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At least 325 records · Page 18Linked to original sources

Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects

Despite advances in toxicity testing and development of new approach methodologies (NAMs) for hazard assessment, the ecological risk assessment (ERA) framework for terrestrial wildlife (i.e., air-breathing amphibians, reptiles, birds, and mammals) has remained unchanged for decades. While survival, growth, and reproductive endpoints derived from whole animal toxicity tests are central to hazard assessment, non-standard measures of biological effects at multiple levels of biological organization (e.g., molecular, cellular, tissue, organ, organism, population, community, ecosystem) have potential to enhance the relevance of prospective and retrospective wildlife ERAs. Other factors (e.g., indirect effects of contaminants on food supplies and infectious disease processes) are influenced by toxicants at individual, population, and community levels, and need to be factored into chemically-based risk assessments to enhance the “eco” component of ERAs. Regulatory and logistical challenges often relegate such non-standard endpoints and indirect effects to post-registration evaluations of pesticides and industrial chemicals, and contaminated site evaluations. While NAMs are being developed, to date their applications in ERAs focused on wildlife have been limited. No single magic tool or model will address all uncertainties in hazard assessment. Modernizing wildlife ERAs will likely entail combinations of laboratory and field-derived data at multiple levels of biological organization, knowledge collection solutions (e.g., systematic review, adverse outcome pathway frameworks), and inferential methods that facilitate integrations and risk estimations focused on species, populations, interspecific extrapolations, and ecosystem services modeling, with less dependence on whole animal data and simple hazard ratios.

Integrated Environmental Assessment and Management↗

Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes

There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects . This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.

Mato Grosso↗

An “EAR” on environmental surveillance and monitoring: A case study on the use of Exposure–Activity Ratios (EARs) to prioritize sites, chemicals, and bioactivities of concern in Great Lakes waters

Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on concentration alone, it can be difficult to identify which compounds may be of toxicological concern and should be prioritized for further monitoring, in-depth testing, or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high-throughput screening (HTS) data, such as the ToxCast database, which contains information for over 9000 compounds screened through up to 1100 bioassays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast effects database were used to calculate exposure–activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Prioritized bioactivities from the EAR analysis were linked to discrete adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts.

Great Lakes↗

A framework to integrate innovations in invasion science for proactive management

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

Biological Reviews↗

Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach

Urban development alters important physical, chemical, and biological processes that define urban stream ecosystems. An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable. In realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.

Scientific Investigations Report↗

Access to bird population data

Access to bird population data is critical for effective conservation planning and implementation. Although a tremendous volume of baseline data exists, it is often diffusely distributed and inaccessible to the resource manager and decision maker. A mechanism that facilitates assembly, documentation and delivery of avian data in a user-friendly manner is needed in order to integrate bird-related information resources across agencies and organizations. To address this fundamental need, the National Biological Information Infrastructure (NBII), in partnership with the U.S. Geological Survey's Patuxent Wildlife Research Center and the U.S. Fish and Wildlife Service, is developing a web-based interactive system that will focus on access to bird population and habitat data used in bird management and conservation. This system, known as the NBII Bird Conservation Node, will support planning and evaluation of bird conservation activities within the context of the North American Bird Conservation Initiative (NABCI), a framework for collaboration among organizations interested in bird conservation across North America. Initial development of the NBII Bird Conservation Node will focus on creating a prototype mapping application that will provide interactive access to data from the North American Breeding Bird Survey, the Colonial Waterbird Survey, the Breeding Waterfowl Population and Habitat Survey, and the Atlantic Flyway Mid-winter Waterfowl Survey. This prototype mapping application, to be available on-line at http://www.nbii.gov by Sep 2001, will lay the foundation for establishment of a Migratory Bird Data Center at Patuxent Wildlife Research Center, and will provide an opportunity for linking to and establishing partnerships with other sources of bird population and habitat data available over the Internet.

Book chapter↗

Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health

Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.

