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USGS2019

Grounding simulation models with qualitative case studies: Toward a holistic framework to make climate science usable for US public land management

Policies directing agencies and public land managers to incorporate climate change into management face several barriers. These stem, in part, from a disconnect between the information that is produced and the information needs of local resource managers. A disproportionate focus on the natural and physical sciences in climate vulnerability and adaptation assessment obscure understandings of complex social systems and the interactions and feedbacks in social-ecological systems. We use a qualitative case study of bison management on Department of the Interior-managed and tribal lands to explore how a social-science driven Determinants and Analogue Vulnerability Assessment (DAVA) can inform ecological response models, specifically simulation models that account for multiple drivers of change. First, we illustrate how a DAVA approach can help to: 1) identify key processes, entities, and interactions across scales; 2) document local impacts, indicators, and monitoring efforts of drought and climate; and 3) identify major tradeoffs and uncertainties. We then demonstrate how qualitative narratives can inform simulation models by: 1) prioritizing model components included in modeling efforts; 2) framing joint management and climate scenarios; and 3) parameterizing and evaluating model performance. We do this by presenting a conceptual joint agent-based/state-and-transition simulation modeling framework. Simulation models can represent multiple interacting variables and can identify surprising, emergent outcomes that might not be evident from qualitative analysis alone, and we argue that qualitative case studies can ground simulation models in local contexts and help make them more structurally realistic and useful. Together, these can provide a step toward developing actionable climate change adaptation strategies.

Climate Risk Management↗

USGS2021

Time marches on, but do the causal pathways driving instream habitat and biology remain consistent?

Stream ecosystems are complex networks of interacting terrestrial and aquatic drivers. To untangle these ecological networks, efforts evaluating the direct and indirect effects of landscape, climate, and instream predictors on biological condition through time are needed. We used structural equation modeling and leveraged a stream survey program to identify and compare important predictors driving condition of benthic macroinvertebrate and fish assemblages. We used data resampled 14 years apart at 252 locations across Maryland, USA. Sample locations covered a wide range of conditions that varied spatiotemporally. Overall, the relationship directions were consistent between sample periods, but their relative strength varied temporally. For benthic macroinvertebrates, we found that the total effect of natural landscape ( e.g ., elevation, longitude, latitude, geology) and land use ( i.e ., forest, development, agriculture) predictors was 1.4 and 1.5 times greater in the late 2010s compared to the 2000s. Moreover, the total effect of water quality ( e.g ., total nitrogen and conductivity) and habitat ( e.g ., embeddedness, riffle quality) was 1.2 and 4.8 times lower in the 2010s, respectively. For fish assemblage condition, the total effect of land use-land cover predictors was 2.3 times greater in the 2010s compared to the 2000s, while the total effect of local habitat was 1.4 times lower in the 2010s, respectively. As expected, we found biological assemblages in catchments with more agriculture and urban development were generally comprised of tolerant, generalist species, while assemblages in catchments with greater forest cover had more-specialized, less-tolerant species ( e.g., Ephemeroptera, Plecoptera, and Trichoptera taxa, clingers, benthic and lithophilic spawning fishes). Changes in the relative importance of landscape and land-use predictors suggest other correlated, yet unmeasured, proximal factors became more important over time. By untangling these ecological networks, stakeholders can gain a better understanding of the spatiotemporal relationships driving biological condition to implement management practices aimed at improving stream condition.

Maryland↗

USGS2023

The use of semiochemicals for attracting and repelling invasive ambrosia beetles (Coleoptera: Curculionidae) in ʻōhiʻa (Metrosideros polymorpha) forests

