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Asking nicely: Best practices for requesting data

Compiling disparate datasets into publicly available composite databases helps natural resource communities explore ecological trends and effectively manage across spatiotemporal scales. Though some studies have reported on the database construction phase, fewer have evaluated the data acquisition and distribution process. To facilitate future data sharing collaborations, Louisiana State University surveyed data providers and requestors to understand the characteristics of effective data requests and sharing. Data providers were largely U.S. natural resource agency personnel, and they reported that unclear data requests, privacy issues, and rigid timelines and formats were the greatest barriers toward providing data, but that they were motivated by improving science and collaboration. Data requestors identified challenges such as evolving needs, standardization issues, and insufficient resources (time and funding) as barriers to compiling data for these types of efforts. In a time of big data, open access, and collaboration, significant scientific advances can be made with effective requests and inclusion of data sets into larger and more powerful databases.

Ecological Indicators

Detecting changes in riparian habitat conditions based on patterns of greenness change: A case study from the Upper San Pedro River Basin, USA

Healthy riparian ecosystems in arid and semi-arid regions exhibit shifting patterns of vegetation in response to periodic flooding. Their conditions also depend upon the amount of grazing and other human uses. Taking advantage of these system properties, we developed and tested an approach that utilizes historical Landsat data to track changes in the patterns of greenness (Normalized Difference Vegetation Index) within riparian zones. We tested the approach in the Upper San Pedro River of southeastern Arizona of the US, an unimpounded river system that flows north into the US from northern Mexico. We evaluated changes in the pattern of greenness in the San Pedro River National Conservation Area (SPRNCA), an area protected from grazing and development since 1988, and in a relatively unprotected area north of the SPRNCA (NA). The SPRNCA exhibited greater positive changes in greenness than did the NA. The SPRNCA also exhibited larger, more continuous patches of positive change than did the NA. These pattern differences may reflect greater pressures from grazing and urban sprawl in the NA than in the SPRNCA, as well as differences in floodplain width, depth to ground water, and base geology. The SPRNCA has greater amounts of ground and surface water available to support a riparian gallery forest than does the NA, and this may have influenced changes during the study period. Estimates of the direction of greenness change (positive or negative) from satellite imagery were similar to estimates derived from aerial photography , except in areas where changes were from one type of shrub community to another, and in areas with agriculture. Change estimates in these areas may be more difficult because of relatively low greenness values, and because of differences in soil moisture, sun-angle, and crop rotations among the dates of data collection. The potential for applying a satellite-based, greenness change approach to evaluate riparian ecosystem condition over broad geographic areas is also discussed.

Arizona

Spatial patterns of aquatic habitat richness in the Upper Mississippi River floodplain, USA

Interactions among hydrology and geomorphology create shifting mosaics of aquatic habitat patches in large river floodplains (e.g., main and side channels, floodplain lakes, and shallow backwater areas) and the connectivity among these habitat patches underpins high levels of biotic diversity and productivity. However, the diversity and connectivity among the habitats of most floodplain rivers have been negatively impacted by hydrologic and structural modifications that support commercial navigation and control flooding. We therefore tested the hypothesis that the rate of increase in patch richness (# of types) with increasing scale reflects anthropogenic modifications to habitat diversity and connectivity in a large floodplain river, the Upper Mississippi River (UMR). To do this, we calculated the number of aquatic habitat patch types within neighborhoods surrounding each of the ≈19 million 5-m aquatic pixels of the UMR for multiple neighborhood sizes (1&ndash;100 ha). For all of the 87 river-reach focal areas we examined, changes in habitat richness ( R ) with increasing neighborhood length ( L , # pixels) were characterized by a fractal-like power function R = L z ( R 2 > 0.92 ( P < 0.05)). The scaling exponent ( z ) measures the rate of increase in habitat richness with neighborhood size and is related to a fractal dimension. Variation in z reflected fundamental changes to spatial patterns of aquatic habitat richness in this river system. With only a few exceptions, z exceeded the river-wide average of 0.18 in focal areas where side channels, contiguous floodplain lakes, and contiguous shallow-water areas exceeded 5%, 5%, and 10% of the floodplain respectively. In contrast, z was always less than 0.18 for focal areas where impounded water exceeded 40% of floodplain area. Our results suggest that rehabilitation efforts that target areas with <5% of the floodplain in side channels, <5% in floodplain lakes, and/or <10% in shallow-water areas could improve habitat diversity across multiple scales in the UMR.

