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Review of: An Illustrated Guide to Theoretical Ecology. Ted J. Case. Oxford University Press, New York, 2000. 460 pp., illus. $45.00 (ISBN 0-19-508512-4 paper).
A continuing discussion in applied and theoretical ecology focuses on the relationship of different organisational levels and on how ecological systems interact across scales. We address principal approaches to cope with complex across-level issues in ecology by applying elements of hierarchy theory and the theory of complex adaptive systems. A top-down approach, often characterised by the use of statistical techniques, can be applied to analyse large-scale dynamics and identify constraints exerted on lower levels. Current developments are illustrated with examples from the analysis of within-community spatial patterns and large-scale vegetation patterns. A bottom-up approach allows one to elucidate how interactions of individuals shape dynamics at higher levels in a self-organisation process; e.g., population development and community composition. This may be facilitated by various modelling tools, which provide the distinction between focal levels and resulting properties. For instance, resilience in grassland communities has been analysed with a cellular automaton approach, and the driving forces in rodent population oscillations have been identified with an agent-based model. Both modelling tools illustrate the principles of analysing higher level processes by representing the interactions of basic components.The focus of most ecological investigations on either top-down or bottom-up approaches may not be appropriate, if strong cross-scale relationships predominate. Here, we propose an 'across-scale-approach', closely interweaving the inherent potentials of both approaches. This combination of analytical and synthesising approaches will enable ecologists to establish a more coherent access to cross-level interactions in ecological systems. ?? 2010 Gesellschaft f??r ??kologie.
The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.
Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long-term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi-population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub-models. We present a case study in which we customized this model for historical population monitoring data (1985–2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi-pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi-population viability analysis bridges the gap between site-level field observations and population-level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.
Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.
We use modeling to determine the optimal relative plant carbon allocations between foliage, fine roots, anti-herbivore defense, and reproduction to maximize reproductive output. The model treats these plant components and the herbivore compartment as variables. Herbivory is assumed to be purely folivory. Key external factors include nutrient availability, degree of shading, and intensity of herbivory. Three alternative functional responses are used for herbivory, two of which are variations on donor-dependent herbivore (models 1a and 1b) and one of which is a Lotka–Volterra type of interaction (model 2). All three were modified to include the negative effect of chemical defenses on the herbivore. Analysis showed that, for all three models, two stable equilibria could occur, which differs from most common functional responses when no plant defense component is included. Optimal strategies of carbon allocation were defined as the maximum biomass of reproductive propagules produced per unit time, and found to vary with changes in external factors. Increased intensity of herbivory always led to an increase in the fractional allocation of carbon to defense. Decreases in available limiting nutrient generally led to increasing importance of defense. Decreases in available light had little effect on defense but led to increased allocation to foliage. Decreases in limiting nutrient and available light led to decreases in allocation to reproduction in models 1a and 1b but not model 2. Increases in allocation to plant defense were usually accompanied by shifts in carbon allocation away from fine roots, possibly because higher plant defense reduced the loss of nutrients to herbivory.
A cellular automata model was developed and parameterized to test the effectiveness of application of biological control insects to water hyacinth ( Pontederia crassipes ), which is an invasive floating plant species in many parts of the world and outcompetes many submersed native aquatic species in southern Florida. In the model, P. crassipes was allowed to compete with Nuttall’s waterweed ( Elodea nuttallii ). In the absence of biocontrol acting on the P. crassipes , E. nuttallii excluded P. crassipes at low concentrations of the limiting nutrient (nitrogen), and the reverse occurred at high nutrient concentrations. At intermediate values, alternative stable states could occur; either P. crassipes alone or a mixture of the two species. When the biocontrol agent, the weevil Neochetina eichhorniae , was applied in the model, there was initially a rapid reduction of the P. crassipes , however, over time a regular striped pattern of moving spatially alternating stripes of P. crassipes and E. nuttallii emerged. -This pattern of moving stripes emerged and persisted over thousands of days but could quickly transform into an irregular pattern at some apparently random time, when either external stochasticity (added adult weevils) or only the weak internal stochasticity of weevil movements occurred. The cause of the end of the long transient can be traced to a single slightly irregular pixel within the striped pattern. Model parameters were varied to study effects of plant growth rate, nutrient concentration and nutrient diffusion rate on the dynamics of the system.
