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USGS2024

The economics of decarbonizing Costa Rica's agriculture, forestry and other land uses sectors

In 2018, Costa Rica demonstrated its commitment to the Paris Agreement and published its Decarbonization Plan for achieving zero net emissions by the year 2050. We evaluate the impacts of the country's strategy for decarbonizing its Agriculture, Forestry and Other Land Uses (AFOLU) sectors by coupling the Integrated Economic-Environmental Modeling framework with high-resolution spatial land use-land cover change and ecosystem services modeling (IEEM+ESM). Our results show that decarbonization of AFOLU would simultaneously enhance carbon storage, water purification, water regulation and erosion mitigation ecosystem services. Moreover, the positive cumulative wealth impact of decarbonization would be approximately US$7.27 billion by 2050 while lifting an additional 3810 individuals out of poverty. From a public investment perspective, decarbonization would have a fiscally neutral impact with the economic benefits sufficient in magnitude to off-set policy implementation costs and generate economic returns of over US$852 million when changes in natural capital stocks and environmental quality are considered. This application to Costa Rica is the first integrated economy-wide analysis of a growing number of decarbonization plans globally. The IEEM+ESM approach provides an integrated framework for analyzing decarbonization plans and can be used to refine AFOLU mitigation strategies to capitalize on synergies and minimize negative trade-offs across the three dimensions of wealth and sustainable economic development, namely economy, society and the environment.

Ecological Economics↗

USGS2002

Comparisons of likelihood and machine learning methods of individual classification

Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k -nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high F ST ), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout ( Salvelinus namaycush ) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k -nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of individuals (e.g., Bowcock et al. 1994 ; Estoup and Angers 1998 ; Jarne and Lagoda 1996 ). Analyses of individual-based genotypic information could substantially improve our understanding of evolutionary phenomena and contribute to effective management of natural populations (review in Bernatchez and Duchesne 2000 ). The use of individual-based methods remained largely unexplored in animal populations until recently due to a lack of highly polymorphic markers ( Bernatchez and Duchesne 2000 ; Smouse and Chevillon 1998 ). Traditional analytical methods in population genetics rely almost exclusively on descriptors of genetic characterizations of populations ( Bernatchez and Duchesne 2000 ) and not on individual genotypes. “Assignment tests” are designed to determine population membership for individuals. One particular application based on a likelihood estimate (LE) was introduced by Paetkau et al. (1995 ; see also Vásquez-Domínguez et al. 2001) to assign an individual to the population of origin on the basis of multilocus genotype and expectations of observing this genotype in each potential source population. The LE approach can be implemented statistically in a Bayesian framework as a convenient way to evaluate hypotheses of plausible genealogical relationships (e.g., that an individual possesses an ancestor in another population) ( Dawson and Belkhir 2001 ; Pritchard et al. 2000 ; Rannala and Mountain 1997 ). Other studies have evaluated the confidence of the assignment ( Almudevar 2000 ) and characteristics of genotypic data (e.g., degree of population divergence, number of loci, number of individuals, number of alleles) that lead to greater population assignment ( Bernatchez and Duchesne 2000 ; Cornuet et al. 1999 ; Haig et al. 1997 ; Shriver et al. 1997; Smouse and Chevillon 1998 ). Main statistical and conceptual differences between methods leading to the use of an assignment test are given in, for example, Cornuet et al. (1999) and Rosenberg et al. (2001) . However, the relative power of those tests has certainly not been fully appreciated and empirical comparisons are scarce ( Eldridge et al. 2001 ). Assignment tests can also be considered as surrogates at the individual level (sensu Hansen et al. 2001a ) for other statistical tools developed earlier, such as mixed-stock analysis (e.g., Pella and Masuda 2001 ; Pella and Milner 1987 ). Detailed theoretical comparison of the interests and limitations of both methods are still lacking, but empirical studies have revealed correlations between outputs of methods ( Knutsen et al. 2001 ; Potvin and Bernatchez 2001 ). Assignment tests have been widely used in different applications, including determination of degree of population differentiation or to establish the relationship among individuals within and among various taxonomic groupings (e.g., Bogdanowicz et al. 1997 ; Koskinen et al. 2001 ; Marshall et al. 2000 ; Müller 2000 ; Neraas and Spruell 2001 ; Nielsen et al. 2001b ; Polzhien et al. 2000 ; Primmer et al. 1999 ; Roeder et al. 2001 ; Roques et al. 1999 ; Schulte-Hostedde et al. 2001 ; Sefc et al. 2000 ; Spidle et al. 2001 ; Vásquez-Domínguez et al. 2001 ), including hybrids (e.g., Beaumont et al. 2001 ; Congiu et al. 2001 ; Randi et al. 2001 ), introgressed individuals (e.g., Martinez et al. 2001 ; Randi and Lucchini 2002 ), and ecotypes (e.g., Taylor et al. 2000 ). Applications of assignment tests also include [human] forensics (e.g., Evett and Weir 1998 ; Primmer et al. 2000 ), identification and/or source of dispersers (e.g., Davies et al. 1999 ; Eldridge et al. 2001 ; Galbusera et al. 2000 ; Petersson et al. 2001 ; Tsutsui et al. 2001 ; Vasemägi et al. 2001), phylogeographical analyses (e.g., King et al. 2001 ; Zeisset and Beebee 2001 ), and the evaluation of the contribution of stocked individuals to natural populations (e.g., Fritzner et al. 2001 ; Hansen et al. 2000 , 2001b ) and of supportive breeding programs ( Nielsen et al. 2001a ; Olsen et al. 2000 ). Fish are among the organisms that have received considerable attention using such tools (see Hansen et al. [2001a] for a review). Moreover, these techniques are now used for profiles of traits outside the limited scope of population genetics ( Thorrold et al. 2001 ). Methods of classification vary widely based on several criteria (e.g., Jain et al. 2000 ) ( Figure 1 ). Two basic classification processes are traditionally recognized in machine learning: supervised classifiers and unsupervised classifiers ( Figure 1 ; e.g., Duda et al. 2000 ; Jain et al. 2000 ). Supervised classifiers represent a group of methods whereby individual assignment is made to predefined classes (i.e., populations of origin). Unsupervised classification classes are unknown and are defined a posteriori on the basis of the degree of difference or similarity in attributes characterized from sampled individuals. Clustering methods (e.g., multidimensional scaling, principal component analysis) are examples of unsupervised classification. Applications of assignment testing in population genetics first used supervised parametric likelihood-based approaches ( Figure 1 ). Other machine learning classification methods are widely used in the physical and social sciences and in other biological disciplines (e.g. Boddy et al. 2000 ; Leung and Tran 2000 ; Manel et al. 1999 ; Raymer et al. 1997 ). Artificial neural networks (ANNs) are a popular technique used in machine learning (e.g., Boddy and Morris 1999 ; Duda et al. 2000 ; Lek and Guégan 2000 ; Ripley 1996 ). However, while recognized ( Hansen et al. 2001a ), ANN methods rarely have been employed for population genetics applications ( Aurelle 1999 ; Aurelle et al. 1999 ; Cornuet et al. 1996 ; Curtis et al. 2001 ; Giraudel et al. 2000 ; Grigull et al. 2001 ; Taylor et al. 1994 ; Whitler et al. 1994 ). Other popular classification methods in machine learning, such as decision trees (e.g., Bell 1996 , 1999 ; Duda et al. 2000 ; Mitchell 1997 ) and k -nearest neighbor analysis ( k -NN; e.g., Dasarathy 1991 ; Duda et al. 2000 ) have yet to be applied in population genetics ( Figure 1 ). Moreover, there has not been a directed effort to compare machine learning methodologies with the likelihood-based procedures widely used in population genetics. Cornuet et al. (1996) compared the relative merits of ANNs to discriminant analysis in an empirical study involving different populations and subspecies of honeybee ( Apis mellifera ). However, they did not compare LE and ANN supervised classifiers. Aurelle (1999) used the approach of Rannala and Mountain (1997) ( Figure 1 ) and ANN analysis using brown trout ( Salmo trutta ) microsatellite data; however, he did not provide a direct comparison of classification results or accuracies. Hansen et al. (2001a) briefly presented ANNs, but rejected their use without really testing their ability to classify individuals. The objective of this article is to describe several of the more widely used machine learning classifiers that may have utility when used with empirical population genetics data. We compare likelihood-based “assignment tests” ( Paetkau et al. 1995 ) with supervised machine learning classifiers including ANN, decision tree, and a k -NN clustering. Simulations were conducted which estimated and compared the assignment accuracy associated with different classifiers using ranges of parameter values (number of loci, allelic diversity, and interpopulation variance in allele frequency) typically encountered in natural populations. Comparative analyses were extended to empirical examples using lake trout ( Salvelinus namaycush ; Salmonidae).