Early detection of invasive species is critical for preventing ecological and economic damage and maintaining ecosystem health. In Hawaiʻi, a complex of generalist ambrosia beetle species in the tribe Xyleborini (Coleoptera: Scolytinae) are threatening the health and productivity of forests and crops due to their association with tree diseases such as rapid ʻōhiʻa death (ROD) and key agricultural commodities including coffee and macadamia. We conducted trapping experiments to determine the efficacy of semiochemicals to attract and repel Xyleborini ambrosia beetles within two ʻōhiʻa ( Metrosideros polymorpha ) forests on the Island of Hawaiʻi. We compared the attraction of beetles to 100% ethanol and a 1:1 mixture of ethanol: methanol at Waiākea Forest Reserve and ʻŌlaʻa Forest in Hawaiʻi Volcanoes National Park. In addition, we tested the extent to which verbenone and verbenone + methyl salicylate repellents (SPLAT® Verb and SPLAT® Beetle Guard, respectively) deterred beetles from baited traps at Waiākea Forest Reserve in two separate experiments. For all invasive ambrosia beetle species, including Xyleborinus saxesenii , Xyleborus affinis , Xyleborus ferrugineus , Xyleborus perforans, Xylosandrus compactus , and Xylosandrus crassiusculus , more beetles were captured in traps baited with 100% ethanol than 1:1 ethanol: methanol. Across all species, both repellents were effective, with fewer beetles captured in traps equipped with repellents. Our research demonstrates the utility of semiochemicals for attracting and repelling invasive ambrosia beetle species in Hawaiʻi, and the potential use of these tools for early detection and management strategies.

Hawaii↗

USGS2009

USGS Water Data for Washington

The U.S. Geological Survey (USGS) has been investigating the water resources of Washington State since the latter part of the 19th century. During this time, demand for water has evolved from primarily domestic and stock needs to the current complex requirements for public-water supplies, irrigation, power generation, navigation, ecological needs, and numerous other uses. Water-resource data collected by the USGS in Washington have been, or soon will be, published by the USGS Washington Water Science Center (WAWSC) in numerous data and interpretive reports. Most of these reports are available online at the WAWSC web page http://wa.water.usgs.gov/pubs/

Fact Sheet↗

USGS2022

Hydrologic and environmental thresholds in stream fish assemblage structure across flow regimes

The characteristic pattern of variation in flow magnitude, frequency, duration, timing, and rate of change defines the flow regime of rivers and streams and is a key driver of ecosystem processes in fluvial ecosystems. Understanding how freshwater biotic assemblages change across gradients of hydrology and anthropogenic-source disturbance in different streamflow regimes is crucial to managing for sustainable environmental flows and watershed conservation. We compiled long-term (1916–2016) occurrence records for fishes collected in the Ouachita-Ozark Interior Highlands and West Gulf Coastal Plain streams, together with hydrologic metrics calculated from daily streamflow data measured at USGS stream gauging stations (n = 111), to examine important drivers and thresholds for fish assemblage turnover in groundwater (GW), runoff (RO), and intermittent (INT) flow regimes. We also examined the importance of spatial gradients (latitude, longitude, elevation, drainage area) and anthropogenic-source stressors (Hydrologic Disturbance Index; HDI) for fish assemblage turnover using a gradient forest modeling approach. Watershed fragmentation was of high importance for fish assemblage turnover in RO and INT streams, while changes in dam storage were more important for fishes in GW streams. Hydrologic metrics describing seasonal and stochastic properties of daily streamflow (Mag6) were most important for fish assemblage turnover in INT streams. Timing of high flow events had significantly higher importance compared to flow magnitude, duration, and frequency metrics, especially for fish assemblages in GW and INT streams. The frequency and timing of low flow events had high importance for fish assemblage turnover across all stream flow classes, while the magnitude of low flows and the magnitude and rate of change of average flows was most important for INT stream fish assemblages. In addition to benefiting multi-species conservation and management actions through identification of local and regional flow-ecology relationships generalized across different flow regimes, the results of this study provide a better understanding of complex nonlinear threshold effects, which is critical to anticipating changes in aquatic ecosystems and communities.