Idaho, Wyoming, Montana↗

A metadata checklist and data formatting guidelines to make eDNA FAIR (Findable, Accessible, Interoperable and Reusable)

The success of environmental DNA (eDNA) approaches for species detection has revolutionized biodiversity monitoring and distribution mapping. Targeted eDNA amplification approaches, such as quantitative PCR, have improved our understanding of species distribution, and metabarcoding-based approaches have enabled biodiversity assessment at unprecedented scales and taxonomic resolution. eDNA datasets, however, are often scattered across repositories with inconsistent formats, varying access restrictions, and inadequate metadata; this limits their interoperation, reuse, and overall impact. Adopting FAIR (Findable, Accessible, Interoperable, and Reusable) data practices with eDNA data can transform the monitoring of biodiversity and individual species and support data-driven biodiversity management across broad scales. FAIR practices remain underdeveloped in the eDNA community, partly due to gaps in adapting existing vocabularies, such as Darwin Core (DwC) and Minimum Information about any (x) Sequence (MIxS), to eDNA-specific needs and workflows. To address these challenges, we propose a comprehensive FAIR eDNA (FAIRe) Metadata Checklist, which integrates existing data standards and introduces new terms tailored to eDNA workflows. Metadata are systematically linked to both raw data (e.g., metabarcoding sequences, Ct/Cq values of targeted qPCR assays) and derived biological observations (e.g., Amplicon Sequence Variant (ASV)/Operational Taxonomic Unit (OTU) tables, species presence/absence). Along with formatting guidelines, tools, templates, and example datasets, we introduce a standardized, ready-to-use approach for FAIR eDNA practices. Through broad collaboration, we seek to integrate these guidelines into established biodiversity and molecular data standards, promote journal data policies, and foster user-driven improvements and uptake of FAIR practices among eDNA data producers. In proposing this standardized approach and developing a long-term plan with key databases and data standard organizations, the goal is to enhance accessibility, maximize reuse, and elevate the scientific impact of these valuable biodiversity data resources.

Environmental DNA↗

Contribution of fish community metrics to the index of biotic integrity in two Ozark rivers

Flow regime, energy source, water quality, and habitat structure within stream systems play major roles in controlling the structure of resident fish communities (Gorman and Karr, 1978; Karr and Dudley, 1981; Schlosser, 1982). Based on the hypothesis that there are predictable relationships between stream variables and fish community structure, overall stream condition has been assessed using indices such as the Index of Biotic Integrity (IBI) (Karr, 1981; Karr et al., 1986). The IBI integrates a wide range of information pertaining to fish community structure that reflects the prevailing chemical, physical, and biological condition of the stream. Assessments made using the IBI have been correlated to actual stream conditions and appear to be sensitive to a wide range of environmental degradation (Fausch et al., 1990).

Ecological Indicators↗

Green River oil shale—concept of origin extended: An interdisciplinary problem being attacked from both ends