Journal of Heredity↗

USGS2017

Defining and classifying migratory habitats as sources and sinks: The migratory pathway approach

Understanding and conserving migratory species requires a method for characterizing the seasonal flow of animals among habitats. Source-sink theory describes the metapopulation dynamics of species by classifying habitats as population sources (i.e. net contributors) or sinks (i.e. net substractors). Migratory species may have non-breeding habitats important to the species (e.g. overwintering or stopover habitats) that traditional source-sink theory would classify as sinks because these habitats produce no individuals. Conversely, existing migratory network models can evaluate the relative contribution of non-breeding nodes, but these models make an equilibrium assumption that is difficult to meet when examining real migratory populations. We extend a pathway-based metric allowing breeding habitats, non-breeding habitats and migratory pathways connecting these habitats to be classified as sources or sinks. Rather than being based on whether place- or season-specific births exceed deaths, our approach quantifies the total demographic contribution from a node or migratory pathway over a flexibly defined yet limited time period across an organism's life cycle. As such, it provides a snapshot of a migratory system and therefore does not require assumptions associated with equilibrium dynamics. We first develop a generalizable mathematical notation and then demonstrate how the metric may be used with two case studies: the common loon ( Gavia immer ) and Yellowstone cutthroat trout ( Oncorhynchus clarkii bouvieri ). These examples highlight how stressors can impact stopover and wintering habitats (loons) and habitat management targeting migratory pathways can improve population status (trout). Synthesis and applications . Each of the two case studies presented describes how effects at one location are felt by populations in another through the seasonal flow of individuals. The contribution metric we present should be helpful in allocating regulatory and management attention to times and locations most critical to migratory species persistence.

Journal of Applied Ecology↗

USGS2023

Plant water-use strategies predict restoration success across degraded drylands

Plant strategies for coping with water limitation are likely to mediate restoration outcomes in degraded dryland ecosystems. Trade-offs in traits related to water acquisition and use can intensify in more arid environments, making their effects on dryland restoration success even more salient. However, isolating the effects of drought responses from those of other environmental factors, as well as identifying the specific drought resistance traits that influence restoration success, can be difficult. In the present study, we couple a controlled dry-down experiment with a cross-site restoration field trial of out-planted seedlings (RestoreNet) using a suite of dryland herbaceous plant species from the same seed sources. We quantified interspecific variation in physiological responses to drought, specifically reductions in stomatal conductance ( g s ) and stem water potential (SWP), by comparing well-watered control plants to those experiencing decreasing soil moisture. Drought responses of SWP and g s varied independently among species, but both were related to survival in the cross-site restoration field trial when effect sizes were aggregated across all sites. Responses were consistent with acquisitive water-use strategies resulting in greater success, where species with greater declines in SWP or weaker declines in g s under drought had greater survival. The correlation between SWP drought response and survival also intensified in sites with lower accumulated precipitation following restoration. Differences among functional groups revealed two different paths to restoration success: forbs that maintain high g s and narrow safety margins to maximize exploitation of moisture pulses before going into drought dormancy, or C 4 grasses that maintain efficient water uptake in drying soils while risking cavitation. C 3 grass species varied between these two strategies. Synthesis and applications . Taken together, the results of this study and others conducted at RestoreNet sites indicate that while a diversity of physiological responses to drought may exist in dryland plant communities, successfully restoring herbaceous species through out-planting in degraded conditions is likely to be achieved with species that maximize water uptake via one of two strategies, with tolerance of low SWP being particularly important in the most arid settings.

