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At least 181 records · Page 10Linked to original sources
USGS2019

Social–ecological mismatches create conservation challenges in introduced species management

Introduced species can have important effects on the component species and processes of native ecosystems. However, effective introduced species management can be complicated by technical and social challenges. We identify “social–ecological mismatches” (that is, differences between the scales and functioning of interacting social and ecological systems) as one such challenge. We present three case studies in which mismatches between the organization and functioning of key social and ecological systems have contributed to controversies and debates surrounding introduced species management and policy. We identify three common issues: social systems and cultures may adapt to a new species’ arrival at a different rate than ecosystems; ecological impacts can arise at one spatial scale while social impacts occur at another; and the effects of introduced species can spread widely, whereas management actions are constrained by organizational and/or political boundaries. We propose strategies for collaborative knowledge building and adaptive management that may help address these challenges.

Frontiers in Ecology and the Environment↗
USGS2024

Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands

Context Dynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging. Objectives We identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment. Methods Using high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance. Results We found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R 2 = 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales. Conclusions Our results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems.

Idaho↗
USGS2018

Evidence and opportunities for integrating landscape ecology into natural resource planning across multiple-use landscapes

Enhancing natural resource management has been a focus of landscape ecology since its inception, but numerous authors argue that landscape ecology has not yet been effective in achieving the underlying goal of planning and designing sustainable landscapes. We developed nine questions reflecting the application of fundamental research topics in landscape ecology to the landscape planning process and reviewed two recent landscape-scale plans in western North America for evidence of these concepts in plan decisions. Both plans considered multiple resources, uses, and values, including energy development, recreation, conservation, and protection of cultural and historic resources. We found that land use change and multiscale perspectives of resource uses and values were very often apparent in planning decisions. Pattern-process relationships, connectivity and fragmentation, ecosystem services, landscape history, and climate change were reflected less frequently. Landscape sustainability was considered only once in the 295 decisions reviewed, and outputs of landscape models were not referenced. We suggest six actionable opportunities for further integrating landscape ecology concepts into landscape planning efforts: 1) use landscape sustainability as an overarching goal, 2) adopt a broad ecosystem services framework, 3) explore the role of landscape history more comprehensively, 4) regularly consider and accommodate potential effects of climate change, 5) use landscape models to support plan decisions, and 6) promote a greater presence of landscape ecologists within agencies that manage large land bases and encourage active involvement in agency planning efforts. Together these actions may improve the defensibility, durability, and sustainability of landscape plan decisions.

Current Landscape Ecology Reports↗
USGS2014

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

Ecological Informatics↗
USGS2017

A guide to multi-objective optimization for ecological problems with an application to cackling goose management

Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem. A large class of methods for solving MOO problems can be separated into two strategies: modelling preferences pre-optimization (the a priori strategy), or modelling preferences post-optimization (the a posteriori strategy). The a priori strategy requires describing preferences among objectives without knowledge of how preferences affect the resulting decision. In the a posteriori strategy, the decision maker simultaneously considers a set of solutions (the Pareto optimal set) and makes a choice based on the trade-offs observed in the set. We describe several methods for modelling preferences pre-optimization, including: the bounded objective function method, the lexicographic method, and the weighted-sum method. We discuss modelling preferences post-optimization through examination of the Pareto optimal set. We applied each MOO strategy to the natural resource management problem of selecting a population target for cackling goose ( Branta hutchinsii minima ) abundance. Cackling geese provide food security to Native Alaskan subsistence hunters in the goose's nesting area, but depredate crops on private agricultural fields in wintering areas. We developed objective functions to represent the competing objectives related to the cackling goose population target and identified an optimal solution first using the a priori strategy, and then by examining trade-offs in the Pareto set using the a posteriori strategy. We used four approaches for selecting a final solution within the a posteriori strategy; the most common optimal solution, the most robust optimal solution, and two solutions based on maximizing a restricted portion of the Pareto set. We discuss MOO with respect to natural resource management, but MOO is sufficiently general to cover any ecological problem that contains multiple competing objectives that can be quantified using objective functions.