A much fuller understanding of the Green River oil shale and its organic chemistry will emerge when the geologists, paleontologists, organic chemists, biologists, paleolimnologists, and biogeochemists, who are now working on it, integrate their findings with those of the others. We know from the geology, paleontology, and paleolimnology that the biologic progenitors of the organic substance in the Green River oil shale could only have been microscopic algae, and other micro-organisms, that grew and accumulated in the central parts of large, shallow lakes that existed under a subtropical climate. The only nonlacustrine organic components were wind-blown, or water borne, pollens and waxy spores. These, however, made up a large and important part of the organic-rich sediment. The geology of the Green River Formation shows that as the algal and pollen-rich sediment was buried deeper and deeper, progressively more of its pore water and dissolved constituents were expressed. Static pressures may have reached as much as 210 kg cm −2 , and the ambient temperature rose, with depth, to somewhere within the range between 90° and 125° C. Beneath the ancient lakes a tectonically quiescent environment persisted for tens of millions of years after their organic sediments had been deeply buried. The organic material of the Green River oil shale can be divided into three fractions—a small bitumen fraction that is extractable with common organic solvents, a major fraction called koerogen that consists of insoluble pyrobitumens, and a somewhat smaller inert fraction that is neither soluble nor does it yield oil on pyrolysis. As all three fractions originated in the same algal, pollen-rich sediment, an explanation for their marked differences must be sought in their geochemical history or from a study of the modern analogues of their progenitors. The components of the bitumen fraction consisted of “biological markers” that were inherited from the Eocene plants and animals in which they originally formed. Diagenesis has changed these hydrogen-rich compounds, but not enough to obscure their provenance. Kerogen presumably became insoluble because its hydrogen-rich components polymerized. My speculation is that the inert fraction was derived from a polyphenolic substance produced in the original algal ooze by “non-enzymatic browning.” Only three Classes of non-marine algae need be considered as progenitors of the Green River oil shale; the Xanthophyceae, the Chlorophyceae, and the Cyanophyceae. Only the Cyanophyceae (the blue-green algae) meet the biologic and paleontologic requirements to have served as the dominant precursors of the Green River oil shale. Several other oil shales clearly were derived from the Xanthophyceae, specifically Botryococcus. The blue-green algal ooze now forming, and accumulating, in Mud Lake, Florida, has been studied biologically and chemically as a possible present-day analogue of the Green River oil shale precursor. In this small lake we have established the fact that a bacterial inhibitor is produced, which inhibits decay of the algae and thereby permits the accumulation of energy-rich organic compounds. We infer that a similar indigenous inhibitor must have acted in the Eocene lakes to permit them to become the huge energy sinks they were. Studies of the organic chemistry of living blue-green algae show that they contain appreciable percentages of fatty acids, hydrocarbons, and very large percentages of proteins. These promising, energy-rich compounds could serve as source materials for potential conversion into oil shale in the geologic future. Certain marine anaerobic bacteria convert fatty acids into aliphatic hydrocarbons. Fresh-water obligate anaerobes should be investigated to see if they also convert fatty acids into hydrocarbons. The part played by aquatic animals that live in, or on, freshwater algal ooze in synthesizing hydrocarbons has not been investigated, but deserves attention. Pollen grains, of course, must be considered an important precursor of hydrocarbons produced on pyrolysis. They contain far higher percentages of long chain hydrocarbons and alcohols than most plant materials. The major problem ahead is to account for the progressive hydrogenation and subsequent polymerization of the relatively oxygen-rich constituents of algae such as the polysaccharides, amino acids, ammo sugars, and fatty acids into the insoluble pyrobitumens that constitute, particularly, the kerogen fraction of the Green River oil shale.

Wyoming↗

Sources of global climate data and visualization portals

Climate is integral to the geophysical foundation upon which ecosystems are structured. Knowledge about mechanistic linkages between the geophysical and biological environments is essential for understanding how global warming may reshape contemporary ecosystems and ecosystem services. Numerous global data sources spanning several decades are available that document key geophysical metrics such as temperature and precipitation, and metrics of primary biological production such as vegetation phenology and ocean phytoplankton. This paper provides an internet directory to portals for visualizing or servers for downloading many of the more commonly used global datasets, as well as a description of how to write simple computer code to efficiently retrieve these data. The data are broadly useful for quantifying relationships between climate, habitat availability, and lower-trophic-level habitat quality - especially in Arctic regions where strong seasonality is accompanied by intrinsically high year-to-year variability. If defensible linkages between the geophysical (climate) and the biological environment can be established, general circulation model (GCM) projections of future climate conditions can be used to infer future biological responses. Robustness of this approach is, however, complicated by the number of direct, indirect, or interacting linkages involved. For example, response of a predator species to climate change will be influenced by the responses of its prey and competitors, and so forth throughout a trophic web. The complexities of ecological systems warrant sensible and parsimonious approaches for assessing and establishing the role of natural climate variability in order to substantiate inferences about the potential effects of global warming.

Conference Paper↗

[Book review] Endangered Birds: Management techniques for preserving threatened species

This volume contains the proceedings of a symposium held at the University of Wisconsin-Madison, during 17-20 August 1977. In the words of the book's editor, Stanley Temple, the symposium "offered a unique opportunity for leading scientists from around the world to meet and share their experiences with endangered birds." That this objective was achieved is indicated by a list of contributors standing as a "Who's Who" in endangered species research and management. This symposium was jointly sponsored by the International Council for Bird Preservation, World Wildlife Fund, National Audubon Society, New York Zoological Society, and U.S. Fish and Wildlife Service. Fifty-one papers by 53 contributors are listed under 10 topics: endangered bird problems and the concept of managing threatened species; increasing reproductive effort and success by reducing nest-site limitations; alleviating problems of competition, predation, parasitism, and disease; supplemental feeding and manipulation of feeding ecology; manipulating aspects of nesting biology; captive breeding of endangered birds; genetic aspects of managing dwindling populations; reintroducing endangered birds to the wild; integrated approaches to management of endangered birds; and summary. A 14-page index provides convenient reference to subjects in the text.

