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USGS2010

Simultaneous modeling of habitat suitability, occupancy, and relative abundance: African elephants in Zimbabwe

The recent development of statistical models such as dynamic site occupancy models provides the opportunity to address fairly complex management and conservation problems with relatively simple models. However, surprisingly few empirical studies have simultaneously modeled habitat suitability and occupancy status of organisms over large landscapes for management purposes. Joint modeling of these components is particularly important in the context of management of wild populations, as it provides a more coherent framework to investigate the population dynamics of organisms in space and time for the application of management decision tools. We applied such an approach to the study of water hole use by African elephants in Hwange National Park, Zimbabwe. Here we show how such methodology may be implemented and derive estimates of annual transition probabilities among three dry-season states for water holes: (1) unsuitable state (dry water holes with no elephants); (2) suitable state (water hole with water) with low abundance of elephants; and (3) suitable state with high abundance of elephants. We found that annual rainfall and the number of neighboring water holes influenced the transition probabilities among these three states. Because of an increase in elephant densities in the park during the study period, we also found that transition probabilities from low abundance to high abundance states increased over time. The application of the joint habitat–occupancy models provides a coherent framework to examine how habitat suitability and factors that affect habitat suitability influence the distribution and abundance of organisms. We discuss how these simple models can further be used to apply structured decision-making tools in order to derive decisions that are optimal relative to specified management objectives. The modeling framework presented in this paper should be applicable to a wide range of existing data sets and should help to address important ecological, conservation, and management problems that deal with occupancy, relative abundance, and habitat suitability.

Hwange National Park↗

USGS2017

Recruitment phenology and pelagic larval duration in Caribbean amphidromous fishes

Amphidromous fishes are major components of oceanic tropical island stream ecosystems, such as those of the Caribbean island, Puerto Rico. Fishes with this life history face threats related to the requirement for connectivity between freshwater and marine environments during early life stages. Pelagic larval duration and recruitment phenology are 2 early life-history processes that are crucial for the biology, ecology, conservation, and management of amphidromous fishes. However, these processes are understudied in the Caribbean in general and have never been quantified in Puerto Rico. We quantified recruit abundance, recruitment phenology, and pelagic larval duration of several Caribbean amphidromous fish species in multiple rivers in Puerto Rico and explored the effects of environmental variables on recruit abundances. Two fish taxa—sirajo goby ( Sicydium spp.) and River Goby ( Awaous banana )—were exceptionally abundant as postlarvae and recruited to Caribbean rivers in pulsed migration episodes that were periodic at annual and lunar scales. Sirajo goby and River Goby recruit abundances varied among rivers, were greater at sunrise than at sunset, and were positively related to river discharge. The pelagic larval duration of 4 fish taxa ranged from a minimum of 28 d to a maximum of 103 d with means between 43 ± 7 d (SD) and 65 ± 11 d. We identified the last-quarter moon phase during the months of June through January as periods of maximum amphidromous fish recruitment to freshwater streams. The results and conclusions of our study can be applied to identify critical times to maintain river–ocean connectivity and stream flow for the benefit of the amphidromous fish population dynamics, stream ecology, and natural resources of the Caribbean.

Puerto Rico↗

USGS2017

Microhabitat and biology of Sphaerium striatinum in a central New York stream

In many lotic systems, drastic declines in freshwater bivalve populations, including fingernail clams (Sphaeriidae), have created concerns about biodiversity and future ecosystem services. We examined the local occurrence of the historically common fingernail clam, Sphaerium striatinum , in a central New York stream. We sampled the density of sphaeriids and measured the associated habitat variables (substrate, depth, water flow) to test within-stream multivariate benthic microhabitat association. Size distribution, density, and diel feeding periodicity were measured as focal aspects of fingernail clam biology and ecology. S. striatinum tended to be found in microhabitats that had harder substrates and faster flow. The Labrador Creek fingernail clam local population had positive indicators (size distribution, density). There was significant diel periodicity in feeding behavior. The clams fed most actively during the 0400–0800 h periods. This kind of behavioral periodicity can indicate a significant ecological interaction between predators and bivalve prey. Increased understanding of the behavioral ecology of small native freshwater bivalves in an unimpacted headwater stream is a fundamental building block for development of overall ecological conservation goals for freshwater bivalves and their lotic habitats.

