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USGS2023

LANDFIRE

Landscape Fire and Resource Management Planning Tools (LANDFIRE) is a key national geospatial data source for strategic fire and resource management planning and analysis. LANDFIRE is the first complete, nationally consistent collection of more than 25 geospatial layers, databases, and ecological models at a 30-meter resolution that describe disturbance, vegetation, fire, and fuel characteristics. Because fires do not stop at ownership borders, LANDFIRE products by design support cross-boundary planning, management, and operations across all lands of the conterminous United States (CONUS), Alaska, Hawaii, and insular areas.

Fact Sheet↗

USGS2000

Digital data and geologic map of the Powder Mill Ferry Quadrangle, Shannon and Reynolds counties, Missouri

The geology of the Powder Mill Ferry 7 1/2-minute quadrangle , Shannon and Reynolds Counties, Missouri was mapped from 1997 through 1998 as part of the Midcontinent Karst Systems and Geologic Mapping Project, Eastern Earth Surface Processes Team. The map supports the production of a geologic framework that will be used in hydrogeologic investigations related to potential lead and zinc mining in the Mark Twain National Forest adjacent to the Ozark National Scenic Riverways (National Park Service). Digital geologic coverages will be used by other federal and state agencies in hydrogeologic analyses of the Ozark karst system and in ecological models.

IMAP↗

USGS2006

2005 annual progress report: Elk and bison grazing ecology in the Great Sand Dunes complex of lands

In 2000 the U.S. Congress authorized the expansion of the former Great Sand Dunes National Monument by establishing a new Great Sand Dunes National Park and Preserve in its place, and establishing the Baca National Wildlife Refuge. The establishment of Great Sand Dunes National Park and Preserve and the new Baca National Wildlife Refuge in the San Luis Valley (SLV), Colorado was one of the most significant land conservation actions in the western U.S. in recent years. The action was a result of cooperation between the National Park Service (NPS), U.S. Fish and Wildlife Service (USFWS), Bureau of Land Management (BLM), U.S. Forest Service (USDA-FS), and The Nature Conservancy (TNC). The new national park, when fully implemented, will consist of 107,265 acres, the new national preserve 41,872 acres, and the new national wildlife refuge (USFWS lands) 92,180 acres (fig. 1). The area encompassed by this designation protects a number of natural wonders and features including a unique ecosystem of natural sand dunes, the entire watershed of surface and groundwaters that are necessary to preserve and recharge the dunes and adjacent wetlands, a unique stunted forest, and other valuable riparian vegetation communities that support a host of associated wildlife and bird species. When the National Park was initially established, there were concerns about overconcentrations and impacts on native plant communities of the unhunted segments of a large and possibly growing elk (Cervus elaphus) population. This led to the designation of the Preserve as a compromise solution, where the elk could be harvested. The Preserve Unit, however, will not address all the ungulate management challenges. In order to reduce the current elk population, harvests of elk may need to be aggressive. But aggressive special hunts of elk to achieve population reductions can result in elk avoidance of certain areas or elk seeking refuge in areas where they cannot be hunted, while removals of whole herd segments and abandonment or alterations of migration routes can occur (Smith and Robbins, 1994; Boyce and others, 1991). Elk may seek refuge from hunting in the newly expanded Park Unit and TNC lands where they might overconcentrate and impact unique vegetation communities. In these sites of refugia, or preferred loafing sites, elk and bison could accelerate a decline in woody riparian shrubs and trees. This decline may also be due to changes in hydrology, climatic, or dunal processes, but ungulate herbivory might exacerbate the effects of those processes. To address the questions and needs of local resource managers, a multi-agency research project was initiated in 2005 to study the ecology, forage relations, and habitat relations of elk and bison in the Great Sand Dunes–Sangre de Cristo–Baca complex of lands. Meetings and discussions of what this research should include were started in 2001 with representatives from NPS, USFWS, TNC, the Colorado Division of Wildlife (CDOW), and USDA-FS/BLM. The final study plan was successfully funded in 2004 with research scheduled to start in 2005. The research was designed to encompass three major study elements: (1) animal movements and population dynamics, (2) vegetation and nutrient effects from ungulate herbivory, and (3) development of ecological models, using empirical data collected from the first two components, that will include estimates of elk carrying capacity and management scenarios for resource managers.