Ecological Modelling↗
USGS2021

Emerging perspectives on resource tracking and animal movement ecology

Resource tracking, where animals increase energy gain by moving to track phenological variation in resources across space, is emerging as a fundamental attribute of animal movement ecology. However, a theoretical framework to understand when and where resource tracking should occur, and how resource tracking should lead to emergent ecological patterns, is lacking. We present a framework that unites concepts from optimal foraging theory and landscape ecology, which can be used to generate and test predictions on how resource dynamics shape animal movement across taxa, systems, and scales. Consideration of the interplay between animal movement and resource dynamics not only advances ecological understanding but can also guide biodiversity conservation in an era of global change.

Trends in Ecology & Evolution↗
USGS2019

The Laurentian Great Lakes: A case study in ecological disturbance and climate change

Climate change effects are already significant, but can also magnify other ecological problems. This can be clearly seen in the Laurentian Great Lakes, which have suffered habitat degradation, fishery overharvest and dramatic alterations by invasive species. Thermal changes are expected to cause extensive loss of suitable fish habitat, and changing precipitation patterns will aggravate the problems with our highly modified lotic and lentic systems. A brief summary of the historic ecological context provided by the Great Lakes case is presented, followed by the descriptions of selected tools that help to understand and evaluate both ecological and climate change problems. Species distribution models and habitat classification combined with climate change predictions can identify the distribution and extent of optimal habitats, and identify which are most vulnerable to climate change. Ecological flow modelling can help to identify when critical flow changes are likely. Mechanistic simulation modelling specifies understanding of how aquatic systems function and can reveal cause and effect relationships. These tools can be used to help managers to protect optimal habitat, resist climate change effects to other habitats and adapt cultural systems to climate‐altered aquatic systems.

Great Lakes↗
USGS2024

Migratory strategies across an ecological barrier: Is the answer blowing in the wind?

Background: Ecological barriers can shape the movement strategies of migratory animals that navigate around or across them, creating migratory divides. Wind plays a large role in facilitating aerial migrations, and can temporally or spatially change the challenge posed by an ecological barrier, with beneficial winds potentially converting a barrier to a corridor. Here, we explore the role wind plays in shaping initial southbound migration strategy between two populations departing from different locations along an ecological barrier. Methods: Using GPS satellite transmitters, we tracked the southbound migration of two populations of Short-billed Dowitchers ( Limnodromus griseus caurinus ) from breeding grounds in Alaska to wintering sites in coastal Mexico. The breeding grounds were positioned in distinct regions along an ecological barrier, the Gulf of Alaska. Between the two populations, we compared migratory timing, wind availability at, and tailwind support en route across the Gulf of Alaska. Results: Route choice and arrival timing to wintering sites differed markedly between the two populations: individuals departing from the more westerly site (King Salmon) left at the same time as those from further east (Beluga) but crossed the Gulf of Alaska farther west and arrived along the Pacific coast of Mexico an average of 19 days earlier than their counterparts. Dowitchers from both sites used a slight tailwind to cue departure, but once aloft over the Gulf of Alaska, birds from the more westerly site had up to ten times more tailwind assistance than birds from the more easterly one. Conclusions: The distinct migration strategies, and degree of wind assistance experienced, of these two populations demonstrates how differences in wind availability along migratory routes may form the basis for intraspecific variation in migration strategies with potential carryover effects. Future changes in wind regimes may therefore interact with changes in habitat availability to influence migration patterns and migratory bird conservation.

