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At least 91 records · Page 5Linked to original sources
USGS2003

Foraminifera as bioindicators in coral reef assessment and monitoring: The foram index

Coral reef communities are threatened worldwide. Resource managers urgently need indicators of the biological condition of reef environments that can relate data acquired through remote-sensing, water-quality and benthic-community monitoring to stress responses in reef organisms. The "FORAM" (Foraminifera in Reef Assessment and Monitoring) Index (FI) is based on 30 years of research on reef sediments and reef-dwelling larger foraminifers. These shelled protists are ideal indicator organisms because: ??? Foraminifers are widely used as environmental and paleoenvironmental indicators in many contexts; ??? Reef-building, zooxanthellate corals and foraminifers with algal symbionts have similar water-quality requirements; ??? The relatively short life spans of foraminifers as compared with long-lived colonial corals facilitate differentiation between long-term water-quality decline and episodic stress events; ??? Foraminifers are relatively small and abundant, permitting statistically significant sample sizes to be collected quickly and relatively inexpensively, ideally as a component of comprehensive monitoring programs; and ??? Collection of foraminifers has minimal impact on reef resources. USEPA guidelines for ecological indicators are used to evaluate the FI. Data required are foraminiferal assemblages from surface sediments of reef-associated environments. The FI provides resource managers with a simple procedure for determining the suitability of benthic environments for communities dominated by algal symbiotic organisms. The FI can be applied independently, or incorporated into existing or planned monitoring efforts. The simple calculations require limited computer capabilities and therefore can be applied readily to reef-associated environments worldwide. In addition, the foraminiferal shells collected can be subjected to morphometric and geochemical analyses in areas of suspected heavy-metal pollution, and the data sets for the index can be used with other monitoring data in detailed multidimensional assessments.

Environmental Monitoring and Assessment↗
USGS2013

The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

The movement of animals is strongly influenced by external factors in their surrounding environment such as weather, habitat types, and human land use. With advances in positioning and sensor technologies, it is now possible to capture animal locations at high spatial and temporal granularities. Likewise, scientists have an increasing access to large volumes of environmental data. Environmental data are heterogeneous in source and format, and are usually obtained at different spatiotemporal scales than movement data. Indeed, there remain scientific and technical challenges in developing linkages between the growing collections of animal movement data and the large repositories of heterogeneous remote sensing observations, as well as in the developments of new statistical and computational methods for the analysis of movement in its environmental context. These challenges include retrieval, indexing, efficient storage, data integration, and analytical techniques.

Movement Ecology↗
USGS2025

Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change

1. Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce. 2. Here, we present a successful case study demonstrating both space-for-time and time-for-space substitutions using emperor penguins (Aptenodytes forsteri) as the focal species. Using abundance-based species distribution models (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average fledgling abundance across 50 colonies. 3. Our analysis reveals that distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. aSDM’s space-for-time substitution performance, as measured by Pearson correlation coefficient was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar time-for-space substitution performance when predicting abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58. 4. We posit that such space-time equivalence arises because: 1) emperor penguins colonies conform to their existing fundamental niche; 2) there is not yet any environmental novelty when comparing the spatial vs temporal variation of distance to nearest open water; and 3) models of more specific components of life histories, such as fledgling abundance, rather than occurrence or total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.

Journal of Animal Ecology↗
USGS2023

Ecological effects of pinyon-juniper removal in the Western United States—A synthesis of scientific research, January 2014–March 2021

