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At least 1,585 records · Page 88Linked to original sources
USGS2020

Comparison of SELDM simulated total-phosphorus concentrations with ecological impervious-area criteria

Ecological studies indicate that impervious cover (IC) greater than approximately 5%–20% may have adverse effects on receiving-stream ecology. It is difficult to separate the effects of runoff quality from other effects of urbanization on receiving streams. This study presents the results of a numerical experiment to assess the effects of increasing IC on water quality using the Stochastic Empirical Loading and Dilution Model (SELDM). Hydrologic and physiographic variables representative of southern New England were used to simulate receiving water quality in a basin with IC ranging from 0.1% to 30%. Simulation results mirror the results of ecological studies; event mean concentrations (EMCs) of total phosphorus (TP) increase proportionally to the logarithms of imperviousness for a given risk percentile. Simulation results indicated that commonly used stormwater treatment methods may be insufficient for mitigating the effects of imperviousness. Therefore, disconnection, rather than treatment, may be needed to protect water quality, and efforts to preserve undeveloped stream basins may be more effective than efforts to remediate conditions in highly developed basins. Results also indicate that commonly used water-quality criteria may be too restrictive for stormwater because TP EMCs frequently exceed these criteria, even in minimally developed basins.

Journal of Environmental Engineering↗
USGS1990

Reference manual for generation and analysis of Habitat Time Series: version II

The selection of an instream flow requirement for water resource management often requires the review of how the physical habitat changes through time. This review is referred to as 'Time Series Analysis." The Tune Series Library (fSLIB) is a group of programs to enter, transform, analyze, and display time series data for use in stream habitat assessment. A time series may be defined as a sequence of data recorded or calculated over time. Examples might be historical monthly flow, predicted monthly weighted usable area, daily electrical power generation, annual irrigation diversion, and so forth. The time series can be analyzed, both descriptively and analytically, to understand the importance of the variation in the events over time. This is especially useful in the development of instream flow needs based on habitat availability. The TSLIB group of programs assumes that you have an adequate study plan to guide you in your analysis. You need to already have knowledge about such things as time period and time step, species and life stages to consider, and appropriate comparisons or statistics to be produced and displayed or tabulated. Knowing your destination, you must first evaluate whether TSLIB can get you there. Remember, data are not answers . This publication is a reference manual to TSLIB and is intended to be a guide to the process of using the various programs in TSLIB. This manual is essentially limited to the hands-on use of the various programs. a TSLIB use interface program (called RTSM) has been developed to provide an integrated working environment where the use has a brief on-line description of each TSLIB program with the capability to run the TSLIB program while in the user interface. For information on the RTSM program, refer to Appendix F. Before applying the computer models described herein, it is recommended that the user enroll in the short course "Problem Solving with the Instream Flow Incremental Methodology (IFIM)." This course is offered by the Aquatic Systems Branch of the National Ecology Research Center. For more information about the TSLIB software, refer to the Memorandum of Understanding. Chapter 1 provides a brief introduction to the Instream Flow Incremental Methodology and TSLIB. Other chapters in this manual provide information on the different aspects of using the models. The information contained in the other chapters includes (2) acquisition, entry, manipulation, and listing of streamflow data; (3) entry, manipulation, and listing of the habitat-versus-streamflow function; (4) transferring streamflow data; (5) water resources systems analysis; (6) generation and analysis of daily streamflow and habitat values; (7) generation of the time series of monthly habitats; (8) manipulation, analysis, and display of month time series data; and (9) generation, analysis, and display of annual time series data. Each section includes documentation for the programs therein with at least one page of information for each program, including a program description, instructions for running the program, and sample output. The Appendixes contain the following: (A) sample file formats; (B) descriptions of default filenames; (C) alphabetical summary of batch-procedure files; (D) installing and running TSLIB on a microcomputer; (E) running TSLIB on a CDC Cyber computer; (F) using the TSLIB user interface program (RTSM); and (G) running WATSTORE on the USGS Amdahl mainframe computer. The number for this version of TSLIB--Version II-- is somewhat arbitrary, as the TSLIB programs were collected into a library some time ago; but operators tended to use and manage them as individual programs. Therefore, we will consider the group of programs from the past that were only on the CDC Cyber computer as Version 0; the programs from the past that were on both the Cyber and the IBM-compatible microcomputer as Version I; and the programs contained in this reference manual as Version II.