New York↗

USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗

USGS2025

Same view through a different lens: Comparing population trends for North American birds using eBird and the Breeding Bird Survey

Confidently estimating population trends is of vital importance for a wide range of ecological, conservation, and management applications. North America has 2 major data sources for estimating population trends of breeding birds—the North American Breeding Bird Survey (BBS) and the global participatory science project eBird. Because the surveys differ in protocols, coverage, and data analysis, their trend estimates are expected to vary in magnitude, direction, and/or precision for at least some species and regions. Here, we compare independently derived estimates of population change between 2012 and 2022 for every combination of species and bird conservation region (BCR) covered by both surveys ( n = 5,577 combinations) as well as aggregated across entire ranges or within the U.S. or Canada. Uncertainty was substantial for both surveys, though more prevalent for BBS (81% of credibility intervals for estimates included zero) than eBird (34% of confidence intervals overlapped zero). We found agreement of trend directions between the 2 surveys. Only 1.3% of estimated trends were significant in opposite directions between the 2 surveys for all species/BCR combinations, with the median difference in trend magnitude being –0.02% (BBS minus eBird trend). Correlations between the 2 were strongest for estimates that were graded as being high credibility compared to estimates judged to have medium or low credibility. Both surveys were subject to species, taxonomic, and regional effects that influenced agreement. Overall, we show where trend estimates derived from BBS and eBird agree, explore where they diverge, present several comparisons to assist in interpreting results from both surveys, and inform efforts to integrate information from each.

Ornithological Applications↗

USGS2024

Weather events influence survival and recruitment of Coereba flaveola (Bananaquit) in the Caribbean

The West Indies is considered a biodiversity hotspot and a priority for ecological conservation efforts. Understanding how environmental conditions influence the survival of resident avifauna is an important information need given the predicted increases in drought and the frequency and intensity of severe storms in the region. Throughout much of the Caribbean, Coereba flaveola (Bananaquit) are widespread and abundant, traits that may facilitate understanding their response to environmental changes in the region. We used a 10-yr capture–mark–recapture data set to examine C. flaveola survival, recruitment, population growth, and age structure in the context of monthly and seasonal precipitation and temperatures, drought conditions, and occurrence and intensity of storm events. Our models suggested wing length, occurrence of storms, and drought all influenced survival. Both the incidence of storms and drier-than-average conditions in the preceding wet season (April–June) decreased survival. The sex of bird and net hours influenced capture probability, but weather influences on recruitment were equivocal. During nonstorm years, mean population growth was stable at 1.019 (95% confidence interval [CI]: 0.962, 1.098) but dropped to 0.843 (95% CI: 0.795, 0.846) in storm years. Increasing frequency of storms, such as back-to-back years, would likely push the growth rate lower. A protracted pattern of increased storm frequency, especially if coupled with a subsequent drought during the wet season, may lead to localized extirpations or strongly reduced populations.

British Virgin Islands, Guana Island↗

USGS2020

Changes in capture rates and body size among vertebrate species occupying an insular urban habitat reserve

Long‐term ecological monitoring provides valuable and objective scientific information to inform management and decision‐making. In this article, we analyze 22 years of herpetofauna monitoring data from the Point Loma Ecological Conservation Area (PLECA), an insular urban reserve near San Diego, CA. Our analysis showed that counts of individuals for one of the four most common terrestrial vertebrates declined, whereas counts for other common species increased or remained stable. Two species exhibited declines in adult body length, whereas biomass pooled over the five most common species increased over time and was associated with higher wet season precipitation. Although the habitat and vegetation at PLECA have remained protected and intact, we suspect that changes in arthropod communities may be driving changes in the abundance, growth, and development of insectivorous lizards. This study underscores the value of long‐term monitoring for establishing quantitative baselines to assess biological changes that would otherwise go undetected.