Arkansas, Missouri, Oklahoma↗

USGS2018

Functional group, biomass, and climate change effects on ecological drought in semiarid grasslands

Water relations in plant communities are influenced both by contrasting functional groups (grasses, shrubs) and by climate change via complex effects on interception, uptake and transpiration. We modelled the effects of functional group replacement and biomass increase, both of which can be outcomes of invasion and vegetation management, and climate change on ecological drought (soil water potential below which photosynthesis stops) in 340 semiarid grassland sites over 30‐year periods. Relative to control vegetation (climate and site‐determined mixes of functional groups), the frequency and duration of drought were increased by shrubs and decreased by annual grasses. The rankings of shrubs, control vegetation, and annual grasses in terms of drought effects were generally consistent in current and future climates, suggesting that current differences among functional groups on drought effects predict future differences. Climate change accompanied by experimentally‐increased biomass (i.e. the effects of invasions that increase community biomass, or management that increases productivity through fertilization or respite from grazing) increased drought frequency and duration, and advanced drought onset. Our results suggest that the replacement of perennial temperate semiarid grasslands by shrubs, or increased biomass, can increase ecological drought both in current and future climates.

Journal of Geophysical Research G: Biogeosciences↗

USGS2023

Obtaining and applying public data for training students in technical statistical writing: Case studies with data from U.S. Geological Survey and general ecological literature

Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area expertise beyond those of a general education or lack connections to real-world applications. Many governments, including the United States, now require the release of data from projects they directly complete or fund though grants and contracts. We show how statistical educators may find datasets and incorporate them into courses. Specifically, we use two examples from the U.S. Geological Survey (USGS) and one example from the ecology literature. We demonstrate the use of these datasets in an upper-level analysis of variance (ANOVA) class. In addition to describing how we found the datasets, we describe how to include them into course work and the course’s student assessments. We have used these datasets over multiple semesters and included student feedback from these courses. Although our examples focus on an ANOVA class, the general methods for finding data shared here could be used for statistical classes ranging from high school to graduate education. Supplementary materials for this article are available online.

Journal of Statistics and Data Science Education↗

USGS2018

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio‐economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user‐friendliness, and excellent user‐support. Forty‐one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user‐support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user‐friendly forms, increasing user‐support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Nonetheless, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.

Ecohydrology↗

USGS2019

Reverberating effects of resource exchanges in stream–riparian food webs

Fluxes of materials or organisms across ecological boundaries, often termed “resource subsidies,” directly affect recipient food webs. Few studies have addressed how such direct responses in one ecosystem may, in turn, influence the fluxes of materials or organisms to other habitats or the potential for feedback relationships to occur among ecosystems. As part of a large-scale, multi-year experiment, we evaluated the hypothesis that the input of a marine-derived subsidy results in a complex array of resource exchanges (i.e., inputs, outputs, feedbacks) between stream and riparian ecosystems as responses disperse across ecological boundaries. Moreover, we evaluated how the physical properties of resource subsidies mediated complex responses by contrasting carcasses with a pelletized salmon treatment. We found that salmon carcasses altered stream–riparian food webs by directly subsidizing multiple aquatic and terrestrial organisms (e.g., benthic insect larvae, fishes, and terrestrial flies). Such responses further influenced food webs along indirect pathways, some of which spanned land and water (e.g., subsidized fishes reduced aquatic insect emergence, with consequences for spiders and bats). Subsidy-mediated feedbacks manifested when carcasses were removed to riparian habitats where they were colonized by carrion flies, some of which fell into the stream and acted as another prey subsidy for fishes. As the effects of salmon subsidies propagated through the stream–riparian food web, the sign of consumer responses was not always positive and appeared to be determined by the outcome of trophic interactions, such that localized trophic interactions within one ecosystem mediated the export of organisms to others.

Idaho↗

USGS2020

Decision analysis for greater insights into the development and evaluation of Chinook salmon restoration strategies in California’s Central Valley