Executive Summary Increasing density of pinyon ( Pinus spp. ) and juniper ( Juniperus spp. ) woodlands (hereinafter “pinyon-juniper”), as well as expansion of these woodlands into adjacent shrublands and grasslands, has altered ecosystem function and wildlife habitat across large areas of the interior western United States. Although there are many natural and human-caused drivers of woodland infilling and expansion, restoration of sagebrush ( Artemisia spp. ) habitat through removal of pinyon-juniper is considered an urgent management objective in many locations, particularly in support of sagebrush-dependent wildlife species of conservation concern. In December 2020, the Bureau of Land Management (BLM) established the Pinyon-Juniper Management Categorical Exclusion (PJCX) to expedite the regulatory process for pinyon-juniper removal projects on public lands, largely intended to benefit mule deer ( Odocoileus hemionus ) and greater sage-grouse ( Centrocercus urophasianus ) habitats. During final preparation of this report, the BLM discontinued use of the PJCX (as of November, 2022), but the pinyon-juniper tree removal techniques assessed in this report are commonly used and understanding their effects remains relevant to land use planning. To address areas of uncertainty relative to potential ecological effects of the PJCX, we conducted a review of the peer-reviewed science literature to better understand the likely responses of vegetation, environmental (for example, soils), and wildlife variables to specific tree removal techniques permitted by the PJCX. In brief, the PJCX permitted removal of trees by either manual cutting, mechanical cutting, or mastication; allowed certain methods to redistribute or remove resulting tree biomass after treatment; and prohibited broadcast burning, roadbuilding, removal of old-growth, and seeding of non-native species. Specifically, we conducted our review to address the following questions: How will PJCX removal techniques affect plant communities, soils, and abiotic resources? How do these pinyon-juniper removal techniques affect wildlife communities, including both woodland- and sagebrush-dependent species? What are the potential ecological implications of different pinyon-juniper removal treatment types and implementation strategies (for example, treatment sizes) over time? What are the most important gaps in our scientific understanding of how treatments might affect targeted ecosystems over space and time (for example, potential effects of climate change)? To answer these questions, we considered studies related to pinyon-juniper ecosystems, focusing on research that occurred over a large portion of the interior western United States that is the primary focus of the PJCX. We also focused on papers published from 2014 onward, to avoid excessive overlap with other recent reviews on pinyon-juniper management effects. Using strict criteria, including only considering research that tested responses for statistical significance, we identified 48 papers that primarily examined treatment effects on vegetation and other environmental variables (1,709 responses), and 11 papers that addressed effects on wildlife (132 responses). Responses to the PJCX-permitted treatments were summarized as either positive (that is, a significant increase), negative (that is, a significant decrease), or non-significant (that is, no significant difference). Responses were assigned to categories (for example, Native Annual Grass/Forb Abundance) and hierarchical treatment levels. We found that there were large proportions of non-significant responses among all categories combined, with roughly half or more of all responses non-significant (48 percent for wildlife, 60 percent for vegetation-environmental), comparable to other recent systematic reviews of pinyon-juniper treatment effects. However, we also found that when there were significant responses, some important trends potentially emerged. Important undesirable outcomes included far more positive than negative responses of exotic grass and forb abundance among nearly all treatment types. Cutting treatments were also more likely to decrease biocrust cover and microbial activity. Potentially beneficial outcomes included mostly positive responses among sagebrush obligate species, including more positive than negative responses for mule deer and sage-grouse. Some treatment types (for example, mastication) also resulted in more positive than negative responses for native grasses and forbs (although, non-significant responses were the majority). We also highlighted many limitations of this review, including how responses often come from few studies, and how some response-treatment category combinations lack adequate response data. Moreover, the existing research is often insufficient to address many key questions about treatment effects, largely owing to short time-scales and limited spatial extents of observations, which do not match the size of treatments being implemented by land managers, nor capture long-term, post-treatment ecological dynamics. We also identify a lack of research that addresses key interactions that could undermine restoration objectives, including potential effects of climate change and grazing on post-treatment environments. Thus, we emphasize the importance of integrating these factors into future pinyon-juniper treatment research, and we stress the need for use of monitoring programs and research studies that partake in data collection and analysis over long durations and broad spatial scales.

Open-File Report↗
USGS2024

Genomic data characterize reproductive ecology patterns in Michigan invasive Red Swamp Crayfish (Procambarus clarkii)

The establishment and spread of invasive species are directly related to intersexual interactions as dispersal and reproductive success are related to distribution, effective population size, and population growth. Accordingly, populations established by r-selected species are particularly difficult to suppress or eradicate. One such species, the red swamp crayfish ( Procambarus clarkii ) is established globally at considerable ecological and financial costs to natural and human communities. Here, we develop a single nucleotide polymorphism (SNP) loci panel for P. clarkii using restriction-associated DNA-sequencing data. We use the SNP panel to successfully genotype 1800 individuals at 930 SNPs in southeastern Michigan, USA. Genotypic data were used to reconstruct pedigrees, which enabled the characterization of P. clarkii's mating system and statistical tests for associations among environmental, demographic, and phenotypic predictors and adult reproductive success estimates. We identified juvenile cohorts using genotype-based pedigrees, body size, and sampling timing, which elucidated the breeding phenology of multiple introduced populations. We report a high prevalence of multiple paternity in each surveyed waterbody, indicating polyandry in this species. We highlight the use of newly developed rapid genomic assessment tools for monitoring population reproductive responses, effective population sizes, and dispersal during ongoing control efforts.

