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An Expanded Framework for Community Viability Analysis
Abstract
Community viability analysis (CVA) has been put forth as an analogue for population viability analysis (PVA), an accepted conservation tool for evaluating species-specific threat and management scenarios. The original proposal recommended that CVAs examine resistance-based questions. PVAs, however, are broadly applicable to multiple types of viability questions, suggesting that the original CVA definition may be too narrow. In the present article, we advance an expanded framework in which CVA includes any analysis assessing the status, threats, or management options of an ecological community. We discuss viability questions that can be investigated with CVA. We group those inquiries into categories of resistance, resilience, and persistence, and provide case studies for each. Finally, we broadly present the steps in a CVA.
... Investigating the interactions between species can be a key aspect of safeguarding biodiversity and promoting effective conservation efforts (Eichenwald & Reed, 2021;Sabo, 2008;Soulé et al., 2005). ...
... One promising approach to addressing these challenges is through community viability analysis (CVA) Ebenman & Jonsson, 2005;Eichenwald & Reed, 2021). CVA encompasses a variety of approaches to quantifying community structure, composition, and function in response to perturbations or management actions. ...
... CVA encompasses a variety of approaches to quantifying community structure, composition, and function in response to perturbations or management actions. For instance, by representing a community as a network of species interactions, researchers can identify species that play a significant role in community stability or resilience (Eichenwald & Reed, 2021;Jönsson & Thor, 2012), or identify effective management interventions (McDonald-Madden et al., 2016). ...
Desert communities are threatened with species loss due to climate change, and their resistance to such losses is unknown. We constructed a food web of the Mojave Desert terrestrial community (300 nodes, 4080 edges) to empirically examine the potential cascading effects of bird extinctions on this desert network, compared to losses of mammals and lizards. We focused on birds because they are already disappearing from the Mojave, and their relative thermal vulnerabilities are known. We quantified bottom‐up secondary extinctions and evaluated the relative resistance of the community to losses of each vertebrate group. The impact of random bird species loss was relatively low compared to the consequences of mammal (causing the greatest number of cascading losses) or reptile loss, and birds were relatively less likely to be in trophic positions that could drive top‐down effects in apparent competition and tri‐tropic cascade motifs. An avian extinction cascade with year‐long resident birds caused more secondary extinctions than the cascade involving all bird species for randomized ordered extinctions. Notably, we also found that relatively high interconnectivity among avian species has formed a subweb, enhancing network resistance to bird losses.
The idea that alternative stable states may exist in communities has been a recurring theme in ecology since the late 1960s, and is now experiencing a resurgence of interest. Since the first papers on the subject appeared, two perspectives have developed to describe how communities shift from one stable state to another. One assumes a constant environment with shifts in variables such as population density, and the other anticipates changes to underlying parameters or environmental “drivers”. We review the theory behind alternative stable states and examine to what extent these perspectives are the same, and in what ways they differ. We discuss the concepts of resilience and hysteresis, and the role of stochasticity within the two formulations. In spite of differences in the two perspectives, the same type of experimental evidence is required to demonstrate
the existence of alternative stable states.
Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.
Natural microbial communities contain hundreds to thousands of interacting species. For this reason, computational simulations are playing an increasingly important role in microbial ecology. In this manuscript, we present a new open-source, freely available Python package called Community Simulator for simulating microbial population dynamics in a reproducible, transparent and scalable way. The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples. To significantly speed up simulations using Community Simulator, our Python package implements a new Expectation-Maximization (EM) algorithm for finding equilibrium states of community dynamics that exploits a recently discovered duality between ecological dynamics and convex optimization. We present data showing that this EM algorithm improves performance by between one and two orders compared to direct numerical integration of the corresponding ordinary differential equations. We conclude by listing several recent applications of the Community Simulator to problems in microbial ecology, and discussing possible extensions of the package for directly analyzing microbiome compositional data.
Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships and the spatio‐temporal context of the study, providing predictive insights into community assembly processes from non‐manipulative observational data of species communities.
The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce H msc 3.0, a user‐friendly r implementation.
We illustrate the use of the package by applying H msc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio‐temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single‐species models, models of small communities, models of large species communities and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit models with different types of random effects, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates and how to make predictions. We further demonstrate how H msc 3.0 can be applied to normally distributed data, count data and presence–absence data.