Considerable amounts of resources have been invested in ecological restoration projects across the globe to restore ecosystem integrity. Restoration strategies are often diverse and have been met with mixed success. In this paper, we describe the Chinook salmon (Oncorhynchus tshawytscha) decision-support models developed by the Central Valley Project Improvement Act Science Integration Team as part of a larger structured decision making effort aimed at maximizing natural adult production of Chinook salmon in California’s Central Valley, USA. We then describe the decision analytic tools the stakeholder group used to solve the models and explore model results, including stochastic dynamic programming, forward simulation, proportional scoring, relative loss, expected value of perfect information, response profile analyses, and indifference curves. Using these tools, the stakeholder group was able to develop and evaluate restoration strategies for multiple Chinook salmon runs simultaneously, a first for the restoration program. We found that actions targeted at one run were detrimental to others, which was unexpected. Furthermore, information uncovered during this process was used to direct efforts towards targeted research/monitoring to reduce critical uncertainties in salmon demographic rates and make better restoration decisions moving forward. The decision sciences have established a wide range of analytical tools and approaches to simplify complex problems into key components, and we believe the concepts described in this paper are of great interest and can be applied by many restoration practitioners that undoubtedly face similar difficulties when implementing restoration strategies for complex systems.

California↗

USGS2018

Looking beyond wildlife: Using remote cameras to evaluate accuracy of gridded snow data

The use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS ) gridded predictions using data from three winters (2014–2016). Snow depth observations were correlated with SNODAS predictions for sites ( R 2 = 0.20) and regions ( R 2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer‐dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km 2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species’ responses to climate change.

New Hampshire, Vermont↗

USGS2013

Biodiversity and disease: a synthesis of ecological perspectives on Lyme disease transmission.

Recent reviews have argued that disease control is among the ecosystem services yielded by biodiversity. Lyme disease (LD) is commonly cited as the best example of the ‘diluting’ effect of biodiversity on disease transmission, but many studies document the opposite relationship, showing that human LD risk can increase with forestation. Here, we unify these divergent perspectives and find strong evidence for a positive link between biodiversity and LD at broad spatial scales (urban to suburban to rural) and equivocal evidence for a negative link between biodiversity and LD at varying levels of biodiversity within forests. This finding suggests that, across zoonotic disease agents, the biodiversity–disease relationship is scale dependent and complex.

Trends in Ecology and Evolution↗

USGS2016

Bacterial genomics reveal the complex epidemiology of an emerging pathogen in arctic and boreal ungulates

Northern ecosystems are currently experiencing unprecedented ecological change, largely driven by a rapidly changing climate. Pathogen range expansion, and emergence and altered patterns of infectious disease, are increasingly reported in wildlife at high latitudes. Understanding the causes and consequences of shifting pathogen diversity and host-pathogen interactions in these ecosystems is important for wildlife conservation, and for indigenous populations that depend on wildlife. Among the key questions are whether disease events are associated with endemic or recently introduced pathogens, and whether emerging strains are spreading throughout the region. In this study, we used a phylogenomic approach to address these questions of pathogen endemicity and spread for Erysipelothrix rhusiopathiae , an opportunistic multi-host bacterial pathogen associated with recent mortalities in arctic and boreal ungulate populations in North America. We isolated E. rhusiopathiae from carcasses associated with large-scale die-offs of muskoxen in the Canadian Arctic Archipelago, and from contemporaneous mortality events and/or population declines among muskoxen in northwestern Alaska and caribou and moose in western Canada. Bacterial genomic diversity differed markedly among these locations; minimal divergence was present among isolates from muskoxen in the Canadian Arctic, while in caribou and moose populations, strains from highly divergent clades were isolated from the same location, or even from within a single carcass. These results indicate that mortalities among northern ungulates are not associated with a single emerging strain of E. rhusiopathiae , and that alternate hypotheses need to be explored. Our study illustrates the value and limitations of bacterial genomic data for discriminating between ecological hypotheses of disease emergence, and highlights the importance of studying emerging pathogens within the broader context of environmental and host factors.

Frontiers in Microbiology↗

USGS2025

A transdisciplinary approach to growing an applied science of cultural evolution for a sustainable future

Addressing sustainability challenges requires an integrative approach that bridges scientific research with practical application. The field of cultural evolution (CE) offers a perspective that may guide transitions and cultural transformations for a sustainable future. However, there have been few efforts to apply this field to sustainability challenges. This study explores how CE can inform sustainability practices through mechanisms of social learning, processes of cultural adaptation and conditions that promote cooperative governance. Through a two-phase research design—comprising online discussions with professionals from six domains, and an international workshop—we examined the perceived benefits, challenges and opportunities for applying CE in real-world contexts. Our findings indicate that professionals recognize CE’s potential to enhance knowledge dissemination, foster adaptation to social–ecological changes, improve governance structures and generate cooperation. However, barriers such as complex terminology, unfamiliarity with CE, the lack of clear, context-specific evidence of added value and competition with existing frameworks hinder its application. To overcome these challenges, we propose simplifying CE concepts, demonstrating its unique contributions, and continuing to foster co-production with practitioners to refine its applicability. This study is an initial step in building an applied science of CE that can support sustainability transformations across diverse domains.