Alaska↗
USGS2024

Ecological trade-offs associated with fuel breaks in the sagebrush ecosystem

Background Unprecedented wildfire frequency, fueled by invasive annual grasses, threatens sagebrush ecosystems. To suppress wildfire and conserve sagebrush, land management agencies have installed fuel breaks across the sagebrush biome. However, despite the potential reduction in wildfire, fuel breaks may have ecological costs. Determining an acceptable balance between risks and benefits of fuel breaks is needed to avoid accelerating sagebrush loss, annual grass invasion, and habitat degradation. To evaluate the potential for ecological trade-offs to occur, we characterized the contexts in which known fuel breaks currently exist. We synthesized spatial data on all known fuel breaks and a suite of variables that may contribute to fuel break risks and benefits, including burn probabilities, predicted fuel break effectiveness, linear infrastructure, invasive annual grass cover, soil moisture conditions that confer resistance to invasion and resilience to disturbance, and priority wildlife habitats across the sagebrush biome. Results We found that within the sagebrush biome, fuel breaks are generally located in areas with high burn probability and are thus positioned well to intercept potential wildfires. However, fuel breaks are also frequently positioned in areas with lower predicted fuel break effectiveness relative to the sagebrush biome overall. Fuel breaks also are spatially associated with high invasive grass cover, indicating the need to better understand the causal relationship between fuel breaks and annual invasive grasses. We also show that the fuel break network is dense within priority wildlife habitats. Dense fuel breaks within wildlife habitats may trade off wildfire protection for decreased integrity of such habitats. Conclusions Our analyses describe the potential for fuel breaks to invoke ecological trade-offs and show that the balance of risks and benefits differs across sagebrush ecosystems. Strategic research and actions are needed to evaluate which factors tip the balance towards maximizing wildfire suppression while minimizing risk to sensitive ecological resources.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2014

Barcodes are a useful tool for labeling and tracking ecological samples

Barcodes are used to label and track just about everything these days. Look around your office, in your medicine cabinet, at the package you just received in the mail, or on the shelves of any shop in town, and you will immediately grasp the ubiquity of their use. Interestingly, railroads and supermarkets were the early pioneers of barcode development: the former needing a way to track railway car location and ownership on a national scale, the latter needing a way to track a diverse array of products and to decrease checkout times (Nelson 1997). Barcodes first came to use in the sciences via the field of medicine, and the medical literature contains hundreds of publications describing how this technology has reduced errors in patient specimen identification and handling, where error mitigation is crucial. In short, barcodes have been adopted by many industries, and in many fields they are now synonymous with asset tracking. In spite of their potential to efficiently organize “assets” (i.e., samples) and minimize human error, the use of barcodes has yet to gain widespread application in ecology. In an age where students take notes on laptops instead of paper, and where “text messaging” involves a smartphone rather than a ball‐point pen, why do otherwise tech‐savvy ecologists persist in hand‐labeling samples? Why do we repeatedly transcribe long and unique identifiers at each step in the process of sample analysis, thereby wasting time and creating opportunities for transcription errors and data loss? Why are most sample storage areas only successfully navigable by the lab manager who personally shelved the samples? In the case of our large ecology lab—and, we suspect, in many others as well—the answer to these questions was perpetually, “bar‐coding won't be worth the trouble.” Recently, however, we realized this was no longer a sufficient answer when we started a new research project that involved collecting an additional thousands of samples each year; we decided to embrace the tangible benefits of an electronic labeling system, and implemented barcoding in our lab. To be clear, the use of barcoding in ecology is not completely novel, and there have been early adopters of this technology. For example, the Cedar Creek Long Term Ecological Research (LTER) site has been using barcodes since at least the mid‐1990s to track the large number of samples collected in their long‐term experimental grasslands (T. A. Kennedy, personal observation ). Overall, however, Cedar Creek is an outlier: in informal e‐mail surveys of LTER sites, only two of eight respondents used barcodes for sample identification or tracking, and even then their use was generally limited to certain samples or certain stages of sample analysis. Our objective in this article is to use our lab as a case study to highlight the potential of barcodes to simplify numerous aspects of sample collection and processing.

