling efforts done for this area have projected marsh persistence to 2100, but our modeling effort with site-specific datasets allowed us to model at a finer resolution with much higher local confidence, resulting in different results for management. Our results suggest that projected sea-level rise will have significant impacts on marsh plant communities and obligate wildlife, including those already under federal and state protection. Comprehensive modeling as done here improves the potential to implement adaptive management strategies and prevent marsh habitat and wildlife loss in the future.

California↗

USGS2016

Long-term lesser prairie-chicken nest ecology in response to grassland management

Long-term population and range declines from habitat loss and fragmentation caused the lesser prairie-chicken ( Tympanuchus pallidicinctus ) to be a species of concern throughout its range. Current lesser prairie-chicken range in New Mexico and Texas is partially restricted to sand shinnery oak ( Quercus havardii ; hereafter shinnery oak) prairies, on which cattle grazing is the main socioeconomic driver for private landowners. Cattle producers within shinnery oak prairies often focus land management on shrub eradication using the herbicide tebuthiuron to promote grass production for forage; however, herbicide application alone, and in combination with grazing, may affect nest site selection and nest survival of lesser prairie-chickens through the reduction of shinnery oak and native grasses. We used a controlled, paired, completely randomized design study to assess the influence of grazing and tebuthiuron application and their combined use on nest site selection and nest survival from 2001 to 2010 in Roosevelt County, New Mexico, USA at 2 spatial scales (i.e., treatment and microhabitat) in 4 treatments: tebuthiuron with grazing, tebuthiuron without grazing, no tebuthiuron with grazing, and a control of no tebuthiuron and no grazing. Grazing treatment was a short-duration system in which plots were grazed once during the dormant season and once during the growing season. Stocking rate was calculated each season based on measured forage production and applied to remove ≤25% of available herbaceous material per season. At the treatment scale, we compared nest site selection among treatments using 1-way χ 2 tests and nest survival among treatments using a priori candidate nest survival models in Program MARK. At the microhabitat scale, we identified important habitat predictors of nest site selection and nest survival using logistic regression and a priori candidate nest survival models in Program MARK, respectively. Females typically used treatments as expected and we did not detect trends in selection. Nest survival did not differ among treatments. At the microhabitat scale, nest sites had less bare ground ( P  = 0.001) and greater angles of obstruction ( P  ≤ 0.001) compared to random sites. There was a high degree of model selection uncertainty among our candidate models at the microhabitat scale and survival estimates were similar among habitat covariates. Results suggest a tebuthiuron application rate of 0.60 kg/ha, short-duration grazing, and a combination of these management techniques were not detrimental to lesser prairie-chicken nest site selection or nest survival. However, intensified management that increases bare ground or reduces overhead cover may negatively affect lesser prairie-chicken nesting habitat and nest survival.

Journal of Wildlife Management↗

USGS2016

Northern bobwhite breeding season ecology on a reclaimed surface mine

Surface coal mining and subsequent reclamation of surface mines have converted large forest areas into early successional vegetative communities in the eastern United States. This reclamation can provide a novel opportunity to conserve northern bobwhite ( Colinus virginianus ). We evaluated the influence of habitat management activities on nest survival, nest-site selection, and brood resource selection on managed and unmanaged units of a reclaimed surface mine, Peabody Wildlife Management Area (Peabody), in west-central Kentucky, USA, from 2010 to 2013. We compared resource selection, using discrete-choice analysis, and nest survival, using the nest survival model in Program MARK, between managed and unmanaged units of Peabody at 2 spatial scales: the composition and configuration of vegetation types (i.e., macrohabitat) and vegetation characteristics at nest sites and brood locations (i.e., microhabitat). On managed sites, we also investigated resource selection relative to a number of different treatments (e.g., herbicide, disking, prescribed fire). We found no evidence that nest-site selection was influenced by macrohabitat variables, but bobwhite selected nest sites in areas with greater litter depth than was available at random sites. On managed units, bobwhite were more likely to nest where herbicide was applied to reduce sericea lespedeza ( Lespedeza cuneata ) compared with areas untreated with herbicide. Daily nest survival was not influenced by habitat characteristics or by habitat management but was influenced by nest age and the interaction of nest initiation date and nest age. Daily nest survival was greater for older nests occurring early in the breeding season (0.99, SE < 0.01) but was lower for older nests occurring later in the season (0.08, SE = 0.13). Brood resource selection was not influenced by macrohabitat or microhabitat variables we measured, but broods on managed units selected areas treated with herbicide to control sericea lespedeza and were located closer to firebreaks and disked native-warm season grass stands than would be expected at random. Our results suggest the vegetation at Peabody was sufficient without manipulation to support nesting and brood-rearing northern bobwhite at a low level, but habitat management practices improved vegetation for nesting and brood-rearing resource selection. Reproductive rates (e.g., nest survival and re-nesting rates) at Peabody were lower than reported in other studies, which may be related to nutritional deficiencies caused by the abundance of sericea lespedeza. On reclaimed mine lands dominated by sericea lespedeza, we suggest continuing practices such as disking and herbicide application that are targeted at reducing sericea lespedeza to improve the vegetation for nesting and brood-rearing bobwhite.

Kentucky↗

USGS2010

Occam's shadow: levels of analysis in evolutionary ecology - where to next?

Evolutionary ecology is the study of evolutionary processes, and the ecological conditions that influence them. A fundamental paradigm underlying the study of evolution is natural selection. Although there are a variety of operational definitions for natural selection in the literature, perhaps the most general one is that which characterizes selection as the process whereby heritable variation in fitness associated with variation in one or more phenotypic traits leads to intergenerational change in the frequency distribution of those traits. The past 20 years have witnessed a marked increase in the precision and reliability of our ability to estimate one or more components of fitness and characterize natural selection in wild populations, owing particularly to significant advances in methods for analysis of data from marked individuals. In this paper, we focus on several issues that we believe are important considerations for the application and development of these methods in the context of addressing questions in evolutionary ecology. First, our traditional approach to estimation often rests upon analysis of aggregates of individuals, which in the wild may reflect increasingly non-random (selected) samples with respect to the trait(s) of interest. In some cases, analysis at the aggregate level, rather than the individual level, may obscure important patterns. While there are a growing number of analytical tools available to estimate parameters at the individual level, and which can cope (to varying degrees) with progressive selection of the sample, the advent of new methods does not reduce the need to consider carefully the appropriate level of analysis in the first place. Estimation should be motivated a priori by strong theoretical analysis. Doing so provides clear guidance, in terms of both (i) assisting in the identification of realistic and meaningful models to include in the candidate model set, and (ii) providing the appropriate context under which the results are interpreted. Second, while it is true that selection (as defined) operates at the level of the individual, the selection gradient is often (if not generally) conditional on the abundance of the population. As such, it may be important to consider estimating transition rates conditional on both the parameter values of the other individuals in the population (or at least their distribution), and population abundance. This will undoubtedly pose a considerable challenge, for both single- and multi-strata applications. It will also require renewed consideration of the estimation of abundance, especially for open populations. Thirdly, selection typically operates on dynamic, individually varying traits. Such estimation may require characterizing fitness in terms of individual plasticity in one or more state variables, constituting analysis of the norms of reaction of individuals to variable environments. This can be quite complex, especially for traits that are under facultative control. Recent work has indicated that the pattern of selection on such traits is conditional on the relative rates of movement among and frequency of spatially heterogeneous habitats, suggesting analyses of evolution of life histories in open populations can be misleading in some cases.

Journal of Applied Statistics↗