Report↗
USGS2018

Inferring species interactions through joint mark–recapture analysis

Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark–recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark–recapture models that allow for joint modeling of interacting species. Here, we introduce a two‐species mark–recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub ( Gila cypha ) and introduced rainbow trout ( Oncorhynchus mykiss ) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long‐term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. Environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. The general approach presented here for combining mark–recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.

Ecology↗
USGS1997

Land cover characterization and land surface parameterization research

The understanding of land surface processes and their parameterization in atmospheric, hydrologic, and ecosystem models has been a dominant research theme over the past decade. For example, many studies have demonstrated the key role of land cover characteristics as controlling factors in determining land surface processes, such as the exchange of water, energy, carbon, and trace gases between the land surface and the lower atmosphere. The requirements for multiresolution land cover characteristics data to support coupled-systems modeling have also been well documented, including the need for data on land cover type, land use, and many seasonally variable land cover characteristics, such as albedo, leaf area index, canopy conductance, surface roughness, and net primary productivity. Recently, the developers of land data have worked more closely with the land surface process modelers in these efforts.

Ecological Applications↗
USGS2019

Spatial and temporal variation in river corridor exchange across a 5th order mountain stream network

Although most field and modeling studies of river corridor exchange have been conducted a scales ranging from 10’s to 100’s of meters; results of these studies are used to predict their ecological and hydrological influences at the scale of river networks. Further complicating prediction, exchanges are expected to vary with hydrologic forcing and the local geomorphic setting. While we desire predictive power, we lack a complete spatiotemporal relationship relating discharge to the variation in geologic setting and hydrologic forcing that are expected across a river basin. Indeed, Wondzell’s (2011) conceptual model predicts systematic variation in river corridor exchange as a function of (1) variation in baseflow over time at a fixed location, (2) variation in discharge with location in the river network, and (3) local geomorphic setting. To test this conceptual model we conducted more than 60 solute tracer studies including a synoptic campaign in the 5th order river network of the H.J. Andrews Experimental Forest (Oregon, USA) and replicate-in-time experiments in four watersheds. We interpret the data using a series of metrics describing river corridor exchange and solute transport, testing for consistent direction and magnitude of relationships relating these metrics to discharge and local geomorphic setting. We confirmed systematic decrease in river corridor exchange space through the river networks, from headwaters to the larger mainstem. However, we did not find systematic variation with changes in discharge through time, nor with local geomorphic setting. While interpretation of our results is complicated by problems with the analytical methods, they are sufficiently robust for us to conclude that space-for-time and time-for-space substitutions are not appropriate in our study system. Finally, we suggest two strategies that will improve the interpretability of tracer test results and help the hyporheic community develop robust data sets that will enable comparisons across multiple sites and/or discharge conditions.

Hydrology and Earth System Sciences↗
USGS2026

From understanding to action: Integrating new and old methodologies to manage marine infectious disease

Marine diseases can have far-reaching effects on population, community and ecosystem health; however, our ability to track, predict and manage these diseases has, historically, been poor. As a result, the fields of disease ecology and epidemiology have developed at a slower pace for marine than terrestrial systems [ 1 ]. New methodologies, including genomic tools for diagnostics [ 2 , 3 ], transcriptomic tools for measuring host and pathogen responses to infection (e.g. [ 4 , 5 ]), regional oceanic modelling systems that estimate environmental conditions influencing pathogen dispersal and disease progression [ 6 ], artificial intelligence methods for quantifying pathology from images (e.g. [ 7 ]) and advanced disease modelling techniques [ 8 , 9 ] are precipitating a rapid increase in our understanding of marine pathosystems. In 2016, these efforts led to the first special issue of Philosophical Transactions of the Royal Society B ( Marine diseases, volume 371, issue 1689) focused entirely on marine disease ecology and evolution, and in 2020, the first book, Marine disease ecology, was devoted to this topic [ 10 ]. This special issue, focused on marine disease management , is being published a decade after the first Philosophical Transactions special issue on marine diseases. The shift to a management focus reflects an urgent need for management strategies to address high-impact diseases and the rapid methodological advances that have resulted. The papers included in this issue demonstrate the value of combining classical approaches (e.g. routine disease surveillance, reductionistic pathogen challenge trials, rapid throughput diagnostics) with cutting-edge technologies (e.g. high-resolution oceanographic models, Bayesian models, replicated transcriptomic studies) to identify drivers of disease, quantify impacts and suggest management strategies.

