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USGS2016

External influences on ecological theory: Report on organized oral Session 80 at the 100th Anniversary Meeting of the Ecological Society of America

The 100‐year history of the Ecological Society of America spans most of the major advances in the field of ecology, from the “niche” of Grinnell and others, to Lotka and Volterra's models of predation and competition based on the logistic growth equation, to the concept of competitive exclusion developed from experimental ecology, to genetics and evolutionary ecology and all the ramifications and specializations of these topics over the rest of the 20th and into the 21st century. The objective of this session, sponsored by the Historical Records Committee of the ESA, was to explore how ecological concepts have been shaped and changed by influences that are external to the scientific method, such as funding priorities, ideology, politics, personalities, and differences between the ecosystems where influential ecologists developed their ideas. Among the many memorable quotations of the philosopher/poet George Santayana (1863–1952) is the often quoted and misquoted observation, “Those who cannot remember the past are condemned to repeat it.”

Bulletin of the Ecological Society of America↗

USGS2020

Multi‐species occupancy models: Review, roadmap, and recommendations

Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.

Ecography↗

USGS2024

Integrating social-ecological outcomes into invasive species management: The Tamarix case

Incorporating societal considerations into decisions related to invasive species management is desirable, but can be challenging because it requires a solid understanding of the ecological functions and socio-cultural and economic benefits and values of the invaded environment before and after invasion. The ecosystem service ( ES ) concept was designed to facilitate such decision-making by establishing direct connections between ecosystem properties and human well-being, but its application in invasive species management has not been systematic. In this Discussion paper, we propose the adoption of the ES cascade model as a framework for understanding the environmental effects, costs and benefits associated with controlling an invasive shrub ( Tamarix spp.) in riparian systems of the western United States. The cascade model has the advantage of explicitly dissecting social-ecological systems into five components: ecosystem structure and processes, ecological functions, ecosystem services, benefits and the economic and socio-cultural valuation of these services and benefits. The first two have received significant attention in the evaluation of Tamarix control effectiveness. The last three have long been implicitly acknowledged over decades of Tamarix management in the region, but have not been formally accounted for, which we believe would increase the effectiveness, accountability and transparency of management efforts.

NeoBiota↗

USGS2025

Modeling lamprey distribution using flow, geomorphology, and elevation in a terminal lake system

Objective Lampreys are an ecologically important group of fishes. Several species are imperiled and lack key distribution and habitat data. The terminal Goose Lake Basin, U.S.A. is home to two such species, the Goose Lake Lamprey, Entosphenus sp. (formally undescribed), and the Pit-Klamath Brook Lamprey, E. lethophagus . Species distribution models (SDMs) are useful for identifying key habitats; however, SDMs are subject to accuracy impairments caused by scale mismatches and spatial autocorrelation—both exacerbated by the hierarchical structure of dendritic stream networks. Our goal was to relate lamprey presence–absence to ecological drivers and predict the distribution of lampreys across the Goose Lake Basin. Methods Using a dataset pooling approach, we integrated count and presence–absence data from five surveys and relevant habitat variables from publicly available, geospatial datasets to build logistic regression models. To account for potential mismatches of scale, we compared three sample grains for slope and sinuosity (i.e., stream segment lengths: 250, 500, and 1,000 m), and two scales of elevation (site and watershed). We accounted for spatial autocorrelation by incorporating network-based and Euclidean spatial dependencies using a spatial stream network (SSN) modeling approach. Using the best-fit spatial and non-spatial models, we predicted basin-wide lamprey distribution. Result Flow, sinuosity at our largest sample grain (1,000 m), and watershed-scale elevation were positively associated with lamprey presence, whereas slope was negatively associated. The non-spatial model predicted lamprey presence among sinuous, low-gradient streams, whereas the spatial model, which identified Euclidean and flow-connected spatial relationships, predicted contiguous patches with a high probability of occurrence near areas with previously observed presences. Conclusions Our study revealed ecological relationships and produced an accurate basinwide SDM. Prediction and inference improved after accounting for spatial relationships across multiple scales. Developing accurate and efficient modeling strategies that incorporate the hierarchical structure inherent to stream ecosystems aids in the management and conservation of native fishes such as lampreys.