The package, along with the extended vignettes, makes JSDM fitting and post‐processing easily accessible to ecologists familiar with r .
Changes in the frequency and severity of extreme weather may introduce new threats to species that are already under stress from gradual habitat loss and climate change. We provide a probabilistic framework that quantifies potential threats by applying concepts from ecological resilience to single populations. Our approach uses computation to compare disturbance–impacted projections to a population's normal range of variation, quantifying the full range of potential impacts. We illustrate this framework with projection models for coastal birds, which are commonly depicted as vulnerable to disturbances, especially hurricanes and oil spills. We found that populations of coastal specialists are resilient to extreme disturbances, with high resistance to the effects of short‐term reductions in vital rates and recovery within 20 years. Applying the general framework presented here across disturbance‐prone species and ecosystems would improve understanding of population resilience and generate specific projections of resilience that are needed for effective conservation planning.
Trophic rewilding has been suggested as a restoration tool to restore ecological interactions and reverse defaunation and its cascading effects on ecosystem functioning. One of the ecological processes that has been jeopardized by defaunation is animal-mediated seed dispersal. Here, we propose an approach that combines joint species distribution models with occurrence data and species interaction records to quantify the potential to restore seed-dispersal interactions through rewilding and apply it to the Atlantic Forest, a global biodiversity hotspot. Using this approach, we identify areas that should benefit the most from trophic rewilding and candidate species that could contribute to cash the credit of seed-dispersal interactions in a given site. We found that sites within large fragments bearing a great diversity of trees may have about 20 times as many interactions to be cashed through rewilding as small fragments in regions where deforestation has been pervasive. We also ranked mammal and bird species according to their potential to restore seed-dispersal interactions if reintroduced while considering the biome as a whole and at finer scales. The suggested approach can aid future conservation efforts in rewilding projects in defaunated tropical rainforests.
This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.
Understanding how changes in biodiversity will impact the stability and functioning of ecosystems is a central challenge in ecology. Food‐web approaches have been advocated to link community composition with ecosystem functioning by describing the fluxes of energy among species or trophic groups. However, estimating such fluxes remains problematic because current methods become unmanageable as network complexity increases. We developed a generalisation of previous indirect estimation methods assuming a steady state system [1, 2, 3]: the model estimates energy fluxes in a top‐down manner assuming system equilibrium; each node's losses (consumption and physiological) balances its consumptive gains. Jointly, we provide theoretical and practical guidelines to use the fluxweb R package (available on CRAN at https://cran.r-project.org/web/packages/fluxweb/index.html We also present how the framework can merge with the allometric theory of ecology [4] to calculate fluxes based on easily obtainable organism‐level data (i.e. body masses and species groups ‐eg, plants animals), opening its use to food webs of all complexities. Physiological losses (metabolic losses or losses due to death other than from predation within the food web) may be directly measured or estimated using allometric relationships based on the metabolic theory of ecology, and losses and gains due to predation are a function of ecological efficiencies that describe the proportion of energy that is used for biomass production. The primary output is a matrix of fluxes among the nodes of the food web. These fluxes can be used to describe the role of a species, a function of interest (e.g. predation; total fluxes to predators), multiple functions, or total energy flux (system throughow or multitrophic functioning). Additionally, the package includes functions to calculate network stability based on the Jacobian matrix, providing insight into how resilient the network is to small perturbations at steady state. Overall, fluxweb provides a flexible set of functions that greatly increase the feasibility of implementing food‐web energetic approaches to more complex systems. As such, the package facilitates novel opportunities for mechanistically linking quantitative food webs and ecosystem functioning in real and dynamic natural landscapes. This article is protected by copyright. All rights reserved.
As impacts of introduced species cascade through trophic levels, they can cause indirect and counter-intuitive effects. To investigate the impact of invasive species at a network level, we use a generalized food web model, capable of propagating changes through networks with a series of ecologically realistic criteria. Using data from a small British offshore island, we quantify the impacts of four virtual introduced species (a rat, an insectivore, a herbivore and a carnivore) and explore which clusters of species react in similar ways. The results suggest that some native species may be at risk from a wider range of invasives than previously believed. The implications of these results for ecologists and land managers are discussed.