Journal of Applied Ecology↗

USGS2009

Population density estimated from locations of individuals on a passive detector array

The density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture–recapture. Likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement ("proximity" detectors such as camera traps and hair snags). As originally proposed, these methods require multiple sampling intervals. We show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. This considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. Acoustic detection can be defined as occurring when received signal strength exceeds a threshold. We suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. While binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small.

Ecology↗

USGS2010

Visual implant elastomer mark retention through metamorphosis in amphibian larvae

Questions in population ecology require the study of marked animals, and marks are assumed to be permanent and not overlooked by observers. I evaluated retention through metamorphosis of visual implant elastomer marks in larval salamanders and frogs and assessed error in observer identification of these marks. I found 1) individual marks were not retained in larval wood frogs ( Rana sylvatica ), whereas only small marks were likely to be retained in larval salamanders ( Eurycea bislineata ), and 2) observers did not always correctly identify marked animals. Evaluating the assumptions of marking protocols is important in the design phase of a study so that correct inference can be made about the population processes of interest. This guidance should be generally useful to the design of mark–recapture studies, with particular application to studies of larval amphibians.

Journal of Wildlife Management↗

USGS2013

Method to support Total Maximum Daily Load development using hydrologic alteration as a surrogate to address aquatic life impairment in New Jersey streams