Philosophical Transactions of the Royal Society, S↗
USGS2020

Characterizing benthic macroinvertebrate and algal biological condition gradient models for California wadeable Streams, USA

The Biological Condition Gradient (BCG) is a conceptual model that describes changes in aquatic communities under increasing levels of anthropogenic stress. The BCG helps decision-makers connect narrative water quality goals (e.g., maintenance of natural structure and function) to quantitative measures of ecological condition by linking index thresholds based on statistical distributions (e.g., percentiles of reference distributions) to expert descriptions of changes in biological condition along disturbance gradients. As a result, the BCG may be more meaningful to managers and the public than indices alone. To develop a BCG model, biological response to stress is divided into 6 levels of condition, represented as changes in biological structure (abundance and diversity of pollution sensitive versus tolerant taxa) and function. We developed benthic macroinvertebrate (BMI) and algal BCG models for California perennial wadeable streams to support interpretation of percentiles of reference-based thresholds for bioassessment indices (i.e., the California Stream Condition Index [CSCI] for BMI and the Algal Stream Condition Index [ASCI] for diatoms and soft-bodied algae). Two panels (one of BMI ecologists and the other of algal ecologists) each calibrated a general BCG model to California wadeable streams by first assigning taxa to specific tolerance and sensitivity attributes, and then independently assigning test samples (264 BMI and 248 algae samples) to BCG Levels 1–6. Consensus on the assignments was developed within each assemblage panel using a modified Delphi method. Panels then developed detailed narratives of changes in BMI and algal taxa that correspond to the 6 BCG levels. Consensus among experts was high, with 81% and 82% expert agreement within 0.5 units of assigned BCG level for BMIs and algae, respectively. According to both BCG models, the 10th percentiles index scores at reference sites corresponded to a BCG Level 3, suggesting that this type of threshold would protect against moderate changes in structure and function while allowing loss of some sensitive taxa. The BCG provides a framework to interpret changes in aquatic biological condition along a gradient of stress. The resulting relationship between index scores and BCG levels and narratives can help decision-makers select thresholds and communicate how these values protect aquatic life use goals.

California↗
USGS2017

The practice of prediction: What can ecologists learn from applied, ecology-related fields?

The pervasive influence of human induced global environmental change affects biodiversity across the globe, and there is great uncertainty as to how the biosphere will react on short and longer time scales. To adapt to what the future holds and to manage the impacts of global change, scientists need to predict the expected effects with some confidence and communicate these predictions to policy makers. However, recent reviews found that we currently lack a clear understanding of how predictable ecology is, with views seeing it as mostly unpredictable to potentially predictable, at least over short time frames. However, in applied, ecology-related fields predictions are more commonly formulated and reported, as well as evaluated in hindsight, potentially allowing one to define baselines of predictive proficiency in these fields. We searched the literature for representative case studies in these fields and collected information about modeling approaches, target variables of prediction, predictive proficiency achieved, as well as the availability of data to parameterize predictive models. We find that some fields such as epidemiology achieve high predictive proficiency, but even in the more predictive fields proficiency is evaluated in different ways. Both phenomenological and mechanistic approaches are used in most fields, but differences are often small, with no clear superiority of one approach over the other. Data availability is limiting in most fields, with long-term studies being rare and detailed data for parameterizing mechanistic models being in short supply. We suggest that ecologists adopt a more rigorous approach to report and assess predictive proficiency, and embrace the challenges of real world decision making to strengthen the practice of prediction in ecology.