Evolutionary Applications↗
USGS2022

Phenological variation in spring migration timing of adult alewife (Alosa pseudoharengus) in coastal Massachusetts

The timing of biological events in plants and animals, such as migration and reproduction, is shifting due to climate change. Anadromous fishes are particularly susceptible to these shifts as they are subject to strong seasonal cycles when transitioning between marine and freshwater habitats to spawn. We used linear models to determine the extent of phenological shifts in adult Alewife Alosa pseudoharengus as they migrated from ocean to freshwater environments during spring to spawn at 12 sites along the northeastern USA. We also evaluated broadscale oceanic and atmospheric drivers that trigger their movements from offshore to inland habitats, including sea surface temperature, North Atlantic Oscillation index, and Gulf Stream index. Run timing metrics of initiation, median (an indicator of peak run timing), end, and duration were found to vary among sites. Although most sites showed negligible shifts towards earlier timing, statistically significant changes were detected in three systems. Overall, winter sea surface temperature, spring and fall transition dates, and annual run size were the strongest predictors of run initiation and median dates, while a combination of within-season and seasonal-lag effects influenced run end and duration timing. Disparate results observed across the 12 spawning runs suggest that regional environmental processes were not consistent drivers of phenology and local environmental and ecological conditions may be more important. Additional years of data to extend time series and monitoring of Alewife timing and movements in nearshore habitats may provide important information about staging behaviors just before adults transition between ocean and freshwater habitats.

Massachusetts↗
USGS2020

Zero or not? Causes and consequences of zero-flow stream gage readings

Streamflow observations can be used to understand, predict, and contextualize hydrologic, ecological, and biogeochemical processes and conditions in streams. Stream gages are point measurements along rivers where streamflow is measured, and are often used to infer upstream watershed‐scale processes. When stream gages read zero, this may indicate that the stream has dried at this location; however, zero‐flow readings can also be caused by a wide range of other factors. Our ability to identify whether or not a zero‐flow gage reading indicates a dry fluvial system has far reaching environmental implications. Incorrect identification and interpretation by the data user can lead to inaccurate hydrologic, ecological, and/or biogeochemical predictions from models and analyses. Here, we describe several causes of zero‐flow gage readings: frozen surface water, flow reversals, instrument error, and natural or human‐driven upstream source losses or bypass flow. For these examples, we discuss the implications of zero‐flow interpretations. We also highlight additional methods for determining flow presence, including direct observations, statistical methods, and hydrologic models, which can be applied to interpret causes of zero‐flow gage readings and implications for reach‐ and watershed‐scale dynamics. Such efforts are necessary to improve our ability to understand and predict surface flow activation, cessation, and connectivity across river networks. Developing this integrated understanding of the wide range of possible meanings of zero‐flows will only attain greater importance in a more variable and changing hydrologic climate.

WIREs Water↗
USGS2025

Meta-analysis of a megafish: Assessing patterns and predictors of Alligator Gar movement across multiple populations