More than 300 ambient monitoring sites in New Jersey have been identified by the New Jersey Department of Environmental Protection (NJDEP) in its integrated water-quality monitoring and assessment report (that is, the 305(b) Report on general water quality and 303(d) List of waters that do not support their designated uses) as being impaired with respect to aquatic life; however, no unambiguous stressors (for example, nutrients or bacteria) have been identified. Because of the indeterminate nature of the broad range of possible impairments, surrogate measures that more holistically encapsulate the full suite of potential environmental stressors need to be developed. Streamflow alteration resulting from anthropogenic changes in the landscape is one such surrogate. For example, increases in impervious surface cover (ISC) commonly cause increases in surface runoff, which can result in “flashy” hydrology and other changes in the stream corridor that are associated with streamflow alteration. The NJDEP has indicated that methodologies to support a hydrologically based Total Maximum Daily Load (hydro-TMDL) need to be developed in order to identify hydrologic targets that represent a minimal percent deviation from a baseline condition (“minimally altered”) as a surrogate measure to meet criteria in support of designated uses. The primary objective of this study was to develop an applicable hydro-TMDL approach to address aquatic-life impairments associated with hydrologic alteration for New Jersey streams. The U.S. Geological Survey, in cooperation with the NJDEP, identified 51 non- to moderately impaired gaged streamflow sites in the Raritan River Basin for evaluation. Quantile regression (QR) analysis was used to compare flow and precipitation records and identify baseline hydrographs at 37 of these sites. At sites without an appropriately long period of record (POR) or where a baseline hydrograph could not be identified with QR, a rainfall-runoff model was used to develop simulated baseline hydrographs. The hydro-TMDL approach provided an opportunity to evaluate proportional differences in flow attributes between observed and baseline hydrographs and to develop complementary flow-ecology response relations at a subset of Raritan River Basin sites where available flow and ecological information overlapped. The New Jersey Stream Classification Tool (NJSCT) was used to determine the stream class of all 51 study sites by using either an observed or a simulated baseline hydrograph. Two New Jersey stream classes (A and C) were evaluated to help characterize the unique hydrology of the Raritan River Basin. In general, class C streams (1.99–40.7 square miles) had smaller drainage areas than class A streams (0.7–785 square miles). Many of the non-impaired and moderately impaired class A and C streams in the Raritan River Basin were found to have significant hydrologic alteration as indicated by numerous flow values that fell outside the established 25th-to-75th- and the more conservative 40th-to-60th-percentile boundaries. However, percent deviations for the class C streams (defined as moderately stable streams with moderately high base-flow contributions) were, in general, much larger than those for the class A streams (defined as semiflashy streams characterized by moderately low base flow). The greater deviations for class C streams in the hydro-TMDL assessments likely resulted from comparisons that were based solely on simulated baseline hydrographs, which were developed without considering any anthropogenic influences in the basin. In contrast, comparisons for many of the class A streams were made by using an observed baseline, which already includes an implicit level of ISC and other human influences on the landscape. By using the hydro-TMDL approach, numerous flow deviations were identified that were indicative of streams that are highly regulated by reservoirs or dams, streams that are affected by increasing amounts of surface runoff resulting from ISC, and streams that are affected by water abstraction (that is, groundwater or surface-water withdrawals used for agricultural and human supply). Eight of the reservoir- and (or) dam-affected sites showed flow deviations that are indicative of flow-managed systems. For example, indices that account for the timing and magnitude of high and low flows were often found to fall outside the 25th-to-75th-percentile range. In general, at regulated class C streams, annual summer low flows are arriving later and tend to be lower, and high flows are arriving earlier with higher magnitudes of longer duration. At class A streams, high and low flows are arriving later with an overall increase in discharge with respect to the prereservoir baseline conditions. The drainage basins of eight of the study sites had large values of ISC (>10 percent), most likely as a result of expanding urban development. In general, the magnitude and frequency of high flows at class A and C sites with high ISC are increasing and were commonly found to fall outside the 25th-to-75th-percentile range. Additionally, magnitudes of low flows are becoming lower and, although the timing of high flows was highly variable, low-flow events appeared to be arriving earlier than would be expected under normal low-flow conditions. Three of the study sites appeared to be affected by hydrologic changes associated with water abstraction. At these sites, the timing of flows appeared to be altered. For example, low flows tended to arrive earlier and high flows arrived later at two of the three sites. Additionally, the magnitude and duration of low flows were commonly less than the 25th-percentile value and the duration of high flows appeared to increase. A reduced set of hydrologic and ecological variables was used to develop univariate and multivariate flow-ecology response models for the aquatic-invertebrate assemblage. Many hydrologic variables accounting for the duration, magnitude, frequency, and timing of flows were significantly correlated with ecological response. Multiple linear regression (MLR) models were developed to provide a more holistic evaluation of the combined effects of hydrologic alteration and to identify models with two or three hydrologic variables that account for a significant proportion of the variability in invertebrate-assemblage condition as represented by assemblage metric scores. MLR models, derived on the basis of hydrologic attributes, accounted for 35 to 75 percent of the variability in assemblage condition. The hydro-TMDL method developed herein for non- to moderately impaired Raritan River Basin streams utilizes a “surrogate” approach in place of the traditional “pollutant of concern” approach commonly used for TMDL development. Managers can use the results obtained by using the hydro-TMDL method to offset the effects of impervious-surface runoff and altered streamflow and to implement measures designed to achieve the necessary load reductions for the “pollutant of concern” (that is, percentage deviations of stream-class-specific flow-index values outside the established 25th-to-75th-percentile range). In this case, such deviations could represent all or a subset of the altered flow indices that prevent the stream from meeting designated aquatic-life criteria. This hydro-TMDL uses a reference, or attainment stream approach for developing the TMDL endpoint. That is, either observed or simulated baseline hydrographs were selected as appropriate reference conditions on the basis of results of QR analysis and watershed modeling procedures, respectively. For any stream in the Raritan River Basin evaluated as part of this study, the hydro-TMDL can be expressed as the greatest amount of deviation in flow a stream can exhibit without violating the stream’s designated aquatic-life criteria. Use of this surrogate approach is appropriate because flows that fall outside the established percentile ranges are ultimately a function of many anthropogenic modifications of the landscape, including the amount of stormwater runoff generated from impervious surfaces within a given basin, the presence of manmade structures designed to retain or divert water, the magnitude of ground- and surface-water abstraction, and the presence of water-supply processes implemented to support human needs. In addition, the stream-type-specific flow indices used as the basis for the hydro-TMDL approach are useful for representing the hydrologic conditions of class A and C streams/basins because they incorporate the full spectrum of flow conditions (very low to very high) that occur in the stream system over a long period of time, as well as those flow properties that change as a result of seasonal variation. Ultimately, an estimate of the maximum percentage flow reduction that could be allowed will be needed to address the aquatic-life impairments in many of the study streams in the Raritan River Basin and will be necessary for identifying appropriate target flow conditions for hydro-TMDL implementation. As described in this report, a target flow value equal to the 25th- or 75th-percentile flow rate could be selected as the point useful for setting specific hydrologic targets. This selection, however, is a management decision that could vary depending on the designated use of the stream or other regulatory factors (for example, water-supply protection, trout production, antidegradation policies, or special protection designations). In New Jersey streams where no unambiguous stressors can be identified, State monitoring agencies, such as the NJDEP, could choose to require the implementation of a flow-based TMDL that not only supports designated uses, but meets the regulatory requirements under the Clean Water Act, and represents a balance between water supply intended to meet human needs and the conservation of ecosystem integrity.

