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At least 757 records · Page 42Linked to original sources
USGS2008

A test of the cross-scale resilience model: Functional richness in Mediterranean-climate ecosystems

Ecological resilience has been proposed to be generated, in part, in the discontinuous structure of complex systems. Environmental discontinuities are reflected in discontinuous, aggregated animal body mass distributions. Diversity of functional groups within body mass aggregations (scales) and redundancy of functional groups across body mass aggregations (scales) has been proposed to increase resilience. We evaluate that proposition by analyzing mammalian and avian communities of Mediterranean-climate ecosystems. We first determined that body mass distributions for each animal community were discontinuous. We then calculated the variance in richness of function across aggregations in each community, and compared observed values with distributions created by 1000 simulations using a null of random distribution of function, with the same n, number of discontinuities and number of functional groups as the observed data. Variance in the richness of functional groups across scales was significantly lower in real communities than in simulations in eight of nine sites. The distribution of function across body mass aggregations in the animal communities we analyzed was non-random, and supports the contentions of the cross-scale resilience model. ?? 2007 Elsevier B.V. All rights reserved.

Ecological Complexity↗
USGS2017

Skagit River coho salmon life history model—Users’ guide

Natural resource management is conducted in the context of multiple anthropogenic stressors and is further challenged owing to changing climate. Experiments to determine the effects of climate change on complex ecological systems are nearly impossible. However, using a simulation model to synthesize current understanding of key ecological processes through the life cycle of a fish population can provide a platform for exploring potential effects of and management responses to changing conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions likely deviating in range and timing from conditions used to create the model so that the model is expected to become obsolete. In the meantime, however, the modeling process explicitly states assumptions, clarifies information gaps, and provides a means to better understand which relationships are robust and which are vulnerable to changing climate by observing whether and why model output diverges from actual observations through time. The purpose of the model described herein is to provide such a decision-support tool regarding coho ( Oncorhynchus kisutch ) salmon for the Sauk-Suiattle Indian Tribe of Washington State. The Skagit coho salmon model is implemented in a system dynamics format and has three primary stocks—(1) predicted smolts, (2) realized smolts, and (3) escapement. “Predicted smolts” are the number of smolts expected based on the number of spawners in any year and the Ricker production curve. Pink salmon ( Oncorhynchus gorbuscha ) return to the Skagit River in odd years, and when they overlap with juvenile rearing coho salmon, coho smolt production is substantially higher than in non-pink years. Therefore, the model uses alternative Ricker equations to predict smolts depending on whether their juvenile year was a pink or non-pink year. The stock “realized smolts” is calculated based on the expected effect of streamflow conditions to alter the productivity predicted by the Ricker curve. Adverse conditions include scouring flow events that occur when redds are present; high-flow events during winter on juveniles, which can cause fish displacement and adverse water turbidity; and extremely low flows in summer. The stock “escapement” represents the fish remaining after accounting for ocean mortality and harvest. Ocean mortality has been linked with indices of ocean conditions, which are related to ocean biological productivity. Ocean survival also may have a density-dependent component such that lower survival is associated with higher numbers of smolts. The model allows the user to change certain model parameters and inputs, and choose among alternative predictors for certain modeled relations.

Washington↗
USGS2018

A review of Bayesian belief network models as decision-support tools for wetland conservation: Are water birds potential umbrella taxa?

Creative approaches to identifying umbrella species hold promise for devising effective surrogates of ecological communities or ecosystems. However, mechanistic niche models that predict range or habitat overlap among species may yet lack development. We reviewed literature on taxon-centered Bayesian belief network (BBN) models to explore a novel approach to identify umbrella taxa identifying taxonomic groups that share the largest proportion of habitat requirements (i.e., states of important habitat variables) with other wetland-dependent taxa. We reviewed and compiled published literature to provide a comprehensive and reproducible account of the current understanding of habitat requirements for freshwater, wetland-dependent taxa using BBNs. We found that wetland birds had the highest degree of shared habitat requirements with other taxa, and consequently may be suitable umbrella taxa in freshwater wetlands. Comparing habitat requirements using a BBN approach to build species distribution models, this review also identified taxa that may not benefit from conservation actions targeted at umbrella taxa by identifying taxa with unique habitat requirements not shared with umbrellas. Using a standard node set that accurately and comprehensively represents the ecosystem in question, BBNs could be designed to improve identification of umbrella taxa. In wetlands, expert knowledge about hydrology, geomorphology and soils could add important information regarding physical landscape characteristics relevant to species. Thus, a systems-oriented framework may improve overarching inferences from BBNs and subsequent utility to conservation planning and management.