The Auk↗

Extending ordinal regression with a latent zero-augmented beta distribution

Ecological abundance data are often recorded on an ordinal scale in which the lowest category represents species absence. One common example is when plant species cover is visually assessedwithin bounded quadrats and then assigned to pre-defined cover class categories.We present an ordinal beta hurdle model that directly models ordinal category probabilitieswith a biologically realistic beta-distributed latent variable.Ahurdle-at-zero model allows ecologists to explore distribution (absence) and abundance processes in an integrated framework. This provides an alternative to cumulative link models when data are inconsistent with the assumption that the odds ofmoving into a higher category are the same for all categories (proportional odds). Graphical tools and a deviance information criterion were developed to assess whether a hurdle-at-zero model should be used for inferences rather than standard ordinal methods. Hurdle-at-zero and non-hurdle ordinal models fit to vegetation cover class data produced substantially different conclusions.The ordinal beta hurdle model yielded more precise parameter estimates than cumulative logit models, although out-of-sample predictions were similar. The ordinal beta hurdle model provides inferences directly on the latent biological variable of interest, percent cover, and supports exploration of more realistic ecological patterns and processes through the hurdle-at-zero or two-part specification.We provide JAGS code as an on-line supplement. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗

Linking the agricultural landscape of the Midwest to stream health with structural equation modeling

Multiple physical and chemical stressors can simultaneously affect the biological condition of streams. To better understand the complex interactions of land-use practices, water quality, and ecological integrity of streams, the U.S. Geological Survey National Water Quality Assessment Project is conducting regional-scale assessments of stream condition across the United States. In the summer of 2013, weekly water samples were collected from 100 streams in the Midwestern United States. Employing watershed theory, we used structural equation modeling (SEM) to represent a general hypothesis for how 16 variables (previously identified to be important to stream condition) might be inter-related. Again, using SEM, we evaluated the ability of this “stressor network” to explain variations in multimetrics of algal, invertebrate, and fish community health, trimming away any environmental variables not contributing to an explanation of the ecological responses. Seven environmental variables—agricultural and urban land use, sand content of soils, basin area, percent riparian area as forest, channel erosion, and relative bed stability—were found to be important for all three-community metrics. The algal and invertebrate models included water-chemistry variables not included in the fish model. Results suggest that ecological integrity of Midwest streams are affected by both agricultural and urban land uses and by the natural geologic setting, as indicated by the sand content of soils. Chemicals related to crops (pesticides and nutrients) and residential uses (pyrethroids) were found to be more strongly related to ecological integrity than were natural factors (riparian forest, watershed soil character).

Environmental Science & Technology↗

The future is now: Amplicon sequencing and sequence capture usher in the conservation genomics era

The genomics revolution has initiated a new era of population genetics where genome-wide data are frequently used to understand complex patterns of population structure and selection. However, the application of genomic tools to inform management and conservation has been somewhat rare outside a few well studied species. Fortunately, two recently developed approaches, amplicon sequencing and sequence capture, have the potential to significantly advance the field of conservation genomics. Here, amplicon sequencing refers to highly multiplexed PCR followed by high-throughput sequencing (e.g., GTseq), and sequence capture refers to using capture probes to isolate loci from reduced-representation libraries (e.g., Rapture). Both approaches allow sequencing of thousands of individuals at relatively low costs, do not require any specialized equipment for library preparation, and generate data that can be analyzed without sophisticated computational infrastructure. Here, we discuss the advantages and disadvantages of each method and provide a decision framework for geneticists who are looking to integrate these methods into their research programme. While it will always be important to consider the specifics of the biological question and system, we believe that amplicon sequencing is best suited for projects aiming to genotype <500 loci on many individuals (>1,500) or for species where continued monitoring is anticipated (e.g., long-term pedigrees). Sequence capture, on the other hand, is best applied to projects including fewer individuals or where >500 loci are required. Both of these techniques should smooth the transition from traditional genetic techniques to genomics, helping to usher in the conservation genomics era.