Arizona↗
USGS2008

Arctic climate change and its impacts on the ecology of the North Atlantic

Arctic climate change from the Paleocene epoch to the present is reconstructed with the objective of assessing its recent and future impacts on the ecology of the North Atlantic. A recurring theme in Earth's paleoclimate record is the importance of the Arctic atmosphere, ocean, and cryosphere in regulating global climate on a variety of spatial and temporal scales. A second recurring theme in this record is the importance of freshwater export from the Arctic in regulating global- to basin-scale ocean circulation patterns and climate. Since the 1970s, historically unprecedented changes have been observed in the Arctic as climate warming has increased precipitation, river discharge, and glacial as well as sea-ice melting. In addition, modal shifts in the atmosphere have altered Arctic Ocean circulation patterns and the export of freshwater into the North Atlantic. The combination of these processes has resulted in variable patterns of freshwater export from the Arctic Ocean and the emergence of salinity anomalies that have periodically freshened waters in the North Atlantic. Since the early 1990s, changes in Arctic Ocean circulation patterns and freshwater export have been associated with two types of ecological responses in the North Atlantic. The first of these responses has been an ongoing series of biogeographic range expansions by boreal plankton, including renewal of the trans-Arctic exchanges of Pacific species with the Atlantic. The second response was a dramatic regime shift in the shelf ecosystems of the Northwest Atlantic that occurred during the early 1990s. This regime shift resulted from freshening and stratification of the shelf waters, which in turn could be linked to changes in the abundances and seasonal cycles of phytoplankton, zooplankton, and higher trophic-level consumer populations. It is predicted that the recently observed ecological responses to Arctic climate change in the North Atlantic will continue into the near future if current trends in sea ice, freshwater export, and surface ocean salinity continue. It is more difficult to predict ecological responses to abrupt climate change in the more distant future as tipping points in the Earth's climate system are exceeded.

Ecology↗
USGS2009

The ecology of dust

Wind erosion and associated dust emissions play a fundamental role in many ecological processes and provide important biogeochemical connectivity at scales ranging from individual plants up to the entire globe. Yet, most ecological studies do not explicitly consider dust‐driven processes, perhaps because most relevant research on aeolian (wind‐driven) processes has been presented in a geosciences rather than an ecological context. To bridge this disciplinary gap, we provide a general overview of the ecological importance of dust, examine complex interactions between wind erosion and ecosystem dynamics from the scale of plants and surrounding space to regional and global scales, and highlight specific examples of how disturbance affects these interactions and their consequences. It is likely that changes in climate and intensification of land use will lead to increased dust production from many drylands. To address these issues, environmental scientists, land managers, and policy makers need to consider wind erosion and dust emissions more explicitly in resource management decisions.

Frontiers in Ecology and the Environment↗
USGS2014

The changing role of history in restoration ecology

In the face of rapid environmental and cultural change, orthodox concepts in restoration ecology such as historical fidelity are being challenged. Here we re-examine the diverse roles played by historical knowledge in restoration, and argue that these roles remain vitally important. As such, historical knowledge will be critical in shaping restoration ecology in the future. Perhaps the most crucial role in shifting from the present version of restoration ecology (“v1.0”) to a newer formulation (“v2.0”) is the value of historical knowledge in guiding scientific interpretation, recognizing key ecological legacies, and influencing the choices available to practitioners of ecosystem intervention under conditions of open-ended and rapid change.