New Jersey↗

USGS2015

On formally integrating science and policy: walking the walk

The contribution of science to the development and implementation of policy is typically neither direct nor transparent. In 1995, the U.S. Fish and Wildlife Service (FWS) made a decision that was unprecedented in natural resource management, turning to an unused and unproven decision process to carry out trust responsibilities mandated by an international treaty. The decision process was adopted for the establishment of annual sport hunting regulations for the most economically important duck population in North America, the 6 to 11 million mallards Anas platyrhynchos breeding in the mid-continent region of north-central United States and central Canada. The key idea underlying the adopted decision process was to formally embed within it a scientific process designed to reduce uncertainty (learn) and thus make better decisions in the future. The scientific process entails use of models to develop predictions of competing hypotheses about system response to the selected action at each decision point. These prediction not only are used to select the optimal management action, but also are compared with the subsequent estimates of system state variables, providing evidence for modifying degrees of confidence in, and hence relative influence of, these models at the next decision point. Science and learning in one step are formally and directly incorporated into the next decision, contrasting with the usual ad hoc and indirect use of scientific results in policy development and decision-making. Application of this approach over the last 20 years has led to a substantial reduction in uncertainty, as well as to an increase in transparency and defensibility of annual decisions and a decrease in the contentiousness of the decision process. As resource managers are faced with increased uncertainty associated with various components of global change, this approach provides a roadmap for the future scientific management of natural resources.

Journal of Applied Ecology↗

USGS2009

Impacts of forest fragmentation on species richness: A hierarchical approach to community modelling

1. Species richness is often used as a tool for prioritizing conservation action. One method for predicting richness and other summaries of community structure is to develop species-specific models of occurrence probability based on habitat or landscape characteristics. However, this approach can be challenging for rare or elusive species for which survey data are often sparse. 2. Recent developments have allowed for improved inference about community structure based on species-specific models of occurrence probability, integrated within a hierarchical modelling framework. This framework offers advantages to inference about species richness over typical approaches by accounting for both species-level effects and the aggregated effects of landscape composition on a community as a whole, thus leading to increased precision in estimates of species richness by improving occupancy estimates for all species, including those that were observed infrequently. 3. We developed a hierarchical model to assess the community response of breeding birds in the Hudson River Valley, New York, to habitat fragmentation and analysed the model using a Bayesian approach. 4. The model was designed to estimate species-specific occurrence and the effects of fragment area and edge (as measured through the perimeter and the perimeter/area ratio, P/A), while accounting for imperfect detection of species. 5. We used the fitted model to make predictions of species richness within forest fragments of variable morphology. The model revealed that species richness of the observed bird community was maximized in small forest fragments with a high P/A. However, the number of forest interior species, a subset of the community with high conservation value, was maximized in large fragments with low P/A. 6. Synthesis and applications. Our results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness for conservation. This framework can be used to investigate the impacts of land-use change and fragmentation on species or assemblage richness, and to further understand trade-offs in species-specific occupancy probabilities associated with landscape variability.

New York↗

USGS2019

Plague management of prairie dog colonies: Degree and duration of deltamethrin flea control

Plague is a flea-borne disease of mammalian hosts. On the grasslands of western North America, plague stifles populations of Cynomys spp. prairie dogs (PDs). To manage plague, PD burrows are treated with 0.05% deltamethrin dust that can suppress flea numbers and plague transmission. Here, we evaluate the degree and duration of deltamethrin flea control with three PD species at six sites across four U.S. states. Data were simultaneously collected at paired plots. Burrows from one randomly assigned member of each pair were treated with deltamethrin; non-treated plots served as experimental baselines. Flea control was strong ≤two months after treatment, remained moderate one year later, and was statistically detectable for up to two years at some sites. Flea abundance was lower in plots with higher rates of deltamethrin application. After burrow treatments, flea abundance increased over time, reaching >one per PD within 255 to 352 days. Nevertheless, annual treatments of burrows with deltamethrin provided PDs with substantial protection against plague. Even so, deltamethrin should be further evaluated and combined with other tools under an integrated approach to plague management. Integrated plague management should help to conserve PDs and species that associate with them, including the endangered black-footed ferret ( Mustela nigripes ).

