Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

A natural river system is organized as a nested hierarchy of interconnected habitats with specific environmental conditions to which the biological community has adapted. Due to this hierarchical structure, identifying the role of different stressors on the biological community is a formidable task. Efforts trying to link stressors to biological integrity have always been bound to the geographic scale of the selected study area, leading to scale-specific results. In this research, an attempt is made to lift this limitation and develop a hierarchical, scale-sensitive methodology that can identify the significant environmental stressors to the biological community at different scales. Sites with similar background environmental conditions are clustered using self-organizing maps (SOM). This is used to identify stressors which affect the biological community throughout the area of study - called environmental gradients or large-scale stressors. Subsequently, these clusters of similar observations (sampling sites) are progressively sub-divided using environmental variables with a significant but localized effect on the biological community - called small-scale stressors. A parent group of sites is split only when the resulting sub-groups have significantly different biological responses. At the end of this recursive sites decomposition procedure, the original set of observations is organized as a tree of environmentally homogeneous groups of observations characterized by unique biological responses to multiple stressors with different geographic extents. The developed hierarchical analysis methodology has been validated using a large-size dataset of environmental observations from the State of Ohio. Our results show that habitat degradation and increased nutrient loading are the large-scale stressors with a widespread impact in Ohio. Other stressors, such as heavy metals, pH or nitrate concentrations have significant albeit localized effects on biological integrity.     

Using the ECLPSS software environment to build a spatially explicit component-based model of ozone effects on forest ecosystems

We have developed a modeling framework to support grid-based simulation of ecosystems at multiple spatial scales, the Ecological Component Library for Parallel Spatial Simulation (ECLPSS). ECLPSS helps ecologists to build robust spatially explicit simulations of ecological processes by providing a growing library of reusable interchangeable components and automating many modeling tasks. To build a model, a user selects components from the library, and then writes new components as needed. Some of these components represent specific ecological processes, such as how environmental factors influence the growth of individual trees. Other components provide simulation support such as reading and writing files in various formats to allow inter-operability with other software. The framework manages components and variables, the order of operations, and spatial interactions. The framework provides only simulation support; it does not include ecological functions or assumptions. This separation allows biologists to build models without becoming computer scientists, while computer scientists can improve the framework without becoming ecologists. The framework is designed to operate on multiple platforms and be used across networks via a World Wide Web-based user interface. ECLPSS is designed for use with both single processor computers for small models, and multiple processors in order to simulate large regions with complex interactions among many individuals or ecological compartments. To test Version 1.0 of ECLPSS, we created a model to evaluate the effect of tropospheric ozone on forest ecosystem dynamics. This model is a reduced-form version of two existing models: , which represents an individual tree, and , which represents forest stand growth and succession. This model demonstrates key features of ECLPSS, such as the ability to examine the effects of cell size and model structure on model predictions.     

Statistical inference on patch-specific survival and movement rates from marked animals

Empirical estimates of patch-specific survival and movement rates are needed to parametrize spatially explicit population models, and for inference on the effects of habitat quality and fragmentation on populations. Data from radio-marked animals, in which both the fates and habitat locations of animals are known over time, can be used in conjunction with continuous-time proportional hazards models to obtain inferences on survival rates. Discrete-time conditional logistic models may provide inference on both survival and movement rates. We use Monte Carlo simulation to investigate accuracy of estimates of survival from both approaches, and movement rates from conditional logistic regression, for two habitats. Bias was low (relative bias < 0.04) and interval coverage accurate (close to the nominal 0.95) for estimates of habitat effect on survival based on proportional hazards. Bias was high ( relative bias 0.60) and interval coverage poor ( = 0.26 vs. nominal 0.95) for estimates of habitat effect based on conditional logistic regression; bias was especially influenced by heterogeneity in survival and the shape of the hazard function, whereas both bias and coverage were affected by ‘memory’ effects in movement patterns. Bias estimates of movement rate was low ( relative bias < 0.05), but interval coverage was poor ( = 0.48–0.80), possibly as a result of poor performance of a Taylor series estimate of variance. An example is provided from a radio-telemetry study of 47 wintering American woodcock (Scolopax minor), illustrating practical difficulties in field studies to parametrize these models. We also discuss extensions of continuous-time models to explicitly include a movement process, and further examine tradeoffs between continuous and discrete models.     