Colorado↗

USGS2016

Science information to support Missouri River Scaphirhynchus albus (pallid sturgeon) effects analysis

The Missouri River Pallid Sturgeon Effects Analysis (EA) was commissioned by the U.S. Army Corps of Engineers to develop a foundation of understanding of how pallid sturgeon ( Scaphirhynchus albus ) population dynamics are linked to management actions in the Missouri River. The EA consists of several steps: (1) development of comprehensive, conceptual ecological models illustrating pallid sturgeon population dynamics and links to management actions and other drivers; (2) compilation and assessment of available scientific literature, databases, and models; (3) development of predictive, quantitative models to explore the system dynamics and population responses to management actions; and (4) analysis and assessment of effects of system operations and actions on species’ habitats and populations. This report addresses the second objective, compilation and assessment of relevant information. Scientific information on pallid sturgeon and its environment has grown substantially during the last decade. Presently available (2015) information indicates that stocked sturgeon are surviving and growing, and that wild and hatchery sturgeon are spawning in the wild. However, natural recruitment to age-1 and older has not been detected since systematic sampling began in 2005. Population models indicate the sensitivity of population growth to certain demographic variables, in particular early-life stage survival and perhaps adult fecundity. This report documents the existing population models for the pallid sturgeon, and the substantial quantities of information developed through the Pallid Sturgeon Population Assessment Program (PSPAP), the Habitat Assessment and Monitoring Program (HAMP), the Comprehensive Sturgeon Research Project (CSRP), range-wide genetics databases, and related research studies. The reference database compiled for the EA consists of over 190 peer-reviewed documents specifically related to pallid sturgeon and over 12,000 references on the Missouri River system and related species. Notwithstanding the large quantity of information available, the EA faces challenges in synthesizing the information into useful, quantitative models. In particular, critical demographic parameters for population models remain uncertain and the functional relationships between the two main categories of physical management action—changes in flow regime and reengineering channel form—and pallid sturgeon survival responses are obscure. In addition, there is an overarching uncertainty about how physical management actions interact with propagation management actions in view of evolving understanding of genetic structuring of the pallid sturgeon population. Synthesis efforts are also challenged by the fragmentation of information sources among projects and agencies; one objective of this report is to facilitate future assessments by providing documentation of what information is available and where.

Iowa, Kansas, Missouri, Montana, Nebraska, North D↗

USGS2016

Development of working hypotheses linking management of the Missouri River to population dynamics of Scaphirhynchus albus (pallid sturgeon)

This report documents a process of filtering of hypotheses that relate Missouri River Scaphirhynchus albus (pallid sturgeon) population dynamics to management actions including flow alterations, channel reconfigurations, and pallid sturgeon population augmentation. The filtering process was a partnership among U.S. Geological Survey, U.S. Army Corps of Engineers, and U.S. Fish and Wildlife Service to contribute to the Missouri River Recovery Management Plan process. The objective of the filtering process was to produce a set of hypotheses with high relevance to pallid sturgeon population dynamics and decision making on the Missouri River. The Missouri River Pallid Sturgeon Effects Analysis team filtered hundreds of potential hypotheses implicit in conceptual ecological models to develop a set of 40 candidate dominant hypotheses that were identified by experts as being important in pallid sturgeon population dynamics. Using a modified Delphi process and additional expert opinion, the team reduced this set of hypotheses to 23 working dominant hypotheses. We then matched the 23 hypotheses with management actions that could influence the biotic outcomes, resulting in as many as 176 potential effects between management actions and pallid sturgeon in the Missouri River. This number was consolidated to a candidate set of 53 working management hypotheses because some management actions applied to multiple life stages of the pallid sturgeon. We used an additional round of expert surveys to identify a set of 30 working management hypotheses. Finally, the set of working management hypotheses was filtered by the U.S. Army Corps of Engineers, Missouri River Recovery Program for actions that were within the agency’s authority and jurisdiction. This round resulted in a set of 21 hypotheses for initial modeling of linkages from management to pallid sturgeon population responses. The initial set of candidate hypotheses provides a useful starting point for quantitative modeling and adaptive management of the river and species. We anticipate that hypotheses will change from the set of working management hypotheses as adaptive management progresses. More importantly, hypotheses that have been filtered out of our multistep process are still being considered. These filtered hypotheses are archived and if existing hypotheses are determined to be inadequate to explain observed population dynamics, new hypotheses can be created or filtered hypotheses can be reinstated.