Background Freshwater megafishes are among some of the most commercially and ecologically important aquatic organisms yet are disproportionately threatened with range and population reduction. Anthropogenic alterations of rivers influencing migrations are among the most significant causes for these declines. However, migratory fishes do not always respond similarly to movement barriers and thus it is necessary to develop models to predict movements of freshwater migratory fishes in the face of anthropogenic alteration. Predicting movement of freshwater fishes is often investigated using statistical packages. However, empirical studies assessing these packages have led to mixed results, questioning its applicability to all taxa. We argue that spatial, temporal, and environmental attributes are more influential for movement of a migratory megafish, the Alligator Gar ( Atractosteus spatula ), than the current parameters explored in a globally relevant fish movement model. Methods This study explored two independent mobile telemetry datasets investigating Alligator Gar movement on the Brazos and Trinity rivers. Environmental associations were investigated to predict Alligator Gar displacement and dispersal using generalized additive models, generalized linear models, and model selection. Leptokurtosis of Alligator Gar populations was also assessed. Predictability of the movement model was tested by comparing observed to model derived stationary and mobile components making up a leptokurtic movement distribution. Results Our study suggests that current and antecedent measures of discharge and water temperature are positively correlated with Alligator Gar displacement and dispersal. However, these patterns are only detectable when monthly relocation intervals are explored rather than seasonal scales. Leptokurtosis was observed in both Alligator Gar populations. However, movement was normally distributed (i.e., mesokurtic) under tracking events following high flood pulses. Additionally, predicted Alligator Gar movement was significantly farther under modeled values compared to observed values, in part because the species undergoes cyclical migrations for reproduction that are sensitive to water temperature and discharge. Discussion In conclusion, this study provides an alternative framework to assess the movement patterns of migratory fishes, which could be tested on additional freshwater fishes, and suggests that assessing spatial, environmental, and temporal processes simultaneously are necessary to capture the complexities of fish movement which currently are unavailable for the movement model we investigated.

Texas↗
USGS2019

A statistical forecasting approach to metapopulation viability analysis

Conservation of at‐risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two‐step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently developed statistical PVAs differ because forecasts are made conditional on models fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 yr of data on the federally threatened Chiricahua leopard frog ( Lithobates chiricahuensis ) to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50‐yr time horizon indicated that metapopulation extinction risk was <4% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 3.9% (95% CI 0–37%) by year 2066. Restoring six ponds by increasing their hydroperiod reduced extinction risk to <1% and greatly reduced uncertainty (95% CI 0–2%). Our results suggest that managers can mitigate the impacts of drought and environmental stochasticity on metapopulation viability by maintaining ponds that hold water throughout the year and keeping them free of invasive predators. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.

Ecological Applications↗
USGS2022

Seasonal and long-term clarity trend assessment of Lake Tahoe, California–Nevada

The clarity of Lake Tahoe, observed using a Secchi disk on a regular basis since the late 1960s, continues to be a sentinel metric of lake health. Water clarity is influenced by physical and biological processes and has declined in the five decades of monitoring, revealing differences between summer (June–September) and winter (December–March). This document summarizes key findings of a study of Lake Tahoe water clarity, including long-term variability and the relative importance of several influencing variables and processes. This study, prepared in cooperation with the Nevada Division of Environmental Protection, focused on (1) an apparent divergence in clarity trends between summer and winter periods, (2) observed changes in in-lake physical and ecological variables that may influence or control seasonal and annual clarity trends, and (3) five research hypotheses regarding lake clarity that were developed by Lake Tahoe management agencies. Previously collected data were used to complete this study. Trend analysis confirmed that winter clarity stabilized (that is, there is no longer a statistically significant trend up or down) during the last 20 years. Evaluation of clarity for selected months in the 50-year Secchi disk clarity dataset showed that only two summer months, July and August, had statistically significant decreases in clarity from 2000–19. Different subsets of available data were analyzed to reveal the presence or absences of trends for each season, decade, and month. Five hypotheses related to lake clarity were part of the study described by this report. Hypothesis 1 stated that clarity is controlled predominantly by the distribution and volumetric density of fine particles in suspension. This hypothesis was studied using available data describing in-lake fine (0–20 micrometers) particles from 2008–19. Water clarity was negatively correlated with in-lake particle abundance, with particles in the 1.0-4.6 μm range having the greatest effect, consistent with light-scattering theory. Estimated abundances of diatoms of the genus Cyclotella also were found to be negatively correlated with clarity. Data limitations precluded a complete investigation of hypothesis 2, which stated that the observed improvements in winter water clarity are a response to decreasing fine suspended-sediment concentrations in the lake resulting from load reductions from upland sources in and near urbanized areas. Data describing fine-sediment loading from urban areas to the lake were only available since 2014, and only once or twice per month. A slight, statistically significant, negative correlation was identified between urban fine-particle loading and monthly lake clarity with a 4-month lag. Particle abundance in monitored streams is highly correlated with simultaneous particle abundance in the lake. Hypothesis 3 stated that changing hydrodynamic conditions in the lake are increasing thermal stability and resistance to mixing. Trend analyses performed on stability index and buoyancy frequency time series computed from long-term observations of lake temperatures support the hypothesis that hydrodynamic conditions have evolved since 1969 to increase the lake’s resistance to mixing. The date of maximum mixing in winter has become progressively earlier in the year. Lake density stratification, defined using the stability index, is commencing earlier in the year and extending a month longer than in the early years of the monitoring program. Hypothesis 4 stated that the trend of decreasing summer clarity is a result of earlier, prolonged, and more intense stratification. Statistically significant correlations were found between summer clarity and (1) date of onset of stratification, (2) duration of stratification, and (3) buoyancy frequency. Hypothesis 5 stated that ecological (food web) interactions are causing changes in the trends of seasonal or annual clarity; data supporting hypothesis 5 were limited to examples from other systems and to intermittent monitoring of Lake Tahoe and Emerald Bay. The resulting narrative assessment was motivated by a 6-year study of Mysis shrimp disappearance and return in Emerald Bay. The available data and a large body of published literature are consistent with the inference that Mysis shrimp-induced food web changes are causing changes in the trends of seasonal or annual clarity. This food-web study focused on the relations between introduced Mysis shrimp, the native cladocerans ( Daphnia and Bosmina ) that were largely eliminated following Mysis introduction, and the effect on fine particles within the lake. The records of Mysis and other zooplankton data for Lake Tahoe are episodic and have large gaps. Consequently, statistical analyses could not be conducted to compare zooplankton data with other variables. The long-term record, however, indicates that the key effect was a change to the phytoplankton assemblage, where larger diatoms disappeared, likely due to Mysis grazing, only to be replaced by Cyclotella that are an order-of-magnitude smaller and have increased the abundance and volumetric density of total fine particles in suspension (biotic and abiotic).