Climate change is predicted to place up to one in six species at risk of extinction in coming decades, but extinction probability is likely to be influenced further by biotic interactions such as predation. We use structural equation modelling to integrate results from remote camera trapping and long-term (17-22 years) regional-scale (8000 km²) datasets on vegetation and small vertebrates (greater than 38 880 captures) to explore how biotic processes and two key abiotic drivers influence the structure of a diverse assemblage of desert biota in central Australia. We use our models to predict how changes in rainfall and wildfire are likely to influence the cover and productivity of the dominant vegetation and the impacts of predators on their primary rodent prey over a 100-year timeframe. Our results show that, while vegetation cover may decline due to climate change, the strongest negative effect on prey populations in this desert system is top-down suppression from introduced predators.
Significance
Ecosystems store vast quantities of wealth, but difficulties measuring wealth held in ecosystems prevent its inclusion in accounting systems. Ecosystem-based management endeavors to manage ecosystems holistically. However, ecosystem-based management lacks headline indicators to evaluate performance. We unify the inclusive wealth and ecosystem-based management paradigms, allowing apples-to-apples comparisons between the wealth of the ecosystem and other forms of wealth, while providing a headline performance index for evaluating the performance of ecosystem-based management. We project that the Baltic Sea fishery ecosystem yields increasing stores of wealth over the next 50 y under the ecosystem-based management-inspired multispecies maximum sustainable yield management beginning in 2017, whereas the previous single-species management generally results in declining wealth.
As defaunation spreads through the world, there is an urgent need for restoring ecological interactions, thus assuring ecosystem processes. Here, we define the new concept of credit of ecological interactions, as the number of interactions that can be restored in a focal area by species colonization or reintroduction. We also define rewiring time, as the time span until all the links that build the credit of ecological interactions of a focal area have become functional again. We expect that the credit will be gradually cashed following refaunation in rates that are proportional to (1) the abundance of the reintroduced species (that is expected to increase in time since release), (2) the abundance of the local species that interact with them, and (3) the traits of reintroduced species. We illustrated this approach using a theoretical model and an empirical case study where the credit of ecological interactions was estimated. This new conceptual framework is useful for setting reintroduction priorities and for evaluating the success of conservation initiatives that aim to restore ecosystem services.
State and transition models (STMs) are used to organize and communicate information regarding ecosystem change, especially the implications for management. The fundamental premise that rangelands can exhibit multiple states is now widely accepted and has deeply pervaded management thinking, even in the absence of formal STM development. The current application of STMs for management, however, has been limited by both the science and the ability of institutions to develop and use STMs. In this chapter, we provide a comprehensive and contemporary overview of STM concepts and applications at a global level. We first review the ecological concepts underlying STMs with the goal of bridging STMs to recent theoretical developments in ecology. We then provide a synthesis of the history of STM development and current applications in rangelands of Australia, Argentina, the United States, and Mongolia, exploring why STMs have been limited in their application for management. Challenges in expanding the use of STMs for management are addressed and recent advances that may improve STMs, including participatory approaches in model development, the use of STMs within a structured decision-making process, and mapping of ecological states are described. We conclude with a summary of actions that could increase the utility of STMs for collaborative adaptive management in the face of global change.
Social behaviors can significantly affect population viability, and some behaviors might reduce extinction risk. We used population viability analysis to evaluate effects of past and proposed habitat loss on the White-breasted Thrasher (Ramphocinclus brachyurus), a cooperatively breeding songbird with a global population size of <2000 individuals. We used an individual-based approach to build the first demographic population projection model for this endangered species, parameterizing the model with data from eight years of field study before and after habitat loss within the stronghold of the species' distribution. The recent habitat loss resulted in an approximately 18% predicted decline in population size; this estimate was mirrored by a separate assessment using occupancy data. When mortality rates remained close to the pre-habitat loss estimate, quasi-extinction probability was low under extant habitat area, but increased with habitat loss expected after current plans for resort construction are completed. Post-habitat loss mortality rate estimates were too high for projected populations to persist. Vital rate sensitivity analyses indicated that population growth rate and population persistence were most sensitive to juvenile mortality. However, observed values for adult mortality were closest to the threshold value above which populations would crash. Adult mortality, already relatively low, may have the least capacity to change compared to other vital rates, whereas juvenile mortality may have the most capacity for improvement. Results suggest that improving mortality estimates and determining the cause(s) of juvenile mortality should be research priorities. Despite predictions that aspects of cooperative systems may result in variation in reproduction or juvenile mortality being the most sensitive vital rates, adult mortality was the most sensitive in half of the demographic models of other avian cooperative breeders. Interestingly, vital rate sensitivity differed by model type. However, studies that explicitly modeled the species' cooperative breeding system found reproduction to be the most sensitive rate.