Ecological Complexity↗
USGS2022

Identifying monitoring information needs that support the management of fish in large rivers

Management actions intended to benefit fish in large rivers can directly or indirectly affect multiple ecosystem components. Without consideration of the effects of management on non-target ecosystem components, unintended consequences may limit management efficacy. Monitoring can help clarify the effects of management actions, including on non-target ecosystem components, but only if data are collected to characterize key ecosystem processes that could affect the outcome. Scientists from across the U.S. convened to develop a conceptual model that would help identify monitoring information needed to better understand how natural and anthropogenic factors affect large river fishes. We applied the conceptual model to case studies in four large U.S. rivers. The application of the conceptual model indicates the model is flexible and relevant to large rivers in different geographic settings and with different management challenges. By visualizing how natural and anthropogenic drivers directly or indirectly affect cascading ecosystem tiers, our model identified critical information gaps and uncertainties that, if resolved, could inform how to best meet management objectives. Despite large differences in the physical and ecological contexts of the river systems, the case studies also demonstrated substantial commonalities in the data needed to better understand how stressors affect fish in these systems. For example, in most systems information on river discharge and water temperature were needed and available. Conversely, information regarding trophic relationships and the habitat requirements of larval fishes were generally lacking. This result suggests that there is a need to better understand a set of common factors across large-river systems. We provide a stepwise procedure to facilitate the application of our conceptual model to other river systems and management goals.

Environmental Management↗
USGS2020

How and why is the timing and occurrence of seasonal migrants in the Gulf of Maine changing due to climate?

Plants and animals undergo certain recurring life-cycle events, such as migrations between summer and winter habitats or the annual blooming of plants. Known as phenology, the timing of these events is very sensitive to changes in climate (and changes in one species’ phenology can impact entire food webs and ecosystems). Shifts in phenology have been described as a “fingerprint” of the temporal and spatial responses of wildlife to climate change impacts. Thus, phenology provides one of the strongest indicators of the adaptive capacity of organisms (or the ability of organisms to cope with future environmental conditions). In this study, researchers are exploring how the timing and occurrence of a number of highly migratory marine animals is changing due to a series of climatic and ecological shifts. First, using existing long-term historical data series, they will determine the direction and magnitude of how migration, abundance, or other phenological factors have changed for marine mammals, sea turtles, and fishes that migrate into the Gulf of Maine on a seasonal basis. Because marine animals are inherently difficult to detect, the team will apply dynamic occupancy models to evaluate seasonal migration patterns and habitat use across multiple habitats in the Gulf of Maine region. The project team will also synthesize regional information on a key, ecologically-important prey fish, sandlance, whose timing and abundance is a strong predictor of the occurrence and behavior of predator species targeted in this study as well as a range of other regional fish and wildlife of conservation and management concern. Results from this component of the project will identify coastal fish and wildlife species that are relatively more or less able to adapt and thus potentially vulnerable to climate change; determine the likely primary drivers of those changes; and identify data gaps and future monitoring needs. Ultimately, this information will be available and useful for regional coastal management and adaptation decisions that will allow managers to effectively plan for the future. In a second component of the project, researchers will focus specifically on changes in migration patterns of the endangered North Atlantic right whale. While shifts in the distribution and time of recurring life events are adaptive responses that may help species cope with climate impacts, they can also lead to changes in how species interact with humans. The North Atlantic right whale is one of the most endangered whale species on the planet. In the North Atlantic Ocean, ship strikes and entanglements with commercial fishing gear represent fatal threats to right whales. Recent reports suggest that North Atlantic right whale migration patterns have changed. Many researchers posit that shifts in migration are responsible for recent increases in the overlap between right whales and human activities, especially fishing. To help understand how changes in right whale movements and behaviors may overlap with ship traffic, and thus put the animals at risk of encountering vessels, we will combine right whale habitat models with ship traffic maps. The end result will be a set of maps identifying risk levels.

Gulf of Maine↗
USGS2014

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

Limnology and Oceanography: Methods↗
USGS2021

Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters

Executive Summary Along the coast of the U.S. Gulf of Mexico, the eastern oyster ( Crassostrea virginica ) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach. Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs. Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions. While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data. Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management. Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.

Alabama, Florida, Louisiana, Mississippi, Texas↗
USGS2010

Making molehills out of mountains: Landscape genetics of the Mojave desert tortoise