California↗

USGS2011

Characteristics of foraging sites and protein status in wintering muskoxen: insights from isotopes of nitrogen

Identifying links between nutritional condition of individuals and population trajectories greatly enhances our understanding of the ecology, conservation, and management of wildlife. For northern ungulates, the potential impacts of a changing climate to populations are predicted to be nutritionally mediated through an increase in the severity and variance in winter conditions. Foraging conditions and the availability of body protein as a store for reproduction in late winter may constrain productivity in northern ungulates, yet the link between characteristics of wintering habitats and protein status has not been established for a wild ungulate. We used a non‐invasive proxy of protein status derived from isotopes of N in excreta to evaluate the influence of winter habitats on the protein status of muskoxen in three populations in Alaska (2005–2008). Multiple regression and an information‐theoretic approach were used to compare models that evaluated the influence of population, year, and characteristics of foraging sites (components of diet and physiography) on protein status for groups of muskoxen. The observed variance in protein status among groups of muskoxen across populations and years was partially explained (45%) by local foraging conditions that affected forage availability. Protein status improved for groups of muskoxen as the amount of graminoids in the diet increased (−0.430 ± 0.31, β± 95% CI) and elevation of foraging sites decreased (0.824 ± 0.67). Resources available for reproduction in muskoxen are highly dependent upon demographic, environmental, and physiographic constraints that affect forage availability in winter. Due to their very sedentary nature in winter, muskoxen are highly susceptible to localized foraging conditions; therefore, the spatial variance in resource availability may exert a strong effect on productivity. Consequently, there is a clear need to account for climate–topography effects in winter at multiple scales when predicting the potential impacts of climatic shifts on population trajectories of muskoxen.

Oikos↗

USGS2022

INHABIT: A web-based decision support tool for invasive plant species habitat visualization and assessment across the contiguous United States

Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT’s credibility, utility, and relevance.

PLoSOne↗

USGS2015

Groundwater and surface-water interaction and effects of pumping in a complex glacial-sediment aquifer, phase 2, east-central Massachusetts

The U.S. Geological Survey, in cooperation with the Town of Framingham, Massachusetts, has investigated the potential of proposed groundwater withdrawals at the Birch Road well site to affect nearby surface water bodies and wetlands, including Lake Cochituate, the Sudbury River, and the Great Meadows National Wildlife Refuge in east-central Massachusetts. In 2012, the U.S. Geological Survey developed a Phase 1 numerical groundwater model of a complex glacial-sediment aquifer to synthesize hydrogeologic information and simulate potential future pumping scenarios. The model was developed with MODFLOW-NWT, an updated version of a standard USGS numerical groundwater flow modeling program that improves solution of unconfined groundwater flow problems. The groundwater model and investigations of the aquifer improved understanding of groundwater–surface-water interaction and the effects of groundwater withdrawals on surface-water bodies and wetlands in the study area. The initial work also revealed a need for additional information and model refinements to better understand this complex aquifer system. In this second phase of the study, the original groundwater flow model was revised to improve representation of groundwater and surface-water hydrology, stabilize the model, and reduce model error. The model was simplified by reducing the number of layers from 5 to 3 and adding the MODFLOW lake package (LAK) to simulate Lake Cochituate and Pod Meadow Pond and better represent interaction between the lakes and the aquifer. Model revisions improved stability and shortened run times, allowing use of automated parameter estimation software (PEST) to further refine the model hydraulic parameters and reduce simulation errors. Model simulations indicate that under average base-flow conditions, the Birch Road wells have a small effect on flow in the Sudbury River during most months, even at the maximum pumping rate of 4.9 ft 3 /s (3.17 Mgal/d). Maximum percent streamflow depletion in the Sudbury River caused by simulated pumping takes place during simulated drought conditions, when streamflow decreased by as much as 21 percent under maximum continuous pumping. Simulations also indicate that groundwater withdrawals at the Birch Road site could be managed so that adverse streamflow impacts are substantially ameliorated. Under the most ecologically conservative simulated drought conditions, simulated streamflow depletion was reduced from 21 percent to 3 percent by pumping at the maximum rate for 6 months rather than for 12 months. Simulations that return 10 percent of the Birch Road well withdrawals to Pod Meadow Pond indicate a modest reduction in the Sudbury River streamflow depletion and provide a larger percentage increase to streamflow just downstream of the pond. The groundwater model also indicates that well locations can have a large effect on the sustainable pumping rate and so should be chosen carefully. The model provides a tool for evaluating alternative pumping rates and schedules not included in this analysis.