Upper Mississippi River

Temporal changes in aquatic-invertebrate and fish assemblages in streams of the north-central and northeastern U.S.

Many management agencies seek to evaluate temporal changes in aquatic assemblages at monitoring sites, but few have sites with ecological time series that are long enough for this purpose. Trends in aquatic-invertebrate and fish assemblage composition were assessed at 27 long-term monitoring sites in the north-central and northeastern United States. Temporal changes were identified using serial trend analysis. Sites with significant serial trends were further evaluated by relating explanatory environmental variables (e.g., streamflow, habitat, and water chemistry) to changes in assemblage composition. Significant trends were found at 19 of 27 study sites; however, differences in the sensitivity of the aquatic fauna to environmental stressors were identified. For example, significant trends in fish assemblages were found at more sites (15 of 27) than for aquatic-invertebrate assemblages (10 of 27 sites). In addition, trends in the invertebrate assemblage were most often explained by changes in streamflow processes (e.g., duration and magnitude of low- and high-flows, streamflow variability, and annual rates of change), whereas trends in the fish assemblage were more related to changes in water chemistry. Results illustrate the value of long-term monitoring for the purpose of assessing temporal trends in aquatic assemblages. The ability to detect trends in assemblage composition and to attribute these changes to environmental factors is necessary to understand mechanistic pathways and to further our understanding of how incremental anthropogenic alterations modify aquatic assemblages over time. Finally, this study's approach to trends analysis can be used to better inform the design of monitoring programs as well as support the ongoing management needs of stakeholders, water-resource agencies, and policy makers.

Connecticut, Illinois, Indiana, Iowa, Massachusett

Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO database over the Greater Platte River Basin, USA

This study assessed and described a relationship between satellite-derived growing season averaged Normalized Difference Vegetation Index (NDVI) and annual productivity for grasslands within the Greater Platte River Basin (GPRB) of the United States. We compared growing season averaged NDVI (GSN) with Soil Survey Geographic (SSURGO) database rangeland productivity and flux tower Gross Primary Productivity (GPP) for grassland areas. The GSN was calculated for each of nine years (2000–2008) using the 7-day composite 250-m eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. Strong correlations exist between the nine-year mean GSN (MGSN) and SSURGO annual productivity for grasslands (R2 = 0.74 for approximately 8000 pixels randomly selected from eight homogeneous regions within the GPRB; R2 = 0.96 for the 14 cluster-averaged points). Results also reveal a strong correlation between GSN and flux tower growing season averaged GPP (R2 = 0.71). Finally, we developed an empirical equation to estimate grassland productivity based on the MGSN. Spatially explicit estimates of grassland productivity over the GPRB were generated, which improved the regional consistency of SSURGO grassland productivity data and can help scientists and land managers to better understand the actual biophysical and ecological characteristics of grassland systems in the GPRB. This final estimated grassland production map can also be used as an input for biogeochemical, ecological, and climate change models.

Ecological Indicators

Evaluating the efficiency of environmental monitoring programs

Statistical uncertainty analyses can be used to improve the efficiency of environmental monitoring, allowing sampling designs to maximize information gained relative to resources required for data collection and analysis. In this paper, we illustrate four methods of data analysis appropriate to four types of environmental monitoring designs. To analyze a long-term record from a single site, we applied a general linear model to weekly stream chemistry data at Biscuit Brook, NY, to simulate the effects of reducing sampling effort and to evaluate statistical confidence in the detection of change over time. To illustrate a detectable difference analysis, we analyzed a one-time survey of mercury concentrations in loon tissues in lakes in the Adirondack Park, NY, demonstrating the effects of sampling intensity on statistical power and the selection of a resampling interval. To illustrate a bootstrapping method, we analyzed the plot-level sampling intensity of forest inventory at the Hubbard Brook Experimental Forest, NH, to quantify the sampling regime needed to achieve a desired confidence interval. Finally, to analyze time-series data from multiple sites, we assessed the number of lakes and the number of samples per year needed to monitor change over time in Adirondack lake chemistry using a repeated-measures mixed-effects model. Evaluations of time series and synoptic long-term monitoring data can help determine whether sampling should be re-allocated in space or time to optimize the use of financial and human resources.