California↗
USGS2013

A causal examination of the effects of confounding factors on multimetric indices

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.

Ecological Indicators↗
USGS2016

Extending ordinal regression with a latent zero-augmented beta distribution

Ecological abundance data are often recorded on an ordinal scale in which the lowest category represents species absence. One common example is when plant species cover is visually assessedwithin bounded quadrats and then assigned to pre-defined cover class categories.We present an ordinal beta hurdle model that directly models ordinal category probabilitieswith a biologically realistic beta-distributed latent variable.Ahurdle-at-zero model allows ecologists to explore distribution (absence) and abundance processes in an integrated framework. This provides an alternative to cumulative link models when data are inconsistent with the assumption that the odds ofmoving into a higher category are the same for all categories (proportional odds). Graphical tools and a deviance information criterion were developed to assess whether a hurdle-at-zero model should be used for inferences rather than standard ordinal methods. Hurdle-at-zero and non-hurdle ordinal models fit to vegetation cover class data produced substantially different conclusions.The ordinal beta hurdle model yielded more precise parameter estimates than cumulative logit models, although out-of-sample predictions were similar. The ordinal beta hurdle model provides inferences directly on the latent biological variable of interest, percent cover, and supports exploration of more realistic ecological patterns and processes through the hurdle-at-zero or two-part specification.We provide JAGS code as an on-line supplement. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗
USGS2006

A permutation test for quantile regression

A drop in dispersion, F -ratio like, permutation test ( D ) for linear quantile regression estimates (0≤τ≤1) had relative power ≥1 compared to quantile rank score tests ( T ) for hypotheses on parameters other than the intercept. Power was compared for combinations of sample sizes ( n =20−300) and quantiles (τ=0.50−0.99) where both tests maintained valid Type I error rates in simulations with p =2 and 6 parameters in homogeneous and heterogeneous error models. The D test required two modifications of permuting residuals from null, reduced parameter models to maintain correct Type I error rates when null models were constrained through the origin or included multiple parameters. A double permutation scheme was used when null models were constrained through the origin and all but 1 of the zero residuals were deleted for null models with multiple parameters. Although there was considerable overlap in sample size, quantiles, and hypotheses where both the D and rank score tests maintained correct Type I error rates, we identified regions at smaller n and more extreme quantiles where one or the other maintained better error rates. Confidence intervals on parameters for an ecological application relating Lahontan cutthroat trout densities to stream channel width:depth were estimated by test inversion, demonstrating a smoother pattern of slightly narrower intervals across quantiles than those provided by the rank score test.