Biological Conservation↗
USGS2017

Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling

Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why ); (2) the Process by which the public was involved in model building or evaluation (the how ); (3) the Partnerships formed (the who ); and (4) the Products that resulted from these efforts (the what ). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human–environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.

Ecological Applications↗
USGS2022

Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments

Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.

Idaho, Montana, Nebraska, North Dakota, South Dako↗
USGS2011

Semiparametric bivariate zero-inflated Poisson models with application to studies of abundance for multiple species

Ecological studies involving counts of abundance, presence–absence or occupancy rates often produce data having a substantial proportion of zeros. Furthermore, these types of processes are typically multivariate and only adequately described by complex nonlinear relationships involving externally measured covariates. Ignoring these aspects of the data and implementing standard approaches can lead to models that fail to provide adequate scientific understanding of the underlying ecological processes, possibly resulting in a loss of inferential power. One method of dealing with data having excess zeros is to consider the class of univariate zero-inflated generalized linear models. However, this class of models fails to address the multivariate and nonlinear aspects associated with the data usually encountered in practice. Therefore, we propose a semiparametric bivariate zero-inflated Poisson model that takes into account both of these data attributes. The general modeling framework is hierarchical Bayes and is suitable for a broad range of applications. We demonstrate the effectiveness of our model through a motivating example on modeling catch per unit area for multiple species using data from the Missouri River Benthic Fishes Study, implemented by the United States Geological Survey.

Environmetrics↗
USGS2022

Balancing model generality and specificity in management-focused habitat selection models for Gunnison sage-grouse

Identifying, protecting, and restoring habitats for declining wildlife populations is foundational to conservation and recovery planning for any species at risk of decline. Resource selection analysis is a key tool to assess habitat and prescribe management actions. Yet, it can be challenging to map suitable resource conditions across a wide range of ecological contexts and use the resulting models to identify effective and universal habitat improvement actions. We developed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses that form the basis of location-specific management prescriptions. To demonstrate this approach, we developed a set of habitat selection models for Gunnison sage-grouse ( Centrocercus minimus ), a threatened species under the U.S. Endangered Species Act. Conservation, species recovery, and habitat management efforts are needed in six isolated satellite populations (San Miguel, Crawford, Piñon Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) where environmental conditions differ, and the already small number of birds are declining. We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. All models included key habitat variables often altered through management actions to assess their differential influences across models. We found important similarities and differences among satellites, indicating that, although some rules of thumb are generally well-grounded, the consideration of population-specific environmental differences could increase the efficiency of local habitat improvement actions. Sage-grouse also had diverse responses to resource conditions at different scales, indicating that regional spatial (e.g., landscape) and local patch scale can differentially influence expected habitat improvements associated with where such management actions are implemented. Although context variables such as topography cannot be manipulated, sage-grouse associations revealed information that could guide the siting of improvement actions. This approach to balancing management objectives associated with habitat assessment may benefit spatially-structured populations with different environmental contexts and species with complex habitat needs and associations.

Colorado↗
USGS2016

Managing water and riparian habitats on the Bill Williams River with scientific benefit for other desert river systems

This report details modeling to: 1) codify flow-ecology relationships for riparian species of the Bill Williams River as operational guidance for water managers, 2) test the guidance under different climate scenarios, and 3) revise the operational guidance as needed to address the effects of climate change. Model applications detailed herein include the River Analysis System (HEC-RAS) and the Ecosystem Functions Model (HEC-EFM), which was used to generate more than three million estimates of local seedling recruitment areas. Areas were aggregated and compared to determine which scenarios generated the most seedling area per unit volume of water. Scenarios that maximized seedling area were grouped into a family of curves that serve as guidance for water managers. This work has direct connections to water management decision-making and builds upon and adds to the rich history of science-based management for the Bill Williams River, Arizona, USA.

Arizona↗
USGS2005

Multiple hypotheses testing of fish incidence patterns in an urbanized ecosystem

Ecological and evolutionary theories have focused traditionally on natural processes with little attempt to incorporate anthropogenic influences despite the fact that humans are such an integral part of virtually all ecosystems. A series of alternate models that incorporated anthropogenic factors and traditional ecological mechanisms of invasion to account for fish incidence patterns in urban lakes was tested. The models were based on fish biology, human intervention, and habitat characteristics. However, the only models to account for empirical patterns were those that included fish invasiveness, which incorporated species-specific information about overall tolerance and fecundity. This suggests that species-specific characteristics are more important in general distributional patterns than human-mediated dispersal. Better information of illegal stocking activities is needed to improve human-mediated models, and more insight into basic life history of ubiquitous species is needed to truly understand underlying mechanisms of biotic homogenization. ?? Springer 2005.