Ecopath with Ecosim (EwE) models are easier to construct and use compared to most other ecosystem modelling techniques and are therefore more widely used by more scientists and managers. This, however, creates a problem with quality assurance; to address this we provide an overview of best practices for creating Ecopath models. We describe the diagnostics that can be used to check for thermodynamic and ecological principles, and highlight principles that should be used for balancing a model. We then highlight the pitfalls when comparing Ecopath models using Ecological Network Analysis indices. For dynamic simulations in Ecosim we show the state of the art in calibrating the model by fitting it to time series using a formal fitting procedure and statistical goodness of fit. Finally, we show how Monte Carlo simulations can be used to address uncertainty in input parameters, and we discuss the use of models in a management context, specifically using the concept of ‘key runs’ for ecosystem-based management. This novel list of best practices for EwE models will enable ecosystem managers to evaluate the goodness of fit of the given EwE model to the ecosystem management question.
Food-web theory can be a powerful guide to the management of complex ecosystems. However, we show that indices of species importance common in food-web and network theory can be a poor guide to ecosystem management, resulting in significantly more extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial Optimization to find optimal management strategies for a wide range of real and hypothetical food webs. This Artificial Intelligence approach provides the ability to test the performance of any index for prioritizing species management in a network. While no single network theory index provides an appropriate guide to management for all food webs, a modified version of the Google PageRank algorithm reliably minimizes the chance and severity of negative outcomes. Our analysis shows that by prioritizing ecosystem management based on the network-wide impact of species protection rather than species loss, we can substantially improve conservation outcomes.
The study of ecological networks is severely limited by 1) the difficulty to access data, 2) the lack of a standardized way to link meta-data with interactions, and 3) the disparity of formats in which ecological networks themselves are stored and represented. To overcome these limitations, we have designed a data specifi cation for ecological networks. We implemented a database respecting this standard, and released an R package (rmangal) allowing users to programmatically access, curate, and deposit data on ecological interactions. In this article, we show how these tools, in conjunction with other frameworks for the programmatic manipulation of open ecological data, streamlines the analysis process and improves replicability and reproducibility of ecological network studies.
Apparent competition is an important process influencing many ecological communities. We used predator-prey theory to predict outcomes of ecosystem experiments aimed at mitigating apparent
competition by reducing primary prey. Simulations predicted declines in secondary prey following reductions in primary prey because predators consumed more secondary prey until predator numbers responded to reduced prey densities. Losses were exacerbated by a higher carrying capacity of primary prey and a longer lag time of the predator’s numerical response, but a gradual reduction in primary prey was less detrimental to the secondary prey. We compared predictions against two field experiments where endangered woodland
caribou (Rangifer tarandus caribou) were victims of apparent competition. First, when deer (Odocoileus sp.) declined suddenly following a severe winter, cougar (Puma concolor) declined with a 1–2-year lag, yet in the interim more caribou were killed by cougars, and caribou populations declined by 40%. Second, when moose (Alces alces) were gradually reduced using a management experiment, wolf (Canis lupus) populations declined but did not shift consumption to caribou, and the largest caribou subpopulation stabilized. The observed contrasting outcomes of sudden versus gradual declines in
primary prey supported theoretical predictions. Combining theory with field studies clarified how to manage communities to mitigate endangerment caused by apparent competition that affects many taxa.
Descriptions of food-web relationships first appeared more than a century ago, and the quantitative analysis of the network structure of food webs dates back several decades. Recent improvements in food-web data collection and analysis methods, coupled with a resurgence of interdisciplinary research on the topology of many kinds of “real-world” networks, have resulted in renewed interest in food-web structure. This
chapter reviews the history of the search for generalities in the structure of complex food webs, and discusses current and future research trends. Analysis of food-web structure has used empirical and modeling
approaches, and has been inspired both by questions from ecology such as “What factors promote stability of complex ecosystems given internal dynamics and external perturbations? and questions from network research such as “Do food webs display universal structure similar to other types of networks?” Recent research has suggested that once variable diversity and connectance are taken into account, there are universal coarse-grained characteristics of how trophic links and species defined according to trophic function are distributed within food webs. In addition, aspects of food-web network structure have been shown to
strongly influence the robust functioning and dynamical persistence of ecosystems.