Ecological Indicators

Bird migration and avian influenza: a comparison of hydrogen stable isotopes and satellite tracking methods

Satellite-based tracking of migratory waterfowl is an important tool for understanding the potential role of wild birds in the long-distance transmission of highly pathogenic avian influenza. However, employing this technique on a continental scale is prohibitively expensive. This study explores the utility of stable isotope ratios in feathers in examining both the distances traveled by migratory birds and variation in migration behavior. We compared the satellite-derived movement data of 22 ducks from 8 species captured at wintering areas in Bangladesh, Turkey, and Hong Kong with deuterium ratios (δD) in the feathers of these and other individuals captured at the same locations. We derived likely molting locations from the satellite tracking data and generated expected isotope ratios based on an interpolated map of δD in rainwater. Although δD was correlated with the distance between wintering and molting locations, surprisingly, measured δD values were not correlated with either expected values or latitudes of molting sites. However, population-level parameters derived from the satellite-tracking data, such as mean distance between wintering and molting locations and variation in migration distance, were reflected by means and variation of the stable isotope values. Our findings call into question the relevance of the rainfall isotope map for Asia for linking feather isotopes to molting locations, and underscore the need for extensive ground truthing in the form of feather-based isoscapes. Nevertheless, stable isotopes from feathers could inform disease models by characterizing the degree to which regional breeding populations interact at common wintering locations. Feather isotopes also could aid in surveying wintering locations to determine where high-resolution tracking techniques (e.g. satellite tracking) could most effectively be employed. Moreover, intrinsic markers such as stable isotopes offer the only means of inferring movement information from birds that have died as a result of infection. In the absence of feather based-isoscapes, we recommend a combination of isotope analysis and satellite-tracking as the best means of generating aggregate movement data for informing disease models.

Ecological Indicators

Interpretation of high-resolution imagery for detecting vegetation cover composition change after fuels reduction treatments in woodlands

The use of very high resolution (VHR; ground sampling distances < ∼5 cm) aerial imagery to estimate site vegetation cover and to detect changes from management has been well documented. However, as the purpose of monitoring is to document change over time, the ability to detect changes from imagery at the same or better level of accuracy and precision as those measured in situ must be assessed for image-based techniques to become reliable tools for ecosystem monitoring. Our objective with this study was to quantify the relationship between field-measured and image-interpreted changes in vegetation and ground cover measured one year apart in a Piñon and Juniper (P–J) woodland in southern Utah, USA. The study area was subject to a variety of fuel removal treatments between 2009 and 2010. We measured changes in plant community composition and ground cover along transects in a control area and three different treatments prior to and following P–J removal. We compared these measurements to vegetation composition and change based on photo-interpretation of ∼4 cm ground sampling distance imagery along similar transects. Estimates of cover were similar between field-based and image-interpreted methods in 2009 and 2010 for woody vegetation, no vegetation, herbaceous vegetation, and litter (including woody litter). Image-interpretation slightly overestimated cover for woody vegetation and no-vegetation classes (average difference between methods of 1.34% and 5.85%) and tended to underestimate cover for herbaceous vegetation and litter (average difference of −5.18% and 0.27%), but the differences were significant only for litter cover in 2009. Level of agreement between the field-measurements and image-interpretation was good for woody vegetation and no-vegetation classes (r between 0.47 and 0.89), but generally poorer for herbaceous vegetation and litter (r between 0.18 and 0.81) likely due to differences in image quality by year and the difficulty in discriminating fine vegetation and litter in imagery. Our results show that image interpretation to detect vegetation changes has utility for monitoring fuels reduction treatments in terms of woody vegetation and no-vegetation classes. The benefits of this technique are that it provides objective and repeatable measurements of site conditions that could be implemented relatively inexpensively and easily without the need for highly specialized software or technical expertise. Perhaps the biggest limitations of image interpretation to monitoring fuels treatments are challenges in estimating litter and herbaceous vegetation cover and the sensitivity of herbaceous cover estimates to image quality and shadowing.