Missouri River Basin↗

USGS2023

Stimulation of aquatic bacteria from Mammoth Cave, Kentucky, by sublethal concentrations of antibiotics

Many microorganisms secrete secondary metabolites with antibiotic properties; however, there is debate whether the secretions evolved as a means to gain a competitive edge or as a chemical signal to coordinate community growth. The objective of this research was to investigate if select antibiotics acted as a weapon or as a chemical signal by exposing communities of aquatic cave bacteria to increasing concentrations of antibiotics. Water samples were collected from six cave locations where actinobacterial mats appeared to be plentiful. Bacterial growth was measured using colony counts on 10 % tryptic soy agar augmented with increasing concentrations of erythromycin, tetracycline, kanamycin, gentamicin, or quaternary ammonia compounds (QAC). Colony counts generally decreased as the gentamicin, kanamycin and QAC dose increased. In contrast, the colony numbers increased on agar plates supplemented with 0.01 mg L −1 , 0.10 mg L −1 and 1.00 mg L −1 erythromycin or tetracycline. A 10.00 mg L −1 dose of each antibiotic treatment reduced bacteria colonies by 98 % or more. Community-level physiological capabilities were evaluated using Ecolog plates inoculated with cave water dosed with either 0.00 mg L −1 or 0.10 mg L −1 of erythromycin. Incubation with the antibiotic almost doubled the number of food substrates used in the first 24 hours. There was a significant increase in the use of acetyl glucosamine, arginine, and putrescine when bacteria were exposed to 0.10 mg L −1 erythromycin triggered by the antibiotic acting as a chemical messenger. Principal component analysis confirmed a shift in substrate preferences when erythromycin was added. A conceptual ecological model is proposed based on the response of aquatic cave bacteria to sublethal antibiotics.

Kentucky↗

USGS2013

Adaptive harvest management for the Svalbard population of pink-footed geese: briefing summary

The African-Eurasian Waterbird Agreement (AEWA; http://www.unep-aewa.org/) calls for means to manage populations which cause conflicts with certain human economic activities. The Svalbard population of the pink-footed goose has been selected as the first test case for such an international species management plan to be developed. This document describes progress to date on the development of an adaptive harvest management (AHM) strategy for maintaining pink-footed goose abundance near their target level by providing for sustainable harvasts in Norway and Denmark. This briefing supplements material provided in the Progress Summary distributed to the International Working Group on February 1, 2013. We emphasize that peer review is an essential aspect of the process of developing and implementing an AHM program for pink-footed geese, and we will continue to solicit reviews by the International Working Group and their staff, as well as scientists not engaged in this effort. We wish to make the Working Group aware the the following two manuscripts have been submitted recently to refereed journals and are available upon request from the senior authors: Jensen, G.H., J. Madsen, F.A. Johnson, and M. Tamstorf. Snow conditions as an estimator of the breeding output in high-Arctic pink-footed geese Anser brachyrhynchus. Polar Biology: In review. Johnson, F.A., G.H. Jensen, J. Madsen, and B.K. Williams. Uncertainity, robustness, and the value of information in managing an expanding Arctic goose population. Ecological Modeling: In review. In addition to these manuscripts, the Progress Summary (February 1, 2013), and this Briefing Summary (April 23, 2013) an annual report will be produced in August 2013 and every summer thereafter. Additional manuscripts for journal publication are also anticipated.