Upstream migrating fish must balance energetic costs with environmental and ecological constraints. Theoretical models predict that fish can conserve energy by adjusting swim speed in response to changing water velocity, but detailed field tests of these predictions remain rare. We used fine-scale acoustic telemetry and hydrodynamic modeling to track adult sea lamprey (Petromyzon marinus) migrating through a river and evaluated how movement speeds varied with environmental conditions and individual traits. Swim speed increased with faster water velocities, while ground speed remained constant, supporting the hypothesis that lampreys regulate swim effort to maintain a fixed migration pace. The average ground speed aligned with a predicted optimum of ∼1 body length per second. Both swim and ground speeds declined slightly in deeper water, suggesting a trade-off between movement efficiency and predator avoidance. These findings provide field-based support for a theoretical model of energy optimization and underscore how migratory fish adaptively adjust behavior to local hydrodynamic conditions. Our results highlight the value of using high-resolution tracking and environmental modeling to reveal behavioral tactics that underlie efficient migration strategies.
Identification of ecological thresholds is important both for theoretical and applied ecology. Recently, Baker and King (2010, King and Baker 2010) proposed a method, threshold indicator analysis (TITAN), to calculate species and community thresholds based on indicator species scores adapted from Dufrêne and Legendre (1997). We tested the ability of TITAN to detect thresholds using models with (broken-stick, disjointed broken-stick, dose-response, step-function, Gaussian) and without (linear) definitive thresholds. TITAN accurately and consistently detected thresholds in step-function models, but not in models characterized by abrupt changes in response slopes or response direction. Threshold detection in TITAN was very sensitive to the distribution of 0 values, which caused TITAN to identify thresholds associated with relatively small differences in the distribution of 0 values while ignoring thresholds associated with large changes in abundance. Threshold identification and tests of statistical significance were based on the same data permutations resulting in inflated estimates of statistical significance. Application of bootstrapping to the split-point problem that underlies TITAN led to underestimates of the confidence intervals of thresholds. Bias in the derivation of the z-scores used to identify TITAN thresholds and skewedness in the distribution of data along the gradient produced TITAN thresholds that were much more similar than the actual thresholds. This tendency may account for the synchronicity of thresholds reported in TITAN analyses. The thresholds identified by TITAN represented disparate characteristics of species responses that, when coupled with the inability of TITAN to identify thresholds accurately and consistently, does not support the aggregation of individual species thresholds into a community threshold.
Although parasites represent an important component of ecosystems, few field and theoretical studies have addressed the structure of parasites in food webs. We evaluate the structure of parasitic links in an extensive salt marsh food web, with a new model distinguishing parasitic links from non-parasitic links among free-living species. The proposed model is an extension of the niche model for food web structure, motivated by the potential role of size (and related metabolic rates) in structuring food webs. The proposed extension captures several properties observed in the data, including patterns of clustering and nestedness, better than does a random model. By relaxing specific assumptions, we demonstrate that two essential elements of the proposed model are the similarity of a parasite's hosts and the increasing degree of parasite specialization, along a one-dimensional niche axis. Thus, inverting one of the basic rules of the original model, the one determining consumers' generality appears critical. Our results support the role of size as one of the organizing principles underlying niche space and food web topology. They also strengthen the evidence for the non-random structure of parasitic links in food webs and open the door to addressing questions concerning the consequences and origins of this structure.
Despite the wide use of ecological regions in conservation and resource-management evaluations and assessments, a commonly accepted theoretical basis for ecological regionalization does not exist. This fact, along with the paucity of focus on ecological regionalization by professional associations, journals, and faculties, has inhibited the advancement of a broadly acceptable scientific basis for the development, use, and verification of ecological regions. The central contention of this article is that ecological regions should improve our understanding of geographic and ecological phenomena associated with biotic and abiotic processes occurring in individual regions and also of processes characteristic of interactions and dependencies among multiple regions. Research associated with any ecoregional framework should facilitate development of hypotheses about ecological phenomena and dominant landscape elements associated with these phenomena, how these phenomena are structured in space, and how they function in a hierarchy. Success in addressing the research recommendations outlined in this article cannot occur within an ad hoc, largely uncoordinated research environment. Successful implementation of this plan will require activities--coordination, funding, and education--that are both scientific and administrative in nature. Perhaps the most important element of an infrastructure to support the scientific work of ecoregionalization would be a national or international authority similar to the Water and Science Technology Board of the National Academy of Sciences.