Utah

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future

Sagebrush ( Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km 2 (1.1%) across the study area under the A1B scenario and 41.15 km 2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km 2 (4.1%) under A1B and 50.8 km 2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km 2 (3.8%) under A1B and 40.59 km 2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse ( Centrocercus urophasianus ) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.

Wyoming

Wetland habitat disturbance best predicts metrics of an amphibian index of biotic integrity

Regression and classification trees were used to identify the best predictors of the five component metrics of the Ohio Amphibian Index of Biotic Integrity (AmphIBI) in 54 wetlands in Ohio, USA. Of the 17 wetland- and surrounding landscape-scale variables considered, the best predictor for all AmphIBI metrics was habitat alteration and development within the wetland. The results were qualitatively similar to the best predictors for a wetland vegetation index of biotic integrity, suggesting that similar management practices (e.g., reducing or eliminating nutrient enrichment from agriculture, mowing, grazing, logging, and removing down woody debris) within the boundaries of the wetland can be applied to effectively increase the quality of wetland vegetation and amphibian communities.

Ecological Indicators

Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas

A major challenge in the biological monitoring of stream ecosystems in protected wilderness areas is discerning whether temporal changes in community structure are significantly outside of a reference condition that represents natural or acceptable annual variation in population cycles. Otherwise sites could erroneously be classified as impaired. Long-term datasets are essential for understanding these trends, to ascertain whether any changes in community structure significantly beyond the reference condition are permanent shifts or with time move back to within previous limits. To this end, we searched for long-term (>8 years) quantitative data sets of macroinvertebrate communities in wadeable rivers collected by similar methods and time of year in protected wilderness areas with minimal anthropogenic disturbance. Four geographic areas with datasets that met these criteria in the USA were identified, namely: McLaughlin Nature Reserve in California (1 stream), Great Smoky Mountains National Park in Tennesse-North Carolina (14 streams), Wind River Wilderness Areas in Wyoming (3 streams) and Denali National Park and Preserve in Alaska (6 streams). Two statistical approaches were applied: Taxonomic Distinctness (TD) to describe changes in diversity over time and non-metric multidimensional scaling (MDS) to describe changes over time in community persistence (Jaccards Index) and community stability (Bray&ndash;Curtis Index). Control charts were used to determine if years in MDS plots were significantly outside a reference condition. For Hunting Creek, TD showed three years outside natural variation which could be attributed to severe hydrological events but years outside the natural-variation funnel at sites in other geographical areas were inconsistent and could not be explained by environmental variables. TD identified simulated severe pollutant events which caused the removal of entire invertebrate assemblages but not simulated water temperature shifts. Within a region, both MDS analyses typically identified similar years as exceeding reference condition variation, illustrating the utility of the approach for identifying wider spatial scale effects that influence more than one stream. MDS responded to both simulated water temperature stress and a pollutant event, and generally outlying years on MDS plots could be explained by environmental variables, particularly higher precipitation. Multivariate control charts successfully identified whether shifts in community structure identified by MDS were significant and whether the shift represented a press disturbance (long-term change) or a pulse disturbance. We consider a combination of TD and MDS with control charts to be a potentially powerful tool for determining years significantly outside of a reference condition variation.