Heterogeneity in habitat often influences how organisms traverse the landscape matrix that connects populations. Understanding landscape connectivity is important to determine the ecological processes that influence those movements, which lead to evolutionary change due to gene flow. Here, we used landscape genetics and statistical models to evaluate hypotheses that could explain isolation among locations of the threatened Mojave desert tortoise ( Gopherus agassizii ). Within a causal modeling framework, we investigated three factors that can influence landscape connectivity: geographic distance, barriers to dispersal, and landscape friction. A statistical model of habitat suitability for the Mojave desert tortoise, based on topography, vegetation, and climate variables, was used as a proxy for landscape friction and barriers to dispersal. We quantified landscape friction with least-cost distances and with resistance distances among sampling locations. A set of diagnostic partial Mantel tests statistically separated the hypotheses of potential causes of genetic isolation. The best-supported model varied depending upon how landscape friction was quantified. Patterns of genetic structure were related to a combination of geographic distance and barriers as defined by least-cost distances, suggesting that mountain ranges and extremely low-elevation valleys influence connectivity at the regional scale beyond the tortoises’ ability to disperse. However, geographic distance was the only influence detected using resistance distances, which we attributed to fundamental differences between the two ways of quantifying friction. Landscape friction, as we measured it, did not influence the observed patterns of genetic distances using either quantification. Barriers and distance may be more valuable predictors of observed population structure for species like the desert tortoise, which has high dispersal capability and a long generation time.

California, Nevada, Utah, Arizona↗
USGS2011

Chapter 9: Occurrence of small mammals: Deer mice and challenge of trapping across large spatial extents

Small mammal communities living in sagebrush ( Artemisia spp. ) may be sensitive to habitat isolation and invasion by exotic grass species. Yet there have been no spatially explicit models to improve our understanding of landscape-scale factors determining small mammal occurrence or abundance. We live-trapped small mammals at 186 locations in the Wyoming Basin Ecoregional Assessment area to develop species distribution (habitat) models for each species. Most small mammal species (n = 14) were trapped at a only few locations. As a result, we developed a small mammal model only for the deer mouse ( Peromyscus maniculatus ). Deer mice were associated with areas having moderately productive habitat as measured by Normalized Difference Vegetation Index (NDVI), increased grassland land cover, contagion of sagebrush land cover, and proximity to intermittent water. The proportion of big sagebrush ( Artemisia tridentata ) within 0.27 km, proportion of mixed shrubland within 5 km, soil clay content, and proximity to pipelines were inversely related to the occurrence of deer mice. Understanding habitat characteristics for deer mice helps our overall understanding of the ecological processes within sagebrush habitats because deer mice act as predator, prey, competitor, and disease reservoir. Development of the empirical data necessary for spatially explicit habitat modeling of small mammal distributions at large spatial extents requires an extensive trapping effort in order to obtain enough observations to construct models, calculate robust detectability estimates, and overcome issues such as trap shyness and population cycling.

Wyoming↗
USGS2019

GPS tracking data reveals daily spatio-temporal movement patterns of waterfowl

Background Spatio-temporal patterns of movement can characterize relationships between organisms and their surroundings, and address gaps in our understanding of species ecology, activity budgets, bioenergetics, and habitat resource management. Highly mobile waterfowl, which can exploit resources over large spatial extents, are excellent models to understand relationships between movements and resource usage, landscape interactions and specific habitat needs. Methods We tracked 3 species of dabbling ducks with GPS-GSM transmitters in 2015–17 to examine fine-scale movement patterns over 24 h periods (30 min interval), dividing movement pathways into temporally continuous segments and spatially contiguous patches. We quantified distances moved, area used and time allocated across the day, using linear and generalized linear mixed models. We investigated behavior through relationships between these variables. Results Movements and space-use were small, and varied by species, sex and season. Gadwall ( Mareca strepera ) generally moved least (FFDs: 0.5–0.7 km), but their larger foraging patches resulted from longer within-area movements. Pintails ( Anas acuta ) moved most, were more likely to conduct flights > 300 m, had FFDs of 0.8–1.1 km, used more segments and patches per day that they revisited more frequently, resulting in the longest daily total movements. Females and males differed only during the post-hunt season when females moved more. 23.6% of track segments were short duration (1–2 locations), approximately 1/3 more than would be expected if they occurred randomly, and were more dispersed in the landscape than longer segments. Distance moved in 30 min shortened as segment duration increased, likely reflecting phases of non-movement captured within segments. Conclusions Pacific Flyway ducks spend the majority of time using smaller foraging and resting areas than expected or previously reported, implying that foraging areas may be highly localized, and nutrients obtainable from smaller areas. Additionally, movement reductions over time demonstrates behavioral adjustments that represent divergent energetic demands, the detection of which is a key advantage of higher frequency data. Ducks likely use less energy for movement than currently predicted and management, including distribution and configuration of essential habitat, may require reconsideration. Our study illustrates how fine-scale movement data from tracking help understand and inform various other fields of research.