Montana, South Dakota, Utah↗

USGS2018

A distributed pipeline for DIDSON data processing

Technological advances in the field of ecology allow data on ecological systems to be collected at high resolution, both temporally and spatially. Devices such as Dual-frequency Identification Sonar (DIDSON) can be deployed in aquatic environments for extended periods and easily generate several terabytes of underwater surveillance data which may need to be processed multiple times. Due to the large amount of data generated and need for flexibility in processing, a distributed pipeline was constructed for DIDSON data making use of the Hadoop ecosystem. The pipeline is capable of ingesting raw DIDSON data, transforming the acoustic data to images, filtering the images, detecting and extracting motion, and generating feature data for machine learning and classification. All of the tasks in the pipeline can be run in parallel and the framework allows for custom processing. Applications of the pipeline include monitoring migration times, determining the presence of a particular species, estimating population size and other fishery management tasks.

Conference Paper↗

USGS2018

Biophysical assessment for indemnity selection of Federal Lands in Colorado

Information on the biophysical features of Federal lands identified as suitable for transfer to the State of Colorado was requested by the Bureau of Land Management (BLM). This information is intended for use in conducting an Environmental Assessment prior to the transfer of ownership (conveyance) to the State. The Colorado State Land Board filed a selective application to obtain public land and mineral estate in lieu of lands to which the State of Colorado was entitled but did not receive at the time of statehood. To address this legal obligation, 339 parcels of Federal lands (organized into 89 indemnity units [IUs]), currently under management by the BLM, have been identified as suitable for transfer to the State. The IUs include 23,130 acres of surface and mineral estate and 6,150 acres of mineral estate only. The specific land parcels to be transferred to the State will be finalized after an Environmental Assessment and other evaluations are completed. To provide the biophysical information necessary for conducting a future Environmental Assessment of the potential effects of the proposed land transfer, information on ecological communities, soil characteristics, and land use was summarized at three levels: (1) all of Colorado, (2) lands under the jurisdiction of the BLM, and (3) the 89 IUs. Information was also synthesized and summarized for 179 plant and animal species or subspecies of management concern to evaluate which species had the potential for occurrence on IUs. Datasets summarized for Colorado and for indemnity units and methodological details for all data summaries are provided in U.S. Geological Survey data releases available online at https://doi.org/10.5066/F7GT5MGV and https://doi.org/10.5066/F7C24VQ0 .

Colorado↗

USGS2017

The Bayesian group lasso for confounded spatial data

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Journal of Agricultural, Biological, and Environme↗

USGS2007

Initial Everglades Depth Estimation Network (EDEN) digital elevation model research and development

The Everglades Depth Estimation Network (EDEN) offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to the Comprehensive Everglades Restoration Plan (Telis, 2006). To produce historic and near-real time maps of water depths, the EDEN requires a system-wide digital elevation model (DEM) of the ground surface. Accurate Everglades wetland ground surface elevation data were non-existent before the U.S. Geological Survey (USGS) undertook the collection of highly accurate surface elevations at the regional scale. These form the foundation for EDEN DEM development. This development process is iterative as additional high accuracy elevation data (HAED) are collected, water surfacing algorithms improve, and additional ground-based ancillary data become available. Models are tested using withheld HAED and independently measured water depth data, and by using DEM data in EDEN adaptive management applications. Here the collection of HAED is briefly described before the approach to DEM development and the current EDEN DEM are detailed. Finally future research directions for continued model development, testing, and refinement are provided.

Florida↗

USGS2023

AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management

To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this – the ‘Automated Interactive Monitoring System (AIMS) for Wildlife’. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.