1. Birds that migrate long distances use stopover sites to optimize fuel loads and complete migration as quickly as possible. Stopover use has been predicted to facilitate a time-minimization strategy in land migrants as well, but empirical tests have been lacking, and alternative migration strategies have not been considered. 2. We used fine-scale movement data to evaluate the ecological role of stopovers in migratory mule deer Odocoileus hemionus — a land migrant whose fitness is strongly influenced by energy intake rather than migration speed. 3. Although deer could easily complete migrations (range 18–144 km) in several days, they took an average of 3 weeks and spent 95% of that time in a series of stopover sites that had higher forage quality than movement corridors. Forage quality of stopovers increased with elevation and distance from winter range. Mule deer use of stopovers corresponded with a narrow phenological range, such that deer occupied stopovers 44 days prior to peak green-up, when forage quality was presumed to be highest. Mule deer used one stopover for every 5∙3 and 6∙7 km travelled during spring and autumn migrations, respectively, and used the same stopovers in consecutive years. 4. Study findings indicate that stopovers play a key role in the migration strategy of mule deer by allowing individuals to migrate in concert with plant phenology and maximize energy intake rather than speed. Our results suggest that stopover use may be more common among non-avian taxa than previously thought and, although the underlying migration strategies of temperate ungulates and birds are quite different, stopover use is important to both. 5. Exploring the role of stopovers in land migrants broadens the scope of stopover ecology and recognizes that the applied and theoretical benefits of stopover ecology need not be limited to avian taxa.
A model could be defined as any abstraction from reality that is used to provide some insight into the real system. In this discussion, we will use a more specific definition that a model is a set of rules or assumptions, expressed as mathematical equations, that describe how animals survive and reproduce, including the external factors that affect these characteristics. A model simplifies a system, retaining essential components while eliminating parts that are not of interest. ecology has a rich history of using models to gain insight into populations, often borrowing both model structures and analysis methods from demographers and engineers. Much of the development of the models has been a consequence of mathematicians and physicists seeing simple analogies between their models and patterns in natural systems. Consequently, one major application of ecological modeling has been to emphasize the analysis of dynamics of often complex models to provide insight into theoretical aspects of ecology. 1
Resource tracking, where animals increase energy gain by moving to track phenological variation in resources across space, is emerging as a fundamental attribute of animal movement ecology. However, a theoretical framework to understand when and where resource tracking should occur, and how resource tracking should lead to emergent ecological patterns, is lacking. We present a framework that unites concepts from optimal foraging theory and landscape ecology, which can be used to generate and test predictions on how resource dynamics shape animal movement across taxa, systems, and scales. Consideration of the interplay between animal movement and resource dynamics not only advances ecological understanding but can also guide biodiversity conservation in an era of global change.
Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the United States. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns. Here, we present a spatial modeling framework for mapping ecological sites using machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology). Based on the theoretical association between ecological sites and landscape biophysical properties, we hypothesized that the spatial distribution of ecological sites could be predicted using readily available geospatial data. This modeling approach was tested at two study areas within the western United States, representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert. Results show our approach was effective in mapping grouped ecological site classes (ESGs), with 10-fold cross-validation accuracies of 70% in the Colorado Plateau based on 1405 point observations across eight expertly-defined ESG classes and 79% in the Chihuahuan Desert based on 2589 point observations across nine expertly-defined ESG classes. Model accuracies were also evaluated using external-validation datasets; resulting in 56 and 44% correct classification for the Colorado Plateau and Chihuahuan Desert, respectively. National coverage of the training and covariate data used in this study provides opportunities for a consistent national-scale mapping effort of ecological sites.