California↗
USGS2022

Species distributions and the recognition of risk in restoration planning: A case study of salmonid fishes

One of the risks faced by habitat restoration practitioners is whether habitats included in restoration planning will be used by the target species or, conversely, whether habitats excluded from restoration planning would have benefited the target species. With the goal of providing a quantitative decision-making approach that represented varying levels of risk tolerance, we used multiple probability decision thresholds (PDT) to predict the range of occurrence for three anadromous fishes ( Oncorhynchus spp.) in a watershed in southwestern Washington, USA. For each species, we compared the predicted range of occurrence to the distribution used for restoration planning and quantified the amount of habitat blocked by anthropogenic barriers. Coho salmon ( O. kisutch ) had the broadest predicted range of occurrence (3061.6–6357.9 km; 0.75–0.25 PDT), followed by steelhead trout ( O. mykiss ; 1828.8–2836.8 km) and chum salmon ( O. keta ; 1373.9–1629.1 km). For each species, the predicted range of occurrence was similar or greater than the distribution used for restoration planning, suggesting that the current plan may exclude habitats that would benefit each species. Coho salmon had the greatest percentage of habitat blocked by anthropogenic barriers, followed by steelhead trout and chum salmon, respectively. Modeling species distributions at multiple risk-tolerance scenarios acknowledges uncertainty in restoration planning and allows practitioners to weigh the ecological benefits and budgetary constraints when considering locations for restoration. To effectively communicate restoration science to support practitioners in decision-making, we developed an R Shiny application online user interface available at: https://shiny.wdfw-fish.us/ChehalisRiverBasinSalmonidRangeOfOccurence/ .

Washington↗
USGS2022

Managing multiple species with conflicting needs in the Greater Everglades

Given limited funding, natural resources decision making is riddled with tradeoffs, including which species or landscapes to prioritize for management action. Florida’s Everglades wetland is home to numerous indicator species, some of which are endangered. But with a multitude of species comes differing hydrologic requirements to yield appropriate foraging and breeding conditions for each. The Everglades ecosystem is highly managed, with water being moved across the landscape to meet the habitat and reproductive needs of species of concern. Predictive modeling can help water managers understand potential consequences to targeted water conditions. EverForecast is a novel spatially explicit, hydrologic, and ecological operational forecast developed to inform conservation management decisions. Not only does EverForecast provide probable near-term water conditions, but also predicted species responses to those hydrologic conditions. Using examples from two focal regions of the Everglades, we show the magnitude of impacts to a suite of species and an almost 70% decline in suitable conditions for one species when prioritizing water management to meet the needs of another species. Although EverForecast is a relatively new decision support tool, its hydrologic outputs are already commonly used to make water management recommendations because it provides near-term hydrologic forecasts that scientists and managers need for water operations decision making. Because species management decisions have historically been made to target a single species at a time, it may take longer for full utility of EverForecast’s ability to quantify tradeoffs among species to become integrated into decision making.

Florida↗
USGS2010

Genetic diversity of lake whitefish in lakes Michigan and Huron: sampling, standardization, and research priorities

We combined data from two laboratories to increase the spatial extent of a genetic data set for lake whitefish Coregonus clupeaformis from lakes Huron and Michigan and saw that genetic diversity was greatest between lakes, but that there was also structuring within lakes. Low diversity among stocks may be a reflection of relatively recent colonization of the Great Lakes, but other factors such as recent population fluctuation and localized stresses such as lamprey predation or heavy exploitation may also have a homogenizing effect. Our data suggested that there is asymmetrical movement of lake whitefish between Lake Huron and Lake Michigan; more genotypes associated with Lake Michigan were observed in Lake Huron. Adding additional collections to the calibrated set will allow further examination of diversity in other Great Lakes, answer questions regarding movement among lakes, and estimate contributions of stocks to commercial yields. As the picture of genetic diversity and population structure of lake whitefish in the Great Lakes region emerges, we need to develop methods to combine data types to help identify important areas for biodiversity and thus conservation. Adding genetic data to existing models will increase the precision of predictions of the impacts of new stresses and changes in existing pressures on an ecologically and commercially important species.

Journal of Great Lakes Research↗