Philosophical Transactions of the Royal Society B ↗

USGS2016

Logging impacts on avian species richness and composition differ across latitudes relative to foraging and breeding habitat preferences

Understanding the causes underlying changes in species diversity is a fundamental pursuit of ecology. Animal species richness and composition often change with decreased forest structural complexity associated with logging. Yet differences in latitude and forest type may strongly influence how species diversity responds to logging. We performed a meta-analysis of logging effects on local species richness and composition of birds across the world and assessed responses by different guilds (nesting strata, foraging strata, diet, and body size). This approach allowed identification of species attributes that might underlie responses to this anthropogenic disturbance. We only examined studies that allowed forests to regrow naturally following logging, and accounted for logging intensity, spatial extent, successional regrowth after logging, and the change in species composition expected due to random assembly from regional species pools. Selective logging in the tropics and clearcut logging in temperate latitudes caused loss of species from nearly all forest strata (ground to canopy), leading to substantial declines in species richness (up to 27% of species). Few species were lost or gained following any intensity of logging in lower-latitude temperate forests, but the relative abundances of these species changed substantially. Selective logging at higher-temperate latitudes generally replaced late-successional specialists with early-successional specialists, leading to no net changes in species richness but large changes in species composition. Removing less basal area during logging mitigated the loss of avian species from all forests and, in some cases, increased diversity in temperate forests. This meta-analysis provides insights into the important role of habitat specialization in determining differential responses of animal communities to logging across tropical and temperate latitudes.

Biological Reviews↗

USGS2010

Puget Sound shorelines and the impacts of armoring: Proceedings of a state of the science workshop, May 2009

The widespread extent and continued construction of seawalls and bulkheads on Puget Sound's beaches has emerged as a significant issue in shoreline management and coastal restoration in the region. Concerns about the impacts of shoreline armoring and managing the potential risks to coastal property are in many ways similar to those in other places, but Puget Sound also poses unique challenges related to its sheltered setting, glacially formed geology, rich estuarine ecology, and historical development pattern. The effects of armoring on shorelines are complex, involving both physical and biological science and requiring consideration of the cumulative impacts of small-scale activities over large scales of space and time. In addition, the issue is controversial, as it often places strongly held private interests in protecting shoreline property against broad public mandates to preserve shorelines for public uses and to protect environmental resources. Communities making difficult decisions about regulating shoreline activities and prioritizing restoration projects need to be informed by the best science available. To address these issues, a scientific workshop was convened in May 2009, specifically to bring local and national experts together to review the state of the science regarding the physical and biological impacts of armoring on sheltered shorelines such as those of Puget Sound.

Washington↗

USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗

USGS2017

Building translational ecology communities of practice: insights from the field

Translational ecology (TE) prioritizes the understanding of social systems and decision contexts in order to address complex natural resource management issues. Although many practitioners in applied fields employ translational tactics, the body of literature addressing such approaches is limited. We present several case studies illustrating the principles of TE and the diversity of its applications. We anticipate that these examples will help others develop scientific products that decision makers can use “off the shelf” when solving critical ecological and social challenges. Our collective experience suggests that research of such immediate utility is rare. Long‐term commitment to working directly with partners to develop and reach shared goals is central to successful translation. The examples discussed here highlight the benefits of translational processes, including actionable scientific results, more informed policy making, increased investment in science‐driven solutions, and inspiration for partnerships. We aim to facilitate future TE‐based projects and build momentum for growing this community of practice.

Frontiers in Ecology and the Environment↗