Transactions of the American Fisheries Society↗

USGS2024

Declining ecological resilience and invasion resistance under climate change in the sagebrush region, United States

In water-limited dryland ecosystems of the Western United States, climate change is intensifying the impacts of heat, drought, and wildfire. Disturbances often lead to increased abundance of invasive species, in part, because dryland restoration and rehabilitation are inhibited by limited moisture and infrequent plant recruitment events. Information on ecological resilience to disturbance (recovery potential) and resistance to invasive species can aid in addressing these challenges by informing long-term restoration and conservation planning. Here, we quantified the impacts of projected future climate on ecological resilience and invasion resistance (R&R) in the sagebrush region using novel algorithms based on ecologically relevant and climate-sensitive predictors of climate and ecological drought. We used a process-based ecohydrological model to project these predictor variables and resulting R&R indicators for two future climate scenarios and 20 climate models. Results suggested widespread future R&R decreases (24%–34% of the 1.16 million km 2 study area) that are generally consistent among climate models. Variables related to rising temperatures were most strongly linked to decreases in R&R indicators. New continuous R&R indices quantified responses to climate change; particularly useful for areas without projected change in the R&R category but where R&R still may decrease, for example, some of the areas with a historically low R&R category. Additionally, we found that areas currently characterized as having high sagebrush ecological integrity had the largest areal percentage with expected declines in R&R in the future, suggesting continuing declines in sagebrush ecosystems. One limitation of these R&R projections was relatively novel future climatic conditions in particularly hot and dry areas that were underrepresented in the training data. Including more data from these areas in future updates could further improve the reliability of the projections. Overall, these projected future declines in R&R highlight a growing challenge for natural resource managers in the region, and the resulting spatially explicit datasets provide information that can improve long-term risk assessments, prioritizations, and climate adaptation efforts.

western United States↗

USGS2010

Testing a Mahalanobis distance model of black bear habitat use in the Ouachita Mountains of Oklahoma

Regional wildlife–habitat models are commonly developed but rarely tested with truly independent data. We tested a published habitat model for black bears ( Ursus americanus ) with new data collected in a different site in the same ecological region (i.e., Ouachita Mountains of Arkansas and Oklahoma, USA). We used a Mahalanobis distance model developed from relocations of black bears in Arkansas to produce a map layer of Mahalanobis distances on a study area in neighboring Oklahoma. We tested this modeled map layer with relocations of black bears on the Oklahoma area. The distributions of relocations of female black bears were consistent with model predictions. We conclude that this modeling approach can be used to predict regional suitability for a species of interest.

Oklahoma↗

USGS2021

A new approach for representing agent-environment feedbacks: Coupled agent-based and state-and-transition simulation models

Context Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success. Objectives Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns. Methods We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park. Results The coupled ABM-STSM: (1) streamlined handling of model inputs and outputs; (2) allowed representation of processes at multiple temporal scales; (3) minimized assumptions; and (4) generated spatial and temporal patterns that better reflected agent-environment interactions. Conclusions These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections, and opens new opportunities for communicating models and linking to other methods.

Landscape Ecology↗

USGS2012

Predictions and retrodictions of the hierarchical representation of habitat in heterogeneous environments

Interaction between habitat and species is central in ecology. Habitat structure may be conceived as being hierarchical, where larger, more diverse, portions or categories contain smaller, more homogeneous portions. When this conceptualization is combined with the observation that species have different abilities to relate to portions of the habitat that differ in their characteristics, a number of known patterns can be derived and new patterns hypothesized. We propose a quantitative form of this habitat–species relationship by considering species abundance to be a function of habitat specialization, habitat fragmentation, amount of habitat, and adult body mass. The model reproduces and explains patterns such as variation in rank–abundance curves, greater variation and extinction probabilities of habitat specialists, discontinuities in traits (abundance, ecological range, pattern of variation, body size) among species sharing a community or area, and triangular distribution of body sizes, among others. The model has affinities to Holling's textural discontinuity hypothesis and metacommunity theory but differs from both by offering a more general perspective. In support of the model, we illustrate its general potential to capture and explain several empirical observations that historically have been treated independently.

Ecological Modelling↗

USGS2023

Forecasting natural regeneration of sagebrush after wildfires using population models and spatial matching

Context Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions. Objectives An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover ( Artemisia spp.) is likely to occur within the burnt areas across the region (> 900,000 km 2 ). Methods First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy. Results Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover. Conclusions We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.

Great Basin↗

USGS2012

Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States

The Great Plains of the United States has undergone extensive land-use and land-cover change in the past 150 years, with much of the once vast native grasslands and wetlands converted to agricultural crops, and much of the unbroken prairie now heavily grazed. Future land-use change in the region could have dramatic impacts on ecological resources and processes. A scenario-based modeling framework is needed to support the analysis of potential land-use change in an uncertain future, and to mitigate potentially negative future impacts on ecosystem processes. We developed a scenario-based modeling framework to analyze potential future land-use change in the Great Plains. A unique scenario construction process, using an integrated modeling framework, historical data, workshops, and expert knowledge, was used to develop quantitative demand for future land-use change for four IPCC scenarios at the ecoregion level. The FORE-SCE model ingested the scenario information and produced spatially explicit land-use maps for the region at relatively fine spatial and thematic resolutions. Spatial modeling of the four scenarios provided spatial patterns of land-use change consistent with underlying assumptions and processes associated with each scenario. Economically oriented scenarios were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. Environmentally oriented scenarios experienced modest declines in natural land covers to slight increases. Model results were assessed for quantity and allocation disagreement between each scenario pair. In conjunction with the U.S. Geological Survey's Biological Carbon Sequestration project, the scenario-based modeling framework used for the Great Plains is now being applied to the entire United States.