Molecular Ecology Resources↗

Progress on water data integration and distribution: a summary of select U.S. Geological Survey data systems

Critical water-resources issues ranging from flood response to water scarcity make access to integrated water information, services, tools, and models essential. Since 1995 when the first water data web pages went online, the U.S. Geological Survey has been at the forefront of water data distribution and integration. Today, real-time and historical streamflow observations are available via web pages and a variety of web service interfaces. The Survey has built partnerships with Federal and State agencies to integrate hydrologic data providing continuous observations of surface and groundwater, temporally discrete water quality data, groundwater well logs, aquatic biology data, water availability and use information, and tools to help characterize the landscape for modeling. In this paper, we summarize the status and design patterns implemented for selected data systems. We describe how these systems contribute to a U.S. Federal Open Water Data Initiative and present some gaps and lessons learned that apply to global hydroinformatics data infrastructure.

Journal of Hydroinformatics↗

Hydrogeomorphic features mediate the effects of land use/cover on reservoir productivity and food webs

Although effects of land use/cover on nutrient concentrations in aquatic systems are well known, half or more of the variation in nutrient concentration remains unexplained by land use/cover alone. Hydrogeomorphic (HGM) landscape features can explain much remaining variation and influence food web interactions. To explore complex linkages among land use/cover, HGM features, reservoir productivity, and food webs, we sampled 11 Ohio reservoirs, ranging broadly in agricultural catchment land use/cover, for 3 years. We hypothesized that HGM features mediate the bottom-up effects of land use/cover on reservoir productivity, chlorophyll a , zooplankton, and recruitment of gizzard shad, an omnivorous fish species common throughout southeastern U.S. reservoirs and capable of exerting strong effects on food web and nutrient dynamics. We tested specific hypotheses using a model selection approach. Percent variation explained was highest for total nitrogen ( R 2 = 0.92), moderately high for total phosphorus, chlorophyll a , and rotifer biomass ( R 2 = 0.57 to 0.67), relatively low for crustacean zooplankton biomass and larval gizzard shad hatch abundance ( R 2 = 0.43 and 0.42), and high for larval gizzard shad survivor abundance ( R 2 = 0.79). The trophic status models included agricultural land use/cover and an HGM predictor, whereas the zooplankton models had few HGM predictors. The larval gizzard shad models had the highest complexity, including more than one HGM feature and food web components. We demonstrate the importance of integrating land use/cover, HGM features, and food web interactions to investigate critical interactions and feedbacks among physical, chemical, and biological components of linked land-water ecosystems.

Limnology and Oceanography↗

Integrated science and interdisciplinary research for parks and protected areas

This paper summarizes presentations and discussion that focused on integrated science and the use of interdisciplinary research during a panel session held at the George Wright Society Meeting in New Orleans, March 14, 2011. The panel brought together nationally recognized members from the social and biological scientific communities, along with decision-makers and managers of parks and protected areas. The goal of the panel was to spark a discussion among panel members and the audience on the benefits and challenges of utilizing interdisciplinary research and integrated science to answer complex questions at the international, national, regional, and local park levels. The key focus points for the panel presentations and subsequent discussion revolved around the following five questions: How do we define interdisciplinary research and integrated science? What are the benefits, drawbacks, and challenges of interdisciplinary research and integrated science? When should this type of science be used? What are the barriers to employing interdisciplinary research and integrated science, and how can those perceived barriers be overcome? What are some examples of situations where this type of science has worked for you? We will break this paper down into seven sections, each summarizing the results of the panel presentations and subsequent audience discussion: 1. How do we define integrated science and interdisciplinary research? 2. The need for integrated science. 3. The challenges of integrated science and interdisciplinary research. 4. The benefits of integrated science and interdisciplinary research. 5. Integrated science in protected area management. 6. Communicating integrated science. 7. Key components to improve scientific outcomes.

Conference Paper↗