An increase in species richness with decreasing latitude is a prominent pattern in nature. However, it remains unclear whether there are corresponding latitudinal gradients in the properties of ecological interaction networks. We investigated the structure of 216 quantitative antagonistic networks comprising insect hosts and their parasitoids, drawn from 28 studies from the High Arctic to the tropics. Key metrics of network structure were strongly affected by the size of the interaction matrix (i.e. the total number of interactions documented between individuals) and by the taxonomic diversity of the host taxa involved. After controlling for these sampling effects, quantitative networks showed no consistent structural patterns across latitude and host guilds, suggesting that there may be basic rules for how sets of antagonists interact with resource species. Furthermore, the strong association between network size and structure implies that many apparent spatial and temporal variations in network structure may prove to be artefacts.
Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological - physical - human systems. We describe a "metamodel" approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples - one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics - to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation.
1. Recent efforts to understand how the patterning of interaction strength affects both structure and dynamics in food webs have highlighted several obstacles to productive synthesis. Issues arise with respect to goals and driving questions, methods and approaches, and placing results in the context of broader ecological theory.
2. Much confusion stems from lack of clarity about whether the questions posed relate to community-level patterns or to species dynamics, and to what authors actually mean by the term 'interaction strength'. Here, we describe the various ways in which this term has been applied and discuss the implications of loose terminology and definition for the development of this field.
3. Of particular concern is the clear gap between theoretical and empirical investigations of interaction strengths and food web dynamics. The ecological community urgently needs to explore new ways to estimate biologically reasonable model coefficients from empirical data, such as foraging rates, body size, metabolic rate, biomass distribution and other species traits.
4. Combining numerical and analytical modelling approaches should allow exploration of the conditions under which different interaction strengths metrics are interchangeable with regard to relative magnitude, system responses, and species identity.
5. Finally, the prime focus on predator-prey links in much of the research to date on interaction strengths in food webs has meant that the potential significance of non-trophic interactions, such as competition, facilitation and biotic disturbance, has been largely ignored by the food web community. Such interactions may be important dynamically and should be routinely included in future food web research programmes.
One of the most interesting and vexing problems in ecology is how distinctly different communities of plants and animals can occur in the same ecosystem. The theory of these systems, known as multiple stable states, is well understood, but whether multiple stable states actually exist in nature has remained a hotly debated subject. This book provides a broad and synthetic critique of recent advances in theory and new experimental evidence. Modern models of systems with multiple stable states are placed in historical context. Current theories are covered in a rigorous fashion with the specific goal of identifying testable predictions about multiple stable states. The book provides a more synthetic, more critical, and broader analysis of multiple stable states in natural ecosystems than any previous review. By making the theory more transparent and the analysis of the evidence more comparative, the book broadens the discussion about multiple stable states, leading to a more general consideration of the interplay between theory and experiment in community ecology and environmental management.
Body size is associated with fundamental biological processes such as metabolism, movement, and the rate of reproduction and evolution. Although allometric principles should also influence the range of potential behavioral responses for a given organism, evidence for such large-scale and cross-taxon relationships is lacking. If they exist, scaling-related changes in behavior should be prominent in predator-prey interactions: body size affects the likelihood of attack and the costs of predator avoidance. We take a interspecific perspective on a traditionally intraspecific topic by using a 142-species data set containing organisms ranging over seven degrees of magnitude in body size to analyze the relationship between mean response to predation risk and both prey size and the predator : prey size ratio. We found a weak but significant relationship between two metrics of prey size (mean species-level prey mass and mean species-level predator : prey size ratio) and two of the five prey response variables: risk-induced changes in prey habitat use and prey fecundity were significantly correlated with prey body size and the predator : prey ratio. Risk-induced reductions in prey activity were positively correlated with prey mass. In contrast, there was no correlation between prey mass or the predator : prey size ratio and risk-induced changes in either prey growth and survival. We also document considerable variation in response to predation risk among taxa, highlighting that many additional factors contribute to the effects of predation risk on prey behavior, growth, fecundity, and survival. The weak but significant large-scale relationships we documented in our work suggest that allometric relationships may play a subtle role in structuring some of a prey organism's response to predation risk.