Report↗

USGS2019

Can’t see the random forest for the decision trees: Selecting predictive models for restoration ecology

Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this paper was to identify an optimal predictive framework for restoration ecology using eleven modeling frameworks (including, machine learning, inferential, and ensemble approaches), and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large post-fire sagebrush reestablishment project in the Great Basin, USA. Predictive power varied among models and data groups, ranging from 58-79% accuracy. Finer scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the ten models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes.

Restoration Ecology↗

USGS2019

Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses

Soil and plant responses to climate change can be quantified in controlled settings. However, the complexity of climate projections often leads researchers to evaluate ecosystem response based on general trends, rather than specific climate model outputs. Climate projections capture spatial and temporal climate extremes and variability that are lost when using mean climate trends. In addition, application of climate projections in experimental settings remains limited. Our objective was to develop a framework to incorporate statistically downscaled climate model projections into the design of temperature and precipitation treatments for ecological experiments. To demonstrate the utility of experimental treatments derived from climate projections, we used wetlands in the Great Plains as a model ecosystem for evaluating plant and soil responses. Spatial and temporal projections were selected to capture variability and intensity of projected future conditions for exemplary purposes. To illustrate climate projection application for ecological experiments, we developed temperature and precipitation treatments based on moderate-emissions scenario climate outputs (i.e., RCP4.5–650 ppm CO 2 equivalent). Our temperature treatments captured weekly trends that represented cool, average, and warm temperature predictions, and our daily precipitation treatments mimicked various seasonal precipitation trends and extreme events projected for the late 21st century. Treatments were applied to two short-term controlled experiments evaluating (1) plant germination (temperature treatment applied in growth chamber) and (2) soil nitrogen cycling (precipitation treatment applied in greenhouse) responses to projected future conditions in the Great Plains. Our approach provides flexibility for selecting appropriate and precise climate model outputs to design experimental treatments. Using these techniques, ecologists can better incorporate variation in climate model projections for experimentally evaluating ecosystem responses to future climate conditions, reduce uncertainty in predictive ecological models, and apply predicted outcomes when making management and policy decisions.

Nebraska, Texas↗

USGS2008

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

Book↗

USGS2018

The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: An extension of the Plankton Ecology Group (PEG) model

The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence (ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3 methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3) profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling techniques would have missed SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean water column data. Contrary to the PEG model’s proposed negligible role for physical control of phytoplankton during the growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus, an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.

Inland Waters↗

USGS2024

Individual-based ecological particle tracking model (ECO-PTM) for simulating juvenile chinook salmon migration and survival through the Sacramento – San Joaquin Delta

Recovery of endangered salmon species in the Central Valley of California amidst prolonged drought and climate change necessitates innovative water management actions that balance species recovery and California's water demands. We describe an individual-based ecological particle tracking model (ECO-PTM) that can be used to assess the efficacy of proposed actions. Based on a random walk theory, the model tracks individual particles’ travel time, routing and survival in a flow field simulated by the Delta Simulation Model 2 hydrodynamic module (DSM2 HYDRO). The random walk particles are parameterized to have fish-like swimming behaviors, including upstream/downstream swimming, probabilistic holding behaviors, and stochastic swimming velocities. Particle routing at key junctions is based on well-established statistical models, and route-specific survival is calculated using the XT mean free-path length model. Behavioral parameters were estimated by fitting several competing models to a multiyear dataset of travel times from acoustic tagged juvenile salmon. The model’s baseline simulations under historical flow conditions from 1991 to 2016 successfully replicated essential relationships between salmon outmigration survival and hydrodynamic conditions, consistent with previous studies and the STARS (Survival Travel Time and Routing Simulation) statistical simulation model. Simulation results for management scenarios revealed multifaceted influences on fish survival, including Delta flow, flow at key junctions, route alterations, seasons, and water year characteristics. Importantly, these results highlight ECO-PTM’s potential to predict fish survival outcomes of proposed actions, serving as a foundation for informed future research, decision-making, and effective management strategies to enhance the survival prospects of out-migrating salmonids within the Sacramento-San Joaquin Delta ecosystem.