Aquatic Ecology↗
USGS2022

A generically parameterized model of Lake eutrophication: The impact of Stoichiometric ratios and constraints on the abundance of natural phytoplankton communities (GPLake-S)

Water quality improvement to avoid excessive phytoplankton blooms often requires eutrophication management where both phosphorus (P) and nitrogen (N) play a role. While empirical eutrophication studies and ecological resource competition theory both provide insight into phytoplankton abundance in response to nutrient loading, they are not seamlessly linked in the current state of eutrophication research. We argue that understanding species competition for multiple nutrients and light in natural phytoplankton communities is key to assessing phytoplankton abundance under changing nutrient supply. Here we present GPLake-S, a mechanistic model rooted in ecological resource competition theory, which has only eight parameters and can predict chlorophyll- a to nutrient relationships for phytoplankton communities under N, P, N+P colimitation and light limitation. GPLake-S offers a simple mechanistic tool to make first estimates of chlorophyll-a levels and nutrient thresholds for generic lake properties, accounting for variation in N:P ratio preferences of phytoplankton species. This makes the model supportive of water management and policy.

Ecological Modelling↗
USGS2026

Nonstationary demographic state-space models using unreplicated counts for species undergoing environmental stressors

A fundamental task in ecological statistics is to estimate abundance and growth rate distributions from wildlife monitoring data to inform conservation management. Modeling time series of wildlife populations presents a number of challenges from both statistical and ecological perspectives, including discreteness; lack of replication; nonstationarity; and observation, demographic, and other phenomenological processes. Nonstationary dynamics are often exhibited by populations undergoing environmental stressors. Models must account for these characteristics to produce reliable estimates of abundance and trends, yet estimation can be challenging with unreplicated data. We propose nonstationary demographic state-space models using unreplicated counts for populations undergoing environmental stressors. A reduced growth rate model matches the complexity of the unreplicated count data, and a fecundity bound on growth rate distributions allows the separation of processes affecting growth rates like environmental stressors from those affecting abundance external to growth rates like migration. NDSSMs allow for the embedding of nonstationary model components, and we explore the use of changepoints, volatility clustering, and migration processes. We apply the proposed nonstationary models in case studies of herons affected by predator/competitor reestablishment and three bat species affected by a fungal pathogen causing white-nose syndrome. Nonstationary models outperform stationary models and generalized linear mixed effects models according to model scoring and visual inspection of predictions, and provide estimates more consistent with published values. Incorporating migration improves model fit universally, even with approximate one-way immigration, most likely because populations are extirpated, recolonized, and increase multiple-fold over the upper bound set by species fecundity. In addition, estimates of the timing and severity of the environmental stressor differed for models with migration. Including nonstationary and demographic components in a fecundity-bounded growth rate model improves inference and benefits interpretability of hyperparameters. In turn, this adjusts uncertainties in predictions of abundance and growth rates over time, providing the ingredients needed for informed conservation analysis and for directing future monitoring of at-risk species.

Journal of Agricultural, Biological and Environmen↗
USGS2016

Generalizing ecological site concepts of the Colorado Plateau for landscape-level applications

Numerous ecological site descriptions in the southern Utah portion of the Colorado Plateau can be difficult to navigate, so we held a workshop aimed at adding value and functionality to the current ecological site system. We created new groups of ecological sites and drafted state-and-transition models for these new groups. We were able to distill the current large number of ecological sites in the study area (ca. 150) into eight ecological site groups that capture important variability in ecosystem dynamics. Several inventory and monitoring programs and landscape scale planning actions will likely benefit from more generalized ecological site group concepts.

Rangelands↗
USGS2017

Computer modelling for ecosystem service assessment

Computer models are simplified representations of the environment that allow biophysical, ecological, and/or socio-economic characteristics to be quantified and explored. Modelling approaches differ from mapping approaches (Chapter 5) as (i) they are not forcibly spatial (although many models do produce spatial outputs); (ii) they focus on understanding and quantifying the interactions between different components of social and/or environmental systems and (iii) by changing parameters within models, they are capable of exploring both alternative scenarios and internal model dynamics. When applied to the assessment of ecosystem services (ES), models are important tools which can quantify the relationships that underpin ES supply, demand and flows and, in some cases, produce maps representing these factors. Furthermore, as models can explore scenarios, trade-offs that result from different scenarios can be assessed. This chapter provides a broad overview of different types of models that have been applied to ES assessments and discusses, with examples, the ways that these models have the potential to be used in practice. In the context of ES, there are a number of ways of distinguishing between different types of models. Here, we distinguish between individual models focussing on single ES and modelling frameworks that can assess multiple ES within the framework of a single modelling tool.