Massachusetts↗

USGS2024

Rowing in the same direction using MIX— A tool to initiate the melding of individual disciplinary experts into an integrated interdisciplinary team

A common problem for interdisciplinary sustainability research is that scientists trained in different disciplines are often not rowing their boat effectively in the same direction. Sustainability tools can aid the implementation of this team-melding process. Here, our purpose is to illustrate our M ulti-step I ntegrated graphical and structured discussion e X ercise ( MIX ) tool that transforms diverse disciplinary experts into an interdisciplinary team. We use a visual puzzle-solving approach based on the blind men and the elephant metaphor (BMEM) because this story illustrates the shortcomings of siloed viewpoints and the need to integrate multiple perspectives. Our six-step MIX tool provides step-specific objectives, group activities, discussion questions, and learning outcomes. Activities promote experiential learning for team problem solving. The step-specific structured discussions are designed to get each individual to change their focus from their own discipline (i.e., an elephant trunk, tail, leg, or other isolated pieces of the whole animal) to the team’s interdisciplinary goal (i.e., the whole elephant or the entire multi-faceted problem). In our example proof of concept, we show that a narrow focus on only economic yield (trunk), ecological conservation (legs), or human values (tail) misrepresents the biologically involved sustainability problem (elephant) and blocks innovative solutions.

Sustainability↗

USGS2026

Investigating hydrologic alteration under historical and future scenarios in the Mobile River and Perdido River basins using the cubist algorithm

This study investigates the impacts of human activities and climate variability on hydrologic alterations in the Mobile River and Perdido River Basins of Alabama. The research uses a machine learning approach, specifically cubist models, to quantify and predict changes in flow duration curves (FDCs) under both historical (1980–2009) and future climate scenarios. Future climate projections include the Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and the Shared Socioeconomic Pathways (SSP2 4.5 and SSP5 8.5), evaluated for two future periods: 1980–2069 and 1980–2099. The models incorporate a wide range of covariates, including basin geomorphology, aquifer characteristics, land cover, water storage, environmental factors, solar radiation, census data, and water use data. Under the baseline period (1980–2009), most level 12 hydrologic unit codes (HUC12s) in both basins showed alterations, with substantial differences observed between pre- and post-alteration FDCs. The model performance varied, with a Nash–Sutcliffe Efficiency between 0.91 and 0.95 for testing and between 0.98 and 0.99 for training during the baseline period. Future projections under the RCP 4.5 and RCP 8.5 scenarios generally differed significantly from baseline conditions across all flow regimes ( p < 0.05). In contrast, SSP2 4.5 showed comparatively limited statistical significance, while SSP5 8.5 exhibited significant departures from baseline conditions across all flow regimes, reflecting the greater influence of high-emissions climate forcing on projected hydrologic alterations. Overall, the RCP scenarios projected more widespread statistically significant changes than the corresponding SSP scenarios at the same forcing level, particularly when comparing RCP4.5 with SSP2-4.5, while both RCP8.5 and SSP5-8.5 consistently indicated greater hydrologic alterations than their moderate-emissions counterparts. These findings highlight the importance of considering different flow regimes when assessing the impacts of climate variability on streamflow. This study contributes to the understanding of hydrologic alterations in the Mobile River and Perdido River Basins, providing insights for water resource management and ecological conservation efforts in the region.