Journal of Agricultural, Biological, and Environme↗
USGS2001

Assessing large-scale surveyor variability in the historic forest data of the original U.S. Public Land Survey

The U.S. General Land Office Public Land Survey (PLS) records are a valuable resource for studying pre-European settlement vegetation. However, these data were taken for legal, not ecological, purposes. In turn, the instructions the surveyors followed affected the data collected. For this reason, it has been suggested that the PLS data may not truly represent the surveyed landscapes. This study examined the PLS data of northern Wisconsin, U.S.A., to determine the extent of variability among surveyors. We statistically tested for differences among surveyors in recorded tree species, size, location, and distance from the survey point. While we cannot rule out effects from other influences (e.g., environmental factors), we found evidence suggesting some level of surveyor bias for four of five variables, including tree species and size. The PLS data remain one of the best records of pre-European settlement vegetation available. However, based on our findings, we recommend that projects using PLS records examine these data carefully. This assessment should include not only the choice of variables to be studied but also the spatial extent at which the data will be examined.

Canadian Journal of Forest Research↗
USGS1994

Horvitz-Thompson survey sample methods for estimating large-scale animal abundance

Large-scale surveys to estimate animal abundance can be useful for monitoring population status and trends, for measuring responses to management or environmental alterations, and for testing ecological hypotheses about abundance. However, large-scale surveys may be expensive and logistically complex. To ensure resources are not wasted on unattainable targets, the goals and uses of each survey should be specified carefully and alternative methods for addressing these objectives always should be considered. During survey design, the impoflance of each survey error component (spatial design, propofiion of detected animals, precision in detection) should be considered carefully to produce a complete statistically based survey. Failure to address these three survey components may produce population estimates that are inaccurate (biased low), have unrealistic precision (too precise) and do not satisfactorily meet the survey objectives. Optimum survey design requires trade-offs in these sources of error relative to the costs of sampling plots and detecting animals on plots, considerations that are specific to the spatial logistics and survey methods. The Horvitz-Thompson estimators provide a comprehensive framework for considering all three survey components during the design and analysis of large-scale wildlife surveys. Problems of spatial and temporal (especially survey to survey) heterogeneity in detection probabilities have received little consideration, but failure to account for heterogeneity produces biased population estimates. The goal of producing unbiased population estimates is in conflict with the increased variation from heterogeneous detection in the population estimate. One solution to this conflict is to use an MSE-based approach to achieve a balance between bias reduction and increased variation. Further research is needed to develop methods that address spatial heterogeneity in detection, evaluate the effects of temporal heterogeneity on survey objectives and optimize decisions related to survey bias and variance. Finally, managers and researchers involved in the survey design process must realize that obtaining the best survey results requires an interactive and recursive process of survey design, execution, analysis and redesign. Survey refinements will be possible as further knowledge is gained on the actual abundance and distribution of the population and on the most efficient techniques for detection animals.

Transactions of the North American Wildlife and Na↗
USGS2015

Developing a workflow to identify inconsistencies in volunteered geographic information: a phenological case study

Recent improvements in online information communication and mobile location-aware technologies have led to the production of large volumes of volunteered geographic information. Widespread, large-scale efforts by volunteers to collect data can inform and drive scientific advances in diverse fields, including ecology and climatology. Traditional workflows to check the quality of such volunteered information can be costly and time consuming as they heavily rely on human interventions. However, identifying factors that can influence data quality, such as inconsistency, is crucial when these data are used in modeling and decision-making frameworks. Recently developed workflows use simple statistical approaches that assume that the majority of the information is consistent. However, this assumption is not generalizable, and ignores underlying geographic and environmental contextual variability that may explain apparent inconsistencies. Here we describe an automated workflow to check inconsistency based on the availability of contextual environmental information for sampling locations. The workflow consists of three steps: (1) dimensionality reduction to facilitate further analysis and interpretation of results, (2) model-based clustering to group observations according to their contextual conditions, and (3) identification of inconsistent observations within each cluster. The workflow was applied to volunteered observations of flowering in common and cloned lilac plants ( Syringa vulgaris and Syringa x chinensis ) in the United States for the period 1980 to 2013. About 97% of the observations for both common and cloned lilacs were flagged as consistent, indicating that volunteers provided reliable information for this case study. Relative to the original dataset, the exclusion of inconsistent observations changed the apparent rate of change in lilac bloom dates by two days per decade, indicating the importance of inconsistency checking as a key step in data quality assessment for volunteered geographic information. Initiatives that leverage volunteered geographic information can adapt this workflow to improve the quality of their datasets and the robustness of their scientific analyses.