Book chapter↗
USGS2017

Water-quality trends in the nation’s rivers and streams, 1972–2012—Data preparation, statistical methods, and trend results

Since passage of the Clean Water Act in 1972, Federal, State, and local governments have invested billions of dollars to reduce pollution entering rivers and streams. To understand the return on these investments and to effectively manage and protect the Nation’s water resources in the future, we need to know how and why water quality has been changing over time. As part of the National Water-Quality Assessment Project, of the U.S. Geological Survey’s National Water-Quality Program, data from the U.S. Geological Survey, along with multiple other Federal, State, Tribal, regional, and local agencies, have been used to support the most comprehensive assessment conducted to date of surface-water-quality trends in the United States. This report documents the methods used to determine trends in water quality and ecology because these methods are vital to ensuring the quality of the results. Specific objectives are to document (1) the data compilation and processing steps used to identify river and stream sites throughout the Nation suitable for water-quality, pesticide, and ecology trend analysis, (2) the statistical methods used to determine trends in target parameters, (3) considerations for water-quality, pesticide, and ecology data and streamflow data when modeling trends, (4) sensitivity analyses for selecting data and interpreting trend results with the Weighted Regressions on Time, Discharge, and Season method, and (5) the final trend results at each site. The scope of this study includes trends in water-quality concentrations and loads (nutrient, sediment, major ion, salinity, and carbon), pesticide concentrations and loads, and metrics for aquatic ecology (fish, invertebrates, and algae) for four time periods: (1) 1972–2012, (2) 1982–2012, (3) 1992–2012, and (4) 2002–12. In total, nearly 12,000 trends in concentration, load, and ecology metrics were evaluated in this study; there were 11,893 combinations of sites, parameters, and trend periods. The final trend results are presented with examples of how to interpret the results from each trend model. Interpretation of the trend results, such as causal analysis, is not included.

Scientific Investigations Report↗
USGS2014

Predicting connectivity of green turtles at Palmyra Atoll, central Pacific: a focus on mtDNA and dispersal modelling

Population connectivity and spatial distribution are fundamentally related to ecology, evolution and behaviour. Here, we combined powerful genetic analysis with simulations of particle dispersal in a high-resolution ocean circulation model to investigate the distribution of green turtles foraging at the remote Palmyra Atoll National Wildlife Refuge, central Pacific. We analysed mitochondrial sequences from turtles ( n = 349) collected there over 5 years (2008–2012). Genetic analysis assigned natal origins almost exclusively (approx. 97%) to the West Central and South Central Pacific combined Regional Management Units. Further, our modelling results indicated that turtles could potentially drift from rookeries to Palmyra Atoll via surface currents along a near-Equatorial swathe traversing the Pacific. Comparing findings from genetics and modelling highlighted the complex impacts of ocean currents and behaviour on natal origins. Although the Palmyra feeding ground was highly differentiated genetically from others in the Indo-Pacific, there was no significant differentiation among years, sexes or stage-classes at the Refuge. Understanding the distribution of this foraging population advances knowledge of green turtles and contributes to effective conservation planning for this threatened species.

Journal of the Royal Society Interface↗
USGS2019

Identifying occupancy model inadequacies: Can residuals separately assess detection and presence?

Occupancy models are widely applied to estimate species distributions, but few methods exist for model checking. Thorough model assessments can uncover inadequacies and allow for deeper ecological insight by exploring structure in the observed data not accounted for by a model. We introduce occupancy model residual definitions that utilize the posterior distribution of the partially latent occupancy states. Residual‐based assessments are valuable because they can target specific assumptions and identify ways to improve a model, such as adding spatial correlation or meaningful covariates. Our approach defines separate residuals for occupancy and detection, and we use simulation to examine whether missing structure for modeling detection probabilities can be distinguished from that for occupancy probabilities. In many scenarios, our residual diagnostics were able to successfully separate inadequacies at the different model levels, but we describe other situations when this may not be the case. Applying Moran's I residual diagnostics to assess models for silver‐haired (Lasionycteris noctivagans) and little brown (Myotis lucifugus) bats only provided evidence of residual spatial correlation among detections. Targeting specific model assumptions using carefully chosen residual diagnostics is valuable for any analysis, and we remove previous barriers for occupancy analyses — lack of examples and practical advice.