Great Plains↗

USGS2018

Machine learning for ecosystem services

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.

Ecosystem Services↗

USGS2017

Network analysis of a regional fishery: Implications for management of natural resources, and recruitment and retention of anglers

Angler groups and water-body types interact to create a complex social-ecological system. Network analysis could inform detailed mechanistic models on, and provide managers better information about, basic patterns of fishing activity. Differences in behavior and reservoir selection among angler groups in a regional fishery, the Salt Valley fishery in southeastern Nebraska, USA, were assessed using a combination of cluster and network analyses. The four angler groups assessed ranged from less active, unskilled anglers (group One) to highly active, very skilled anglers (group Four). Reservoir use patterns and the resulting network communities of these four angler groups differed; the number of reservoir communities for these groups ranged from two to three and appeared to be driven by reservoir location (group One), reservoir size and its associated attributes (groups Two and Four), or an interaction between reservoir size and location (group Three). Network analysis is a useful tool to describe differences in participation among angler groups within a regional fishery, and provides new insights about possible recruitment of anglers. For example, group One anglers fished reservoirs closer to home and had a greater probability of dropping out if local reservoir access were restricted.

Fisheries Research↗

USGS2026

Predicting reach-scale macroinvertebrate community changes due to declining flows in an aridland river

Understanding the connection between streamflow and macroinvertebrate community structure is critical for managing river ecosystems under scenarios of change caused by human activities and drought. Streamflow influences physical habitat characteristics, including depth, water velocity, and substrate composition, which structure macroinvertebrate communities. Although strong empirical evidence has linked macroinvertebrates to these variables, most studies are correlative, and predictive approaches that link altered streamflow to changes in habitat availability and community structure remain limited. To address this gap, we coupled benthic macroinvertebrate surveys with reach-scale, 2-dimensional hydraulic models to quantify changes in community structure in response to altered streamflows. We characterized macroinvertebrate communities and physical habitat conditions in riffle, run, and pool mesohabitats across 3 reaches of a free-flowing section of the Verde River (Arizona, USA) over 3 y and developed hydraulic models using overflight data, ground surveys, and streamflow monitoring. Macroinvertebrate communities differed among mesohabitats, with riffles supporting higher macroinvertebrate densities and pools exhibiting higher taxonomic richness. The 2-dimensional hydraulic models indicated that declining streamflows could reduce the area of suitable habitat in riffles and runs relative to pools, potentially shifting the distribution of mesohabitats and altering community composition. These changes may lead to declines in overall macroinvertebrate diversity and abundance under reduced flow conditions. By explicitly linking streamflow to habitat availability and community structure, this study demonstrates how integrating hydraulic modeling with biological surveys can improve predictions of ecological response to flow alteration. This framework provides a basis for flow management strategies aimed at maintaining habitat heterogeneity and supporting biodiversity in river networks.

Arizona↗

USGS2000

National land-cover pattern data

Land cover and its spatial patterns are key ingredients in ecological studies that consider large regions and the impacts of human activities. Because land-cover maps show only cover types and their locations, further processing is needed to extract pattern information and to characterize its spatial variability. We are producing a nationally consistent spatial database of six land-cover pattern indices: forest area density, forest connectivity, the U index (a measure of general land-use pressure by humans), land-cover connectivity, land-cover diversity, and landscape pattern types. We use the land-cover maps produced by the Multi-resolution Land Characteristics Consortium for the conterminous United States at 30-m resolution. The goal of this paper is to encourage use of the pattern data as: contextual information and independent variables for studies involving a set of field sites; indicators of landscape conditions for ecological assessments; and dependent variables in biogeographic and socioeconomic models. The new maps will be most useful in studies that require consistent and comparable land-cover pattern measurements over large regions and can be combined with the original land-cover maps and other data.