California↗

USGS2003

Structural Equation Modeling: Applications in ecological and evolutionary biology

This book presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems. Supplementary information can be found at the authors website, http://www.jamesbgrace.com/ . Details why multivariate analyses should be used to study ecological systems Exposes unappreciated weakness in many current popular analyses Emphasizes the future methodological developments needed to advance our understanding of ecological systems.

Book↗

USGS2023

Translating stakeholder narratives for participatory modeling in landscape ecology

Context Engaging stakeholders in research is needed for many of the sustainability challenges that landscape ecologists address. Involving stakeholders’ perspectives through narratives in participatory modeling fosters better understanding of the problem and evaluation of the acceptability of tradeoffs and creates buy-in for management actions. However, stakeholder-driven inputs often take the form of complex qualitative descriptions, rather than model-ready numerical or categorical inputs. Objectives Translating narratives into models, model parameters, or scenarios is essential for leveraging stakeholder knowledge and engagement. Drawing from varied experiences to identify lessons learned and pitfalls, we address the practice of translating narratives into models and using those narratives to interpret and communicate results. Methods We drew from seven participatory landscape ecology projects across North America to synthesize lessons for the inclusion of stakeholder narratives in modeling studies. Results We offer 8 lessons as practical guidance for other landscape ecologists to move the science beyond a unilateral focus on ecological systems and to maximize the benefits of landscape sustainability science. Conclusions These lessons are starting points, as real projects are complex, nuanced, and sometimes contradictory. Translating narratives into models is important for addressing complex sustainability challenges; we hope that these starting points are helpful to those foraying into this type of research.

Landscape Ecology↗

USGS1993

Applications of bioenergetics models to fish ecology and management: where do we go from here?

Papers and panel discussions given during a 1992 symposium on bioenergetics models are summarized. Bioenergetics models have been applied to a variety of research and management questions related to fish stocks, populations, food webs, and ecosystems. Applications include estimates of the intensity and dynamics of predator-prey interactions, nutrient cycling within aquatic food webs of varying trophic structure, and food requirements of single animals, whole populations, and communities of fishes. As tools in food web and ecosystem applications, bioenergetics models have been used to compare forage consumption by salmonid predators across the Laurentian Great Lakes for single populations and whole communities, and to estimate the growth potential of pelagic predators in Chesapeake Bay and Lake Ontario. Some critics say that bioenergetics models lack sufficient detail to produce reliable results in such field applications, whereas others say that the models are too complex to be useful tools for fishery managers. Nevertheless, bioenergetics models have achieved notable predictive successes. Improved estimates are needed for model parameters such as metabolic costs of activity, and more complete studies are needed of the bioenergetics of larval and juvenile fishes. Future research on bioenergetics should include laboratory and field measurements of key model parameters such as weight-dependent maximum consumption, respiration and activity, and thermal habitats actually occupied by fish. Future applications of bioenergetics models to fish populations also depend on accurate estimates of population sizes and survival rates.