Journal of Environmental Management↗

USGS2010

Quantifying human disturbance in watersheds: Variable selection and performance of a GIS-based disturbance index for predicting the biological condition of perennial streams

Characterizing the relative severity of human disturbance in watersheds is often part of stream assessments and is frequently done with the aid of Geographic Information System (GIS)-derived data. However, the choice of variables and how they are used to quantify disturbance are often subjective. In this study, we developed a number of disturbance indices by testing sets of variables, scoring methods, and weightings of 33 potential disturbance factors derived from readily available GIS data. The indices were calibrated using 770 watersheds located in the western United States for which the severity of disturbance had previously been classified from detailed local data by the United States Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). The indices were calibrated by determining which variable or variable combinations and aggregation method best differentiated between least- and most-disturbed sites. Indices composed of several variables performed better than any individual variable, and best results came from a threshold method of scoring using six uncorrelated variables: housing unit density, road density, pesticide application, dam storage, land cover along a mainstem buffer, and distance to nearest canal/pipeline. The final index was validated with 192 withheld watersheds and correctly classified about two-thirds (68%) of least- and most-disturbed sites. These results provide information about the potential for using a disturbance index as a screening tool for a priori ranking of watersheds at a regional/national scale, and which landscape variables and methods of combination may be most helpful in doing so.

Ecological Indicators↗

USGS2020

Movements and habitat use of loons for assessment of conservation buffer zones in the Arctic Coastal Plain of northern Alaska

Oil and gas development in the Arctic Coastal Plain, Alaska, may pose threats to wildlife. Management guidelines within the National Petroleum Reserve-Alaska dictate buffer zones for coastal wildlife habitat and for breeding and foraging sites of yellow-billed loons (YBLOs; Gavia adamsii), a species of conservation concern. However, few data exist to assess the value of these buffer zones for YBLOs or for sympatrically breeding red-throated loons (RTLOs; G. stellata) and Pacific loons (PALOs; G. pacifica). We evaluated movements of these loons on the Arctic Coastal Plain to understand: 1) seasonal timing; 2) marine habitat use; and 3) spatial characteristics of breeding home ranges. We then used those findings to evaluate current management strategies in the context of loon biology. Compared to RTLOs and PALOs, YBLOs nested on larger lakes, stayed on them longer, had larger home ranges, but less frequently used the marine environment in summer. During autumn, YBLOs and PALOs were located farther from shore than during spring or summer, whereas RTLOs used the marine environment similarly during all three seasons. Management buffers established for YBLO nesting areas corresponded well with nest site space use of all three species, suggesting cross-taxa applicability of current management guidelines for nesting habitat. However, the majority of loon locations on the Arctic Coastal Plain were outside of coastal buffer zones established to protect fish and wildlife habitat within the National Petroleum Reserve-Alaska. These findings can inform assessment of conservation buffer zones to mitigate anthropogenic disturbance to loons in Alaska.

Alaska↗

USGS2018

Weather-centric rangeland revegetation planning

Invasive annual weeds negatively impact ecosystem services and pose a major conservation threat on semiarid rangelands throughout the western United States. Rehabilitation of these rangelands is challenging due to interannual climate and subseasonal weather variability that impacts seed germination, seedling survival and establishment, annual weed dynamics, wildfire frequency, and soil stability. Rehabilitation and restoration outcomes could be improved by adopting a weather-centric approach that uses the full spectrum of available site-specific weather information from historical observations, seasonal climate forecasts, and climate-change projections. Climate data can be used retrospectively to interpret success or failure of past seedings by describing seasonal and longer-term patterns of environmental variability subsequent to planting. A more detailed evaluation of weather impacts on site conditions may yield more flexible adaptive-management strategies for rangeland restoration and rehabilitation, as well as provide estimates of transition probabilities between desirable and undesirable vegetation states. Skillful seasonal climate forecasts could greatly improve the cost efficiency of management treatments by limiting revegetation activities to time periods where forecasts suggest higher probabilities of successful seedling establishment. Climate-change projections are key to the application of current environmental models for development of mitigation and adaptation strategies and for management practices that require a multidecadal planning horizon. Adoption of new weather technology will require collaboration between land managers and revegetation specialists and modifications to the way we currently plan and conduct rangeland rehabilitation and restoration in the Intermountain West.

Rangeland Ecology and Management↗