Alaska, California, North Carolina, Tennesse, Wyom

Structure and spatial patterns of macrobenthic community in Tai Lake, a large shallow lake, China

Tai Lake (Chinese: Taihu ), the third-largest freshwater lake in China, suffers from harmful cyanobacteria blooms that are caused by economic development and population growth near the lake. Several studies have focused on phytoplankton in Tai Lake after a drinking water crisis in 2007; however, these studies primarily focused on microcystin bioaccumulation and toxicity to individual species without examining the effects of microcystin on macrobenthic community diversity. In this study, we conducted a survey of the lake to examine the effects of microcystine and other pollutants on marcobenthic community diversity. A totally of forty-nine species of macroinvertebrates were found in Tai Lake. Limnodrilus hoffmeisteri and Corbicula fluminea were the most abundant species. Cluster-analysis and one-way analysis of similarity (ANOSIM) identified three significantly different macrobenthic communities among the sample sites. More specifically, sites in the eastern bays, where aquatic macrophytes were abundant, had the highest diversity of macrobenthic communities, which were dominated by Bellamya aeruginosa , Bellamya purificata , L. hoffmeisteri , and Alocinma longicornis . Sites in Zhushan Bay contained relatively diverse communities, mainly composed of L. hoffmeisteri , C. fluminea , L. claparederanus , R. sinicus , and Cythura sp. Sites in the western region, Meiliang Bay and Wuli Bay had the lowest diversity, mainly composed of L. hoffmeisteri , C. fluminea , Branchiura sowerbyi , and Rhyacodrilus sinicus . In addition, the relationships between macrobenthic metrics (Shannon&ndash;Wiener, Margalef, and Pielou) and environmental variables showed that community structure and spatial patterns of macrobenthos in Tai Lake were significantly influenced by chemical oxygen demand (COD Cr ), biochemical oxygen demand (BOD 5 ), lead (Pb), and microcystin-LR (L for leucine and R for arginine). Our findings provide critical information that could help managers and policymakers assess and modify ecological restoration practices.

Tai Lake

Flexible risk metrics for identifying and monitoring conservation-priority species

Region-specific conservation programs should have objective, reliable metrics for species prioritization and progress evaluation that are customizable to the goals of a program, easy to comprehend and communicate, and standardized across time. Regional programs may have vastly different goals, spatial coverage, or management agendas, and one-size-fits-all schemes may not always be the best approach. We propose a quantitative and objective framework for generating metrics for prioritizing species that is straightforward to implement and update, customizable to different spatial resolutions, and based on readily available time-series data. This framework is also well-suited to handling missing-data and observer error. We demonstrate this approach using North American Breeding Bird Survey (NABBS) data to identify conservation priority species from a list of over 300 landbirds across 33 bird conservation regions (BCRs). To highlight the flexibility of the framework for different management goals and timeframes we calculate two different metrics. The first identifies species that may be inadequately monitored by NABBS protocols in the near future (TMT, time to monitoring threshold), and the other identifies species likely to decline significantly in the near future based on recent trends (TPD, time to percent decline). Within the individual BCRs we found up to 45% (mean 28%) of the species analyzed had overall declining population trajectories, which could result in up to 37 species declining below a minimum NABBS monitoring threshold in at least one currently occupied BCR within the next 50 years. Additionally, up to 26% (mean 8%) of the species analyzed within the individual BCRs may decline by 30% within the next decade. Conservation workers interested in conserving avian diversity and abundance within these BCRs can use these metrics to plan alternative monitoring schemes or highlight the urgency of those populations experiencing the fastest declines. However, this framework is adaptable to many taxa besides birds where abundance time-series data are available.

Ecological Indicators

Selecting focal species as surrogates for imperiled species using relative sensitivities derived from occupancy analysis

Most imperiled species are rare or elusive and difficult to detect, which makes gathering data to estimate their response to habitat restoration a challenge. We used a repeatable, systematic method for selecting focal species using relative sensitivities derived from occupancy analysis. Our objective was to select suites of focal species that would be useful as surrogates when predicting effects of restoration of habitat characteristics preferred by imperiled species. We developed 27 habitat profiles that represent general habitat relationships for 118 imperiled species. We identified 23 regularly encountered species that were sensitive to important aspects of those profiles. We validated our approach by examining the correlation between estimated probabilities of occupancy for species of concern and focal species selected using our method. Occupancy rates of focal species were more related to occupancy rates of imperiled species when they were sensitive to more of the parameters appearing in profiles of imperiled species. We suggest that this approach can be an effective means of predicting responses by imperiled species to proposed management actions. However, adequate monitoring will be required to determine the effectiveness of using focal species to guide management actions.