Transactions of the American Fisheries Society↗

USGS2020

Hydrologic modeling for flow-ecology science in the Southeastern United States and Puerto Rico

An understanding of the applicability and utility of hydrologic models is critical to support the effective management of water resources throughout the Southeastern United States (SEUS) and Puerto Rico (PR). Hydrologic models have the capacity to provide an estimate of the quantity of available water at ungauged locations (i.e., areas of the country where a U.S. Geological Survey [USGS] continuous record gauge is not installed) and provide the baseline flow information necessary to develop the linkages between water availability and characteristics of streamflow that support ecological communities (i.e., support the development of flow-ecology response models). This report inventories and then directly examines and compares a subset of hydrologic models used to estimate streamflow at a number of gauged basins across the SEUS and PR. This effort was designed to evaluate, quantify, and compare the magnitude of error and to investigate the potential causes of error associated with predicted streamflows from seven hydrologic models of varying complexity and calibration strategy. This was accomplished by computing and then comparing classical hydrologic model fit statistics (e.g., mean bias, coefficient of determination [R2], root mean squared error [RMSE], Nash-Sutcliffe Efficiency [NSE]) and understanding the bias in the prediction in these and a subset of ecologically relevant flow metrics (ERFMs). Additionally, streamflow predictions from a larger regional-scale hydrologic model were compared to those of several fine-scale hydrologic models under a range of hypothetical climate change scenarios to determine the range of predicted streamflow responses to fixed climate perturbations. A pilot study was conducted using predicted streamflow and boosted regression trees to develop a set of predictive flow-ecology response models to assess the potential change in fish species richness in the North Carolina Piedmont under several scenarios of water availability change. This report is intended to provide a general assessment of all the tools and techniques available to support hydrologic modeling for flow-ecology science in the SEUS and PR. It is our hope that the approach used herein to understand differences in streamflow predictions among a subset of hydrologic models that have been applied in the SEUS for developing flow-ecology response models will provide water resource managers and stakeholders with an informed pathway for developing the capacity to link streamflow and ecological response and an understanding of some of the limitations associated with these type of modeling efforts.

Alabama, Florida, Georgia, Mississippi, North Caro↗

USGS2015

From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology

In this perspectives paper we highlight a heretofore underused statistical method in soil ecological research, structural equation modeling (SEM). SEM is commonly used in the general ecological literature to develop causal understanding from observational data, but has been more slowly adopted by soil ecologists. We provide some basic information on the many advantages and possibilities associated with using SEM and provide some examples of how SEM can be used by soil ecologists to shift focus from describing patterns to developing causal understanding and inspiring new types of experimental tests. SEM is a promising tool to aid the growth of soil ecology as a discipline, particularly by supporting research that is increasingly hypothesis-driven and interdisciplinary, thus shining light into the black box of interactions belowground.

Pedobiologia↗

USGS2003

Importance of the predator's ecological neighborhood in modeling predation on migrating prey

Most mathematical descriptions of predator-prey interactions fail to take into account the spatio-temporal structures of the populations, which can lead to errors or misinterpretations. For example, a compact pulse of prey migrating through a field of quasi-stationary predators may not be well described by standard predator-prey models, because the predators and prey are unlikely to be well mixed; that is, the prey may be exposed to only a fraction of the predator population at a time. This underscores the importance of properly accounting for the ecological neighborhood, or effective feeding range, of predators in models. We illustrate this situation with a series of models of salmon smolts migrating through a reservoir arrayed with predators. The reservoir is divided into a number of longitudinal compartments or spatial cells, the length of each cell representing the upstream-downstream range over which predators can forage. In this series of models a 100-km-long reservoir is divided, successively into 2, 5, 10, 25, 50, 100, 200, and 400 cells, with respective cell lengths of 50, 20, 10, 4, 2, 1, 0.5, and 0.25 km. We used a detailed individual-based simulation model at first, but to ensure robustness of results we supplemented this with a simple analytic model. Both models showed sharp differences in the predicted mortality to a compact pulse of smolt prey moving through the reservoir, depending on the number of spatial cells in the model. In particular, models with fewer than about 10 cells vastly overpredicted the amount of mortality due to predators with activity ranges of not more than a few kilometers. These results corroborate recent theoretical and simulation studies on the importance of spatial scale and behavior in modeling predator-prey dynamics.

Oikos↗