Recent advances in aquatic ecosystem modelling have particularly focused on trophic network analysis through trophodynamic models. We present here a R package devoted to a recently developed model, EcoTroph. This model enables the analysis of aquatic ecological networks and the related impacts of fisheries. It was available through a plug-in in the well-known Ecopath with Ecosim software or through implementations in Excel sheets. The R package we developed simplifies the access to the EcoTroph model and offers a new interfacing between two widely used software, Ecopath and R.
Nestled amid the Appalachian Mountains of southwestern Virginia and northeastern Tennessee lies the Clinch Valley, the nation’s leading hot spot for imperiled aquatic organisms. The Clinch River is the only undimmed headwater of the Tennessee River basin, which in turn is the nation’s most biologically diverse drainage system. The surface waters of the Clinch run rich indeed: They are home to at least 29 rare mussels and 19 rare fish. Underground, the region’s limestone bedrock is honeycombed by more than a thousand caves and uncounted underground springs and streams. This little-known world is filled with a menagerie of rare beetles, isopods, and other subterranean insects. These underground realms have yielded more than 30 species new to science in just the past few years. The Clinch Valley is largely rural and sparsely populated. Most residents make their living directly from the land, either mining coal, harvesting timber, grazing cattle, or planting crops. These rural lifestyles have maintained much of the region in a relatively natural state, and more than two-thirds of the Clinch Valley remains forested. The forested hills mask a history of ecologically unsound land use practices, however, that have degraded the legendary quality of the region’s waterways. Virtually anything released in the valley flows downhill into the streams and rivers. Among the greatest threats to the valley’s extraordinary aquatic life are heavy metals leaching from abandoned coal mines, sediment eroding from cutover slopes, and nutrients released by streamside-grazing cattle. These and other threats have already taken a toll on the region’s extraordinary biological richness. Where once there were 60 kinds of freshwater mussels, only about 40 remain. Coastal southern California, in contrast, is one of the most densely populated regions in the nation. It, too, is a hot spot for imperiled species. Its dry Mediterranean climate and varied topography have favored the evolution of a host of unique plants and animals. Altogether, some 86 imperiled species are found along the coast and in the mountains of this nationally significant center of biodiversity. Certain areas stand out even by California standards as having a truly extraordinary diversity of rare species.
Population viability analysis (PVA) has been used for three decades to assess threats and evaluate conservation options for wildlife populations. What has been learned from PVA on in situ populations are valuable lessons also for assessing and managing viability and sustainability of ex situ populations. The dynamics of individual populations are unpredictable, due to limited knowledge about important factors, variability in the environment, and the probabilistic nature of demographic events. PVA considers such uncertainty within simulations that generate the distribution of likely fates for a population; management of ex situ populations should also take into consideration the uncertainty in our data and in the trajectories of populations. The processes affecting wildlife populations interact, with feedbacks often leading to amplified threats to viability; projections of ex situ populations should include such feedbacks to allow for management that foresees and responds to the cumulative and synergistic threats. PVA is useful for evaluating conservation options only if the goals for each population and measures of success are first clearly identified; similarly, for ex situ populations to contribute maximally to species conservation, the purposes for the population and definitions of sustainability in terms of acceptable risk must be documented. PVA requires a lot of data, knowledge of many processes affecting the populations, modeling expertize, and understanding of management goals and constraints. Therefore, to be useful in guiding conservation it must be a collaborative, trans‐disciplinary, and social process. PVA can help integrate management of in situ and ex situ populations within comprehensive species conservation plans.