Ecology↗
USGS2008

Calibrating and testing a gap model for simulating forest management in the Oregon Coast Range

The complex mix of economic and ecological objectives facing today's forest managers necessitates the development of growth models with a capacity for simulating a wide range of forest conditions while producing outputs useful for economic analyses. We calibrated the gap model ZELIG to simulate stand-level forest development in the Oregon Coast Range as part of a landscape-scale assessment of different forest management strategies. Our goal was to incorporate the predictive ability of an empirical model with the flexibility of a forest succession model. We emphasized the development of commercial-aged stands of Douglas-fir, the dominant tree species in the study area and primary source of timber. In addition, we judged that the ecological approach of ZELIG would be robust to the variety of other forest conditions and practices encountered in the Coast Range, including mixed-species stands, small-scale gap formation, innovative silvicultural methods, and reserve areas where forests grow unmanaged for long periods of time. We parameterized the model to distinguish forest development among two ecoregions, three forest types and two site productivity classes using three data sources: chronosequences of forest inventory data, long-term research data, and simulations from an empirical growth-and-yield model. The calibrated model was tested with independent, long-term measurements from 11 Douglas-fir plots (6 unthinned, 5 thinned), 3 spruce-hemlock plots, and 1 red alder plot. ZELIG closely approximated developmental trajectories of basal area and large trees in the Douglas-fir plots. Differences between simulated and observed conifer basal area for these plots ranged from -2.6 to 2.4 m2/ha; differences in the number of trees/ha ???50 cm dbh ranged from -8.8 to 7.3 tph. Achieving these results required the use of a diameter-growth multiplier, suggesting some underlying constraints on tree growth such as the temperature response function. ZELIG also tended to overestimate regeneration of shade-tolerant trees and underestimate total tree density (i.e., higher rates of tree mortality). However, comparisons with the chronosequences of forest inventory data indicated that the simulated data are within the range of variability observed in the Coast Range. Further exploration and improvement of ZELIG is warranted in three key areas: (1) modeling rapid rates of conifer tree growth without the need for a diameter-growth multiplier; (2) understanding and remedying rates of tree mortality that were higher than those observed in the independent data; and (3) improving the tree regeneration module to account for competition with understory vegetation. ?? 2008 Elsevier B.V.

Forest Ecology and Management↗
USGS2011

Exploring sensitivity of a multistate occupancy model to inform management decisions

1. Dynamic occupancy models are often used to investigate questions regarding the processes that influence patch occupancy and are prominent in the fields of population and community ecology and conservation biology. Recently, multistate occupancy models have been developed to investigate dynamic systems involving more than one occupied state, including reproductive states, relative abundance states and joint habitat‐occupancy states. Here we investigate the sensitivities of the equilibrium‐state distribution of multistate occupancy models to changes in transition rates. 2. We develop equilibrium occupancy expressions and their associated sensitivity metrics for dynamic multistate occupancy models. To illustrate our approach, we use two examples that represent common multistate occupancy systems. The first example involves a three‐state dynamic model involving occupied states with and without successful reproduction (California spotted owl Strix occidentalis occidentalis ), and the second involves a novel way of using a multistate occupancy approach to accommodate second‐order Markov processes (wood frog Lithobates sylvatica breeding and metamorphosis). 3. In many ways, multistate sensitivity metrics behave in similar ways as standard occupancy sensitivities. When equilibrium occupancy rates are low, sensitivity to parameters related to colonisation is high, while sensitivity to persistence parameters is greater when equilibrium occupancy rates are high. Sensitivities can also provide guidance for managers when estimates of transition probabilities are not available. 4. Synthesis and applications. Multistate models provide practitioners a flexible framework to define multiple, distinct occupied states and the ability to choose which state, or combination of states, is most relevant to questions and decisions about their own systems. In addition to standard multistate occupancy models, we provide an example of how a second‐order Markov process can be modified to fit a multistate framework. Assuming the system is near equilibrium, our sensitivity analyses illustrate how to investigate the sensitivity of the system‐specific equilibrium state(s) to changes in transition rates. Because management will typically act on these transition rates, sensitivity analyses can provide valuable information about the potential influence of different actions and when it may be prudent to shift the focus of management among the various transition rates.

Journal of Applied Ecology↗