Alabama↗

USGS2024

Seroprevalence, blood chemistry, and patterns of canine parvovirus, distemper virus, plague, and tularemia in free-ranging coyotes (Canis latrans) in northern New Mexico, USA.

Wildlife diseases have implications for ecology, conservation, human health, and health of domestic animals. They may impact wildlife health and population dynamics. Exposure rates of coyotes ( Canis latrans ) to pathogens such as Yersinia pestis , the cause of plague, may reflect prevalence rates in both rodent prey and human populations. We captured coyotes in north-central New Mexico during 2005–2008 and collected blood samples for serologic surveys. We tested for antibodies against canine distemper virus (CDV, Canine morbillivirus ), canine parvovirus (CPV, Carnivore protoparvovirus ), plague, tularemia ( Francisella tularensis ), and for canine heartworm ( Dirofilaria immitis ) antigen. Serum biochemistry variables that fell outside reference ranges were probably related to capture stress. We detected antibodies to parvovirus in 32/32 samples (100%), and to Y. pestis in 26/31 (84%). More than half 19/32 (59%) had antibodies against CDV, and 5/31 (39%) had antibodies against F. tularensis . We did not detect any heartworm antigens ( n = 9). Pathogen prevalence was similar between sexes and among the three coyote packs in the study area. Parvovirus exposure appeared to happen early in life, and prevalence of antibodies against CDV increased with increasing age class. Exposure to Y. pestis and F. tularensis occurred across all age classes. The high coyote seroprevalence rates observed for CPV, Y. pestis , and CDV may indicate high prevalence in sympatric vertebrate populations, with implications for regional wildlife conservation as well as risk to humans via zoonotic transmission.

New Mexico↗

USGS2013

Spatial capture-recapture

Spatial Capture-Recapture provides a revolutionary extension of traditional capture-recapture methods for studying animal populations using data from live trapping, camera trapping, DNA sampling, acoustic sampling, and related field methods. This book is a conceptual and methodological synthesis of spatial capture-recapture modeling. As a comprehensive how-to manual, this reference contains detailed examples of a wide range of relevant spatial capture-recapture models for inference about population size and spatial and temporal variation in demographic parameters. Practicing field biologists studying animal populations will find this book to be a useful resource, as will graduate students and professionals in ecology, conservation biology, and fisheries and wildlife management.

Book↗

USGS2012

Assessing the vulnerability of human and biological communities to changing ecosystem services using a GIS-based multi-criteria decision support tool

In this paper we describe an application of a GIS-based multi-criteria decision support web tool that models and evaluates relative changes in ecosystem services to policy and land management decisions. The Santa Cruz Watershed Ecosystem Portfolio (SCWEPM) was designed to provide credible forecasts of responses to ecosystem drivers and stressors and to illustrate the role of land use decisions on spatial and temporal distributions of ecosystem services within a binational (U.S. and Mexico) watershed. We present two SCWEPM sub-models that when analyzed together address bidirectional relationships between social and ecological vulnerability and ecosystem services. The first model employs the Modified Socio-Environmental Vulnerability Index (M-SEVI), which assesses community vulnerability using information from U.S. and Mexico censuses on education, access to resources, migratory status, housing situation, and number of dependents. The second, relating land cover change to biodiversity (provisioning services), models changes in the distribution of terrestrial vertebrate habitat based on multitemporal vegetation and land cover maps, wildlife habitat relationships, and changes in land use/land cover patterns. When assessed concurrently, the models exposed some unexpected relationships between vulnerable communities and ecosystem services provisioning. For instance, the most species-rich habitat type in the watershed, Desert Riparian Forest, increased over time in areas occupied by the most vulnerable populations and declined in areas with less vulnerable populations. This type of information can be used to identify ecological conservation and restoration targets that enhance the livelihoods of people in vulnerable communities and promote biodiversity and ecosystem health.