Introduction: Food webs have aroused interest and scientific analysis for many decades (Cohen, 1978). Especially Odum's school of ecosystem thinking sought to quantify fluxes in ecosystems, based on feeding guilds (Odum, 1953). Later, the theoretical analysis of interactions among species took a prominent role, arguing that information fluxes are as important as energy fluxes (Pimm, 1982). These interaction-network ideas still dominate models and experiments today (Rossberg, 2013). In contrast, interactions between two (trophic) levels (“bipartite” or “two-mode” networks) are a more recent ecological mainstream activity. Pollination networks featured verbally in early scientific works (dating back to comments in the third chapter of Darwin, 1859), but it was only in the 1980s that data describing such interaction networks specifically received analytical attention (starting with the work of Jordano, 1987). Today, food-web ecologists and network ecologists are still two largely separate scientific communities, with different data, methods, aims, and interpretations. Attempts to bridge this gap are relatively few (Ings et al., 2009). Any food-web workshop will typically bring together people from both sides and those already straddling the fields. Still, studies show a considerable separation, despite substantial intellectual overlap. In this chapter, we present an (necessarily incomplete) overview of current differences between food-web and network ecology with the aim of highlighting the underlying similarities. We believe that both fields can profit from the expertise and experience present in the other, and we suggest specific steps toward incorporating so far neglected issues tackled in the other field. Specifically, we organize this chapter into four main dimensions (scientific focus; data; nodes and links; and methods) after a brief section defining the terms we use. Definitions Food webs describe who-eats-whom-relationships in an n × n adjacency matrix. Since every food-web entity may interact with any other, this matrix has the dimensions of the number of entities (species, guilds) and is called one-mode or unipartite. If interactions are restricted to those between, and not among, two trophic levels, the resulting k × l matrix describes bipartite networks. Food webs are typically interpreted along trophic relationships (and often contain only data on trophic interactions, as revealed, e.g., in the 113 webs in http://ipmnet.org/loop/foodweb.aspx). Interaction networks, in contrast, include a large diversity of relationships between species, e.g., mutualism, facilitation, or commensalism (Figure 1.1).
Introduction: Two Types of Extinctions There are two types of species extinctions: true, or numerical extinctions, and functional extinctions. Numerical extinction – the traditional concept of extinction – occurs when the very last member of a species dies, while functional or ecological extinction occurs when a species becomes too rare to fulfill its ecological, interactive role in the ecosystem (Conner, 1988; Estes et al., 1989; Novaro et al., 2000; Jackson et al., 2001; Redford and Feinsinger, 2001; Soulé et al., 2003; Sekercioglu et al., 2004; McConkey and Drake, 2006; Baum and Worm, 2009; Estes et al., 2010; Anderson et al., 2011; Cury et al., 2011; Galetti et al., 2013; Säterberg et al., 2013; McConkey and O´Farrill, 2015; Sellman et al., 2015). It has been estimated that the current rate of numerical species extinction is about 1000 times higher than the natural background rate of extinction, on par with that of the great mass extinctions (Pereira et al., 2010; Barnosky et al., 2011; Pimm et al., 2014). In contrast, the rate of functional extinctions is largely unknown. Critical abundance thresholds or ecologically effective population sizes of species, below which they cease to function in the system of which they are parts, have been established for only a few species (McConkey and Drake, 2006; Estes et al., 2010; Cury et al., 2011). However, trophic cascades and regime shifts observed in a variety of ecosystems following declining population size of a species clearly indicate the presence of such abundance thresholds of species in many ecosystems (Frank et al., 2005; Casini et al., 2009; Estes et al., 2011; Smith et al., 2011; Ripple et al., 2014). Indeed, recent theoretical studies suggest that the frequency of functional extinctions might be disturbingly high and that even moderate declines in the densities of some species might lead to numerical extinctions of other dependent species (Säterberg et al., 2013; Sellman et al., 2015). In other words, a species can go functionally extinct well before the species becomes so rare that it loses its genetic and demographic viability and puts it in danger of a numerical extinction.