Nutritional geometry and macronutrient variation in the diets of gannets: the challenges in marine field studies

Foraging theory proposes that the nutritional driver of food choice and foraging in carnivores is energy gain. In contrast, recent laboratory experiments have shown that several species of carnivore select prey that provides a diet with a specific balance of macronutrients, rather than the highest energy content. It remains, however, to be determined how nutritionally variable the foods of predators in the wild are, and whether they feed selectively from available prey to balance their diet. Here, we used a geometric method named the right-angled mixture triangle (RMT) for examining nutritional variability in the prey and selected diets of a group of wild carnivores and marine top predators, the gannets (Morus spp.). A prey-level diet analysis was performed on Australasian gannets (M. serrator) from two New Zealand locations, and the macronutrient composition of their chosen prey species was measured. We use RMT to extend the comparison in the compositions of foods and diets from Australasian gannets from Australia as well as Northern gannets (M. bassanus) and Cape gannets (M. capensis). We found nutritional variability at multiple scales: intra- and interspecific variability in the pelagic fish and squid prey themselves; and intra- and interspecific variability in the diets consumed by geographically disparate populations of gannets. This nutritional variability potentially presents these predatory seabirds with both opportunity to select an optimal diet, and constraint if prevented from securing an optimal diet.     

Application of adaptive cluster sampling to low-density populations of freshwater mussels

Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.     

Stable lead isotope profiles in smelter and general urban communities: a comparison of environmental and blood measures

High-precision lead isotope ratios and lead concentrations have been compared statistically and graphically in women of child-bearing age (n = 77) from two smelter communities and one general urban community to evaluate the relative contributions to blood lead of tissue lead stores and lead from the contemporaneous environment (soil, floor dust, indoor airborne dust, water, food). Blood lead (PbB) contents were generally low (e.g. <10 g dL^–1). Statistically significant isotopic differences in blood and environmental samples were observed between the three cities although isotopic differences in blood for individual subjects living in close proximity (200 m radius) was as large as the differences within a city. No single environmental measure dominated the biological isotope profile and in many cases the low levels of blood lead meant that their isotopic profiles could be easily perturbed by relatively small changes of environmental exposure. Apportioning of sources using lead isotopes is possibly not feasible, nor cost effective, when blood lead levels are <5 g dL^–1. Interpretations based on statistical analyses of city-wide data do not give the same conclusions as when the houses are considered individually. Aggregating data from multiple subjects in a study such as this obscures potentially useful information. Most of the measures employed in this study, and many other similar studies, are markers of only short-to-medium integration of lead exposure. Serial sampling of blood and longer sampling times, especially for household variables, should provide more meaningful information.     

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?     

The relationship between diversity profiles,evenness and species richness based on partial ordering

Problems with the notion of evenness, such as ambiguity, proliferation of indices, choice of indices, etc. can be overcome by a more fundamental, mathematical approach. We show that the Lorenz curve is an adequate representation of evenness. The corresponding Lorenz order induces a partial order in the set of equivalent abundance vectors. Also diversity can adequately be studied through a partial order and represented by a curve derived from the classical Lorenz curve. This curve is known as the intrinsic diversity profile (or k-dominance curve) and was introduced by Patil and Taillie (1979) and Lambshead et al. (1981).     

Intraspecific variation in a predator affects community structure and cascading trophic interactions

Intraspecific phenotypic variation in ecologically important traits is widespread and important for evolutionary processes, but its effects on community and ecosystem processes are poorly understood. We use life history differences among populations of alewives, Alosa pseudoharengus, to test the effects of intraspecific phenotypic variation in a predator on pelagic zooplankton community structure and the strength of cascading trophic interactions. We focus on the effects of differences in (1) the duration of residence in fresh water (either seasonal or year-round) and (2) differences in foraging morphology, both of which may strongly influence interactions between alewives and their prey. We measured zooplankton community structure, algal biomass, and spring total phosphorus in lakes that contained landlocked, anadromous, or no alewives. Both the duration of residence and the intraspecific variation in foraging morphology strongly influenced zooplankton community structure. Lakes with landlocked alewives had small-bodied zooplankton year-round, and lakes with no alewives had large-bodied zooplankton year-round. In contrast, zooplankton communities in lakes with anadromous alewives cycled between large-bodied zooplankton in the winter and spring and small-bodied zooplankton in the summer. In summer, differences in feeding morphology of alewives caused zooplankton biomass to be lower and body size to be smaller in lakes with anadromous alewives than in lakes with landlocked alewives. Furthermore, intraspecific variation altered the strength of the trophic cascade caused by alewives. Our results demonstrate that intraspecific phenotypic variation of predators can regulate community structure and ecosystem processes by modifying the form and strength of complex trophic interactions.