Optimal control theory is finding increased application in both theoretical and applied ecology, and it is a central element of adaptive resource management. One of the steps in an adaptive management process is to develop alternative models of system dynamics, models that are all reasonable in light of available data, but that differ substantially in their implications for optimal control of the resource. We explored how the form of the recruitment and survival functions in a general population model for ducks affected the patterns in the optimal harvest strategy, using a combination of analytical, numerical, and simulation techniques. We compared three relationships between recruitment and population density (linear, exponential, and hyperbolic) and three relationships between survival during the nonharvest season and population density (constant, logistic, and one related to the compensatory harvest mortality hypothesis). We found that the form of the component functions had a dramatic influence on the optimal harvest strategy and the ultimate equilibrium state of the system. For instance, while it is commonly assumed that a compensatory hypothesis leads to higher optimal harvest rates than an additive hypothesis, we found this to depend on the form of the recruitment function, in part because of differences in the optimal steady-state population density. This work has strong direct consequences for those developing alternative models to describe harvested systems, but it is relevant to a larger class of problems applying optimal control at the population level. Often, different functional forms will not be statistically distinguishable in the range of the data. Nevertheless, differences between the functions outside the range of the data can have an important impact on the optimal harvest strategy. Thus, development of alternative models by identifying a single functional form, then choosing different parameter combinations from extremes on the likelihood profile may end up producing alternatives that do not differ as importantly as if different functional forms had been used. We recommend that biological knowledge be used to bracket a range of possible functional forms, and robustness of conclusions be checked over this range.
In this chapter, we demonstrate the application of the various classes of thresholds, detailed in earlier chapters and elsewhere, via an actual but simplified natural resource management case study. We intend our example to provide the reader with the ability to recognize and apply the theoretical concepts of utility, ecological and decision thresholds to management problems through a formalized decision-analytic process. Our case study concerns the management of human recreational activities in Alaska’s Denali National Park, USA, and the possible impacts of such activities on nesting Golden Eagles, Aquila chrysaetos. Managers desire to allow visitors the greatest amount of access to park lands, provided that eagle nesting-site occupancy is maintained at a level determined to be acceptable by the managers themselves. As these two management objectives are potentially at odds, we treat minimum desired occupancy level as a utility threshold which, then, serves to guide the selection of annual management alternatives in the decision process. As human disturbance is not the only factor influencing eagle occupancy, we model nesting-site dynamics as a function of both disturbance and prey availability. We incorporate uncertainty in these dynamics by considering several hypotheses, including a hypothesis that site occupancy is affected only at a threshold level of prey abundance (i.e., an ecological threshold effect). By considering competing management objectives and accounting for two forms of thresholds in the decision process, we are able to determine the optimal number of annual nesting-site restrictions that will produce the greatest long-term benefits for both eagles and humans. Setting a utility threshold of 75 occupied sites, out of a total of 90 potential nesting sites, the optimization specified a decision threshold at approximately 80 occupied sites. At the point that current occupancy falls below 80 sites, the recommended decision is to begin restricting access to humans; above this level, it is recommended that all eagle territories be opened to human recreation. We evaluated the sensitivity of the decision threshold to uncertainty in system dynamics and to management objectives (i.e., to the utility threshold).
Using ecological criteria as a theoretical framework, we describe the steps involved in designing a network of marine reserves for conservation and fisheries management. Although we describe the case study of the Channel Islands, the approach to marine reserve design may be effective in other regions where traditional management alone does not sustain marine resources. A group of agencies, organizations, and individuals established clear goals for marine reserves in the Channel Islands, including conservation of ecosystem biodiversity, sustainable fisheries, economic viability, natural and cultural heritage, and education. Given the constraints of risk management, experimental design, monitoring, and enforcement, scientists recommended at least one, but no more than four, reserves in each biogeographic region. In general, the percentage of an area to be included in a reserve network depends on the goals. In the Channel Islands, after consideration of both conservation goals and the risk from human threats and natural catastrophes, scientists recommended reserving an area of 30–50% of all representative habitats in each biogeographic region. For most species of concern, except pinnipeds and seabirds, information about distributions, dispersal, and population growth was limited. As an alternative to species distribution information, suitable habitats for species of concern were used to locate potential reserve sites. We used a simulated annealing algorithm to identify potential reserve network scenarios that would represent all habitats within the smallest area possible. The analysis produced an array of potential reserve network scenarios that all met the established goals.