PLoS ONE↗
USGS2006

Relationships between reef fish communities and remotely sensed rugosity measurements in Biscayne National Park, Florida, USA

The realization that coral reef ecosystem management must occur across multiple spatial scales and habitat types has led scientists and resource managers to seek variables that are easily measured over large areas and correlate well with reef resources. Here we investigate the utility of new technology in airborne laser surveying (NASA Experimental Advanced Airborne Research Lidar (EAARL)) in assessing topographical complexity (rugosity) to predict reef fish community structure on shallow (<10 m deep) patch reefs. Marine portions of Biscayne National Park, Florida, USA, were surveyed remotely using the EAARL, and reef fish populations were visually surveyed on 10 patch reefs at independent, randomly selected stations ( n = 10–13 per reef). Rugosity at each station was assessed in situ by divers using the traditional chain-transect method (10-m scale), and remotely using the EAARL submarine topography data at multiple spatial scales (2, 5, and 10 m). The rugosity and biological datasets were analyzed together to elucidate the predictive power of EAARL rugosity in describing the variance in reef fish community variables and to assess the correlation between chain-transect and EAARL rugosity. EAARL rugosity was not well correlated with chain-transect rugosity, or with species richness of fishes (although statistically significant, the amount of variance explained by the model was very low). Variance in reef fish community attributes was better explained in reef-by-reef variability than by physical variables. However, once the reef-by-reef variability was taken into account in a two-way analysis of variance, the importance of rugosity could be seen on individual reefs. Fish species richness and abundance were statistically higher at high rugosity stations compared to medium and low rugosity stations, as predicted by prior ecological research. The EAARL shows promise as an important mapping tool for reef resource managers as they strive to inventory and protect coral reef resources.

Florida↗
USGS2020

Learning from real-world experience to understand renewable energy impacts to wildlife

The project team sought to use real-world data to understand adverse effects to wildlife of renewable energy production that is critical to meeting California’s climate and clean energy goals. The project had three main components. First, a systematic literature review studied 20 peer-reviewed publications and 612 reports from other nonreviewed sources from 231 wind and solar facilities in North America. Within California, 50 percent of facilities collected pre- and post-construction data, 30 percent had experimental study designs, and fewer than 7 percent estimated detection probability during habitat use surveys. Mitigation at wind power plants focused on repowering to reduce risk to soaring birds and at solar facilities emphasized wildlife deterrence and compensatory mitigation. Second, the authors developed a best-practices approach to employ environmental isotopes (for example, hydrogen obtained from animal tissue) and rescaling functions (a statistical approach to modeling the relationship between variables) to assign individual birds or bats to their place of origin. The team applied this approach to feathers from 411 individuals of 12 species killed at wind facilities and 515 individuals of 19 species killed at solar facilities. From 24 percent to 100 percent (mean +/- SD = 49 percent +/- 33 percent) and 25 percent to 100 percent (73 percent +/- 25 percent) of birds grew feathers at a location outside the collection site at wind and solar facilities, respectively. Third, the authors constructed Bayesian integrated population models (probability models) for 29 focal species affected by wind or solar energy generation in California. Species predominantly local in origin generally had lower population growth rates than did species that were predominantly nonlocal in origin. These patterns illustrate the complex linkages between behavioral ecology, vulnerability to mortality, and population-level impacts to wildlife from fatalities at renewable energy facilities. This project benefits the renewable energy sector by providing a framework and specific tools for understanding environmental impacts of renewable energy generation.

California↗