Frontiers in Ecology and the Environment↗
USGS2013

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (<1 km 2 ) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km 2 ) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

Wyoming↗
USGS2013

Host range, host ecology, and distribution of more than 11800 fish parasite species

Our data set includes 38 008 fish parasite records (for Acanthocephala, Cestoda, Monogenea, Nematoda, Trematoda) compiled from the scientific literature, Internet databases, and museum collections paired to the corresponding host ecological, biogeographical, and phylogenetic traits (maximum length, growth rate, life span, age at maturity, trophic level, habitat preference, geographical range size, taxonomy). The data focus on host features, because specific parasite traits are not consistently available across records. For this reason, the data set is intended as a flexible resource able to extend the principles of ecological niche modeling to the host–parasite system, providing researchers with the data to model parasite niches based on their distribution in host species and the associated host features. In this sense, the database offers a framework for testing general ecological, biogeographical, and phylogenetic hypotheses based on the identification of hosts as parasite habitat. Potential applications of the data set are, for example, the investigation of species–area relationships or the taxonomic distribution of host-specificity. The provided host–parasite list is that currently used by Fish Parasite Ecology Software Tool (FishPEST, http://purl.oclc.org/fishpest ), which is a website that allows researchers to model several aspects of the relationships between fish parasites and their hosts. The database is intended for researchers who wish to have more freedom to analyze the database than currently possible with FishPEST. However, for readers who have not seen FishPEST, we recommend using this as a starting point for interacting with the database.

Ecology↗
USGS2014

Bringing an ecological view of change to Landsat-based remote sensing

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Frontiers in Ecology and the Environment↗
USGS2005

Ecological science and sustainability for the 21st century

Ecological science has contributed greatly to our understanding of the natural world and the impact of humans on that world. Now, we need to refocus the discipline towards research that ensures a future in which natural systems and the humans they include coexist on a more sustainable planet. Acknowledging that managed ecosystems and intensive exploitation of resources define our future, ecologists must play a greatly expanded role in communicating their research and influencing policy and decisions that affect the environment. To accomplish this, they will have to forge partnerships at scales and in forms they have not traditionally used. These alliances must act within three visionary areas: enhancing the extent to which decisions are ecologically informed; advancing innovative ecological research directed at the sustainability of the planet; and stimulating cultural changes within the science itself, thereby building a forward-looking and international ecology. We recommend: (1) a research initiative to enhance research project development, facilitate large-scale experiments and data collection, and link science to solutions; (2) procedures that will improve interactions among researchers, managers, and decision makers; and (3) efforts to build public understanding of the links between ecosystem services and humans.

Frontiers in Ecology and the Environment↗
USGS2006

Ecological and sampling constraints on defining landscape fire severity

Ecological definition and detection of fire severity are influenced by factors of spatial resolution and timing. Resolution determines the aggregation of effects within a sampling unit or pixel (alpha variation), hence limiting the discernible ecological responses, and controlling the spatial patchiness of responses distributed throughout a burn (beta variation). As resolution decreases, alpha variation increases, extracting beta variation and complexity from the spatial model of the whole burn. Seasonal timing impacts the quality of radiometric data in terms of transmittance, sun angle, and potential contrast between responses within burns. Detection sensitivity candegrade toward the end of many fire seasons when low sun angles, vegetation senescence, incomplete burning, hazy conditions, or snow are common. Thus, a need exists to supersede many rapid response applications when remote sensing conditions improve. Lag timing, or timesince fire, notably shapes the ecological character of severity through first-order effects that only emerge with time after fire, including delayed survivorship and mortality. Survivorship diminishes the detected magnitude of severity, as burned vegetation remains viable and resprouts, though at first it may appear completely charred or consumed above ground. Conversely, delayed mortality increases the severity estimate when apparently healthy vegetation is in fact damaged by heat to the extent that it dies over time. Both responses dependon fire behavior and various species-specific adaptations to fire that are unique to the pre-firecomposition of each burned area. Both responses can lead initially to either over- or underestimating severity. Based on such implications, three sampling intervals for short-term burn severity are identified; rapid, initial, and extended assessment, sampled within about two weeks, two months, and depending on the ecotype, from three months to one year after fire, respectively. Spatial and temporal conditions of sampling strategies constrain data quality and ecological information obtained about fire severity. Though commonly overlooked, such considerations determine the objectives and hypotheses that are appropriate for each application, and are especially important when building comparative studies or long-term reference databases on fire severity.

Fire Ecology↗