Alabama

Estimating linear temporal trends from aggregated environmental monitoring data

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Ecological Indicators

Mercury contamination and stable isotopes reveal variability in foraging ecology of generalist California gulls

Environmental contaminants are a concern for animal health, but contaminant exposure can also be used as a tracer of foraging ecology. In particular, mercury (Hg) concentrations are highly variable among aquatic and terrestrial food webs as a result of habitat- and site-specific biogeochemical processes that produce the bioaccumulative form, methylmercury (MeHg). We used stable isotopes and total Hg (THg) concentrations of a generalist consumer, the California gull ( Larus californicus ), to examine foraging ecology and illustrate the utility of using Hg contamination as an ecological tracer under certain conditions. We identified four main foraging clusters of gulls during pre-breeding and breeding, using a traditional approach based on light stable isotopes. The foraging cluster with the highest δ 15 N and δ 34 S values in gulls (cluster 4) had mean blood THg concentrations 614% (pre-breeding) and 250% (breeding) higher than gulls with the lowest isotope values (cluster 1). Using a traditional approach of stable-isotope mixing models, we showed that breeding birds with a higher proportion of garbage in their diet (cluster 2: 63–82% garbage) corresponded to lower THg concentrations and lower δ 15 N and δ 34 S values. In contrast, gull clusters with higher THg concentrations, which were more enriched in 15 N and 34 S isotopes, consumed a higher proportion of more natural, estuarine prey. δ 34 S values, which change markedly across the terrestrial to marine habitat gradient, were positively correlated with blood THg concentrations in gulls. The linkage we observed between stable isotopes and THg concentrations suggests that Hg contamination can be used as an additional tool for understanding animal foraging across coastal habitat gradients.

Ecological Indicators

Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R 2 = 0.77, RMSE = 7%), annual grasses (R 2 = 0.70, RMSE = 21%), perennial grasses (R 2 = 0.36, RMSE = 12%), forbs (R 2 = 0.52, RMSE = 6%), bare earth or litter (R 2 = 0.49, RMSE = 19%), and the biomass of shrubs (R 2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R 2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

Idaho

The utility of point count surveys to predict wildlife interactions with wind energy facilities: An example focused on golden eagles

Wind energy development is rapidly expanding in North America, often accompanied by requirements to survey potential facility locations for existing wildlife. Within the USA, golden eagles ( Aquila chrysaetos ) are among the most high-profile species of birds that are at risk from wind turbines. To m inimize golden eagle fatalities in areas proposed for wind development, modified point count surveys are usually conducted to estimate use by these birds. However, it is not always clear what drives variation in the relationship between on-site point count data and actual use by eagles of a wind energy project footprint. We used existing GPS-GSM telemetry data, collected at 15 min intervals from 13 golden eagles in 2012 and 2013, to explore the relationship between point count data and eagle use of an entire project footprint. To do this, we overlaid the telemetry data on hypothetical project footprints and simulated a variety of point count sampling strategies for those footprints. We compared the time an eagle was found in the sample plots with the time it was found in the project footprint using a metric we called “error due to sampling”. Error due to sampling for individual eagles appeared to be influenced by interactions between the size of the project footprint (20, 40, 90 or 180 km 2 ) and the sampling type (random, systematic or stratified) and was greatest on 90 km 2 plots. However, use of random sampling resulted in lowest error due to sampling within intermediate sized plots. In addition sampling intensity and sampling frequency both influenced the effectiveness of point count sampling. Although our work focuses on individual eagles (not the eagle populations typically surveyed in the field), our analysis shows both the utility of simulations to identify specific influences on error and also potential improvements to sampling that consider the context-specific manner that point counts are laid out on the landscape.

California