Santa Cruz watershed↗

USGS2017

Quantifying the relative contribution of an ecological reserve to conservation objectives

Evaluating the role public lands play in meeting conservation goals is an essential step in good governance. We present a tool for comparing the regional contribution of each of a suite of wildlife management units to conservation goals. We use weighted summation ( simple additive weighting ) to compute a Unit Contribution Index ( UCI ) based on species richness, population abundance, and a conservation score based on IUCN Red List classified threat levels. We evaluate UCI for a subset of the 729 participating wetlands of the Integrated Waterbird Management and Monitoring (IWMM) Program across U.S. Fish and Wildlife Service Regions 3 (Midwest USA), 4 (Southeast USA), and 5 (Northeast USA). We found that the median across-Region UCI for Region 5 was greater than Regions 3 and 4, while Region 4 had the greatest within-Region UCI median. This index is a powerful tool for wildlife managers to evaluate the performance of units within the conservation estate.

Global Ecology and Conservation↗

USGS2021

Hawai‘i forest review: Synthesizing the ecology, evolution, and conservation of a model system

As the most remote archipelago in the world, the Hawaiian Islands are home to a highly endemic and disharmonic biota that has fascinated biologists for centuries. Forests are the dominant terrestrial biome in Hawai‘i, spanning complex, heterogeneous climates across substrates that vary tremendously in age, soil structure, and nutrient availability. Species richness is low in Hawaiian forests compared to other tropical forests, as a consequence of dispersal limitation from continents and adaptive radiations in only some lineages, and forests are dominated by the widespread Metrosideros species complex. Low species richness provides a relatively tractable model system for studies of community assembly, local adaptation, and species interactions. Moreover, Hawaiian forests provide insights into predicted patterns of evolution on islands, revealing that while some evidence supports “island syndromes,” there are exceptions to them all. For example, Hawaiian plants are not as a whole less defended against herbivores, less dispersible, more conservative in resource use, or more slow-growing than their continental relatives. Clearly, more work is needed to understand the drivers, sources, and constraints on phenotypic variation among Hawaiian species, including both widespread and rare species, and to understand the role of this variation for ecological and evolutionary processes, which will further contribute to conservation of this unique biota. Today, Hawaiian forests are among the most threatened globally. Resource management failures – the proliferation of non-native species in particular – have led to devastating declines in native taxa and resulted in dominance by novel species assemblages. Conservation and restoration of Hawaiian forests now rely on managing threats including climate change, ongoing species introductions, novel pathogens, lost mutualists, and altered ecosystem dynamics through the use of diverse tools and strategies grounded in basic ecological, evolutionary, and biocultural principles. The future of Hawaiian forests thus depends on the synthesis of ecological and evolutionary research, which will continue to inform future conservation and restoration practices.

Hawaii↗

USGS2025

Bright spots for advancing ecological understanding and conservation decision-making

A lot can be learned by studying bright spots—defined as unexpected positive outcomes. In fields like public health, education, and oncology, identifying factors behind bright spots reveals previously unknown drivers of success that can be replicated elsewhere. This concept is being applied in conservation but is hampered by variations in definitions of bright spots and confusion with hotspots—sites with high absolute values of a metric. We developed a framework to clearly define and distinguish between hotspots (e.g., a wetland with high plant diversity) and bright spots (e.g., a biodiverse wetland in a housing development), which outperform conservation expectations. The framework is an iterative cycle, consisting of setting expectations for relative comparisons, classifying systems into bright, dark, hot, and cold categories, and digging deeper to reveal hidden mechanisms and opportunities for intervention. We drew on examples from diverse fields to demonstrate how our framework can generate new knowledge, identify potential interventions, and inform management priorities. Defining conservation and management expectations, often through predictive models, is essential to understanding drivers of success and fosters hypotheses about overlooked factors. Our framework can enhance ecological understanding, guide interventions, and help prioritize actions in conservation and natural resource management.

Conservation Biology↗