Ecology↗

USGS2018

Montane-breeding bird distribution and abundance across national parks of southwestern Alaska

Between 2004 and 2008, biologists conducted an inventory of breeding birds during May–June primarily in montane areas (>100 m above sea level) in Aniakchak National Monument and Preserve (Aniakchak NMP), Katmai National Park and Preserve (Katmai NPP), and Lake Clark National Park and Preserve (Lake Clark NPP) in southwestern Alaska. Observers conducted 1,021 point counts along 169 transects within 63 10-km × 10-km plots that were randomly selected and stratified by ecological subsection. We created hierarchical N-mixture models to estimate detection probability and abundance for 15 species, including 12 passerines, 2 galliforms, and 1 shorebird. We first modeled detection probability relative to observer, date within season, and proportion of dense vegetation cover around the point, then modeled abundance as a function of land cover composition (proportion of seven coarse-scale land cover types) within 300 m of the survey point. Land cover relationships varied widely among species but most showed selection for low to tall shrubs (0.2–5 m tall) and an avoidance of alpine and 2 dwarf shrub–herbaceous cover types. After adjusting for species not observed, we estimated a minimum of 107 ± 9 species bred in the areas surveyed within the three parks combined. Species richness was negatively associated with elevation and associated land cover types. At comparable levels of survey effort (n = 721 birds detected), species richness was greatest in Lake Clark NPP (75 ± 12 species), lowest in Aniakchak NMP (45 ± 6 species), and intermediate at Katmai NPP (59 ± 10 species). Species richness was similar at equivalent survey effort (n = 973 birds detected) within the Lime Hills, Alaska Range, and Alaska Peninsula ecoregions (68 ± 8; 79 ± 11; 67 ± 11, respectively). Species composition was similar across all three parks and across the three major ecoregions (Alaska Range, Alaska Peninsula, Lime Hills) that encompass them. Our results provide baseline estimates of relative abundance and models of abundance and species richness relative to land cover that can be used to assess future changes in avian distribution. Additionally, these subarctic montane parks may serve as signals of landscape change and barometers for the assessment of population and distributional changes as a result of warming temperatures and changing precipitation patterns.

Alaska↗

USGS2012

Relative value of managed wetlands and tidal marshlands for wintering northern pintails

Northern pintail Anas acuta (hereafter, pintail) populations have declined substantially throughout the western US since the 1970s, largely as a result of converting wetlands to cropland. Managed wetlands have been developed throughout the San Francisco Bay estuaries to provide wildlife habitat, particularly for waterfowl. Many of these areas were historically tidal baylands and plans are underway to remove dikes and restore tidal action. The relationship between tidal baylands and waterfowl populations is poorly understood. Our objective was to provide information on selection and avoidance of managed and tidal marshland by pintails. During 1991–1993 and 1998–2000, we radio-marked and relocated 330 female pintails (relocations, n =11,574) at Suisun Marsh, the largest brackish water estuary within San Francisco Bay, to estimate resource selection functions during the nonbreeding months (winter). Using a distance-based modeling approach, we calculated selection functions for different ecological communities (e.g., tidal baylands) and investigated variation explained by time of day (day or night hours) to account for differences in pintail behavior (i.e., foraging vs. roosting). We found strong evidence for selection of managed wetlands. Pintails also avoided tidal marshes and bays and channels. We did not detect differences in selection function between day and night hours for managed wetlands but the degree of avoidance of other habitats varied by time of day. We also found that areas subjected to tidal action did not influence the selection of immediately adjacent managed wetlands. If current management goals include providing habitat for wintering waterfowl populations, particularly pintail, then we recommend wildlife managers focus tidal restoration on areas that are not currently managed wetland and/or improve conditions in areas of managed wetlands to increase local carrying capacities

California↗

USGS1998

Characterization of an old-growth bottomland hardwood wetland forest in Northeast Texas: Harrison Bayou

Most wetland losses in the southern region over the past 200 years have occurred in bottomland hardwood forests. By 1980 the original extent of palustrine bottomland in Texas had been reduced by 63%, from roughly 16 to 6 million acres. Additional losses have occurred during more recent years as a result of conversion to agriculture and timber harvests; these factors and the need to supply new hardwood chip mills in the region pose a potential threat to the remaining hardwood resource. The Harrison Bayou watershed in northeast Texas contains one of the few relatively undisturbed bottomland hardwood wetland forests in the State. Harrison Bayou is part of the Caddo Lake wetlands complex, most of which was designated a Wetland of International Importance under the Ramsar Treaty in October of 1993. Caddo Lake State Park is one of fifteen "Ramsar" wetlands in the United States; it is the only wetland with this designation in the State of Texas. Harrison Bayou is an important component of the Caddo Lake watershed; it represents a model bottomland hardwood wetland in both structure and ecological function. Three major forest cover types illustrate the diversity of the 600-hectare bottomland hardwood/baldcypress forest at Harrison Bayou. Comparison of wetland forest extent and species composition in 1977 with 1993 revealed very little change in wetland forest community structure.

Texas↗

USGS2007

Representing general theoretical concepts in structural equation models: The role of composite variables

Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically-based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling heterogeneous concepts of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially-reduced-form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influence of suites of variables are often of interest. ?? Springer Science+Business Media, LLC 2007.

Environmental and Ecological Statistics↗