Introduction The dynamical behavior of individuals in ecosystems involves a multifaceted set of interaction types and processes that take place at different hierarchical levels. We present an individual-based, stochastic model that considers species dynamics at three hierarchical levels: population, community, and metacommunity. We use an individual-based model to show how the consequences of mechanisms that are specific to each hierarchical level may interact with processes that belong to other hierarchical levels. The strength of these effects is quantified in terms of impacts on metapopulation sizes and spatial distribution of populations. Results indicate the following: (1) the cohesion of the social network structure among conspecific individuals heavily affects their feeding efficiency at food-web level; (2) more generalist feeding habits trigger homogeneous spatial distribution of species at the landscape scale; and (3) high frequency of migration movements limits the local success of a generalist species thus leading to small metapopulation sizes. We illustrate how such a hierarchical framework may contribute to understanding the emergence of macroscopic patterns (i.e., metapopulation size and spatial heterogeneity) starting from elementary, bottom–up rules defined at the individual level. Hierarchical Organization and Individual-Based Modeling in Ecology Concurrent processes and interactions occur at different hierarchical levels in ecosystems (i.e., individual, population, community, and metapopulation/metacommunity) and do often spread their effects beyond the levels in which they actually originate. Some studies describe how ecological dynamics involving two hierarchical layers may interplay with each other. Social interactions among conspecific individuals may be regulated by metapopulation and community dynamics, community composition may be molded by landscape fragmentation, and species coexistence in metacommunities may result from the trade-off between spatial dispersal and multiple interaction types in food webs. Association rates in a population of wild Asian elephants depend on environmental conditions and seasonality (de Silva et al., 2011). The rates at which social ties are formed peak in dry periods and resident elephants tend to maintain over time a stable pool of interactions with the same individuals. The cohesion of social groups of baboons may vary in response to predation pressure or spatial food distribution (Barton et al., 1996). When predation pressure is high, the distances between conspecific individuals are smaller and social groups are more cohesive; this raises the chances of contest competition.
Oil spills can disrupt marine and coastal ecosystem services leading to reduced employment opportunities and income. Ecosystem models can be used to estimate the effects of oil pollution; however, uncertainty in model predictions may influence damage assessment. We performed an uncertainty analysis for the Atlantis ecosystem model of the Gulf of Mexico (Atlantis-GOM), under a scenario simulating the effects of the Deepwater Horizon oil spill. Atlantis-GOM simulates major biophysical processes, including the effects of oil hydrocarbons on fish growth and mortality. We used all available fish stomach content data to inform parameter distribution for the Atlantis-GOM availability matrix, which represents predator total consumption potential and diet preference. We sampled the fish diet composition distribution and analyzed the variability of functional group biomass and catch predicted by Atlantis-GOM simulations to changes in the availability matrix. Resulting biomass and catch were then used to fit statistical emulators of the ecosystem model and predict biomass and catch given the complete diet parameter space. We used simulated and emulated data to assess changes in recovery time to oil spill effects. Uncertainty in diet composition had large effects on model outputs and may, therefore, influence damage assessment of oil exposure on economically important species.
Food web models parameterised using body size show promise to predict trophic interaction strengths (IS) and abundance dynamics. However, this remains to be rigorously tested in food webs beyond simple trophic modules, where indirect and intraguild interactions could be important and driven by traits other than body size. We systematically varied predator body size, guild composition and richness in microcosm insect webs and compared experimental outcomes with predictions of IS from models with allometrically scaled parameters. Body size was a strong predictor of IS in simple modules (r² = 0.92), but with increasing complexity the predictive power decreased, with model IS being consistently overestimated. We quantify the strength of observed trophic interaction modifications, partition this into density‐mediated vs. behaviour‐mediated indirect effects and show that model shortcomings in predicting IS is related to the size of behaviour‐mediated effects. Our findings encourage development of dynamical food web models explicitly including and exploring indirect mechanisms.
An ecosystem's complexity develops from the vast numbers of species interacting in ecological communities. The nature of these interactions, in turn, depends on environmental context. How do these components together influence an ecosystem's behavior as a whole? Can ecologists resolve an ecosystem's complexity in order to predict its response to disturbances? Resolving Ecosystem Complexity develops a framework for anticipating the ways environmental context determines the functioning of ecosystems. Oswald Schmitz addresses the critical questions of contemporary ecology: How should an ecosystem be conceptualized to blend its biotic and biophysical components? How should evolutionary ecological principles be used to derive an operational understanding of complex, adaptive ecosystems? How should the relationship between the functional biotic diversity of ecosystems and their properties be understood? Schmitz begins with the universal concept that ecosystems are comprised of species that consume resources and which are then resources for other consumers. From this, he deduces a fundamental rule or evolutionary ecological mechanism for explaining context dependency: individuals within a species trade off foraging gains against the risk of being consumed by predators. Through empirical examples, Schmitz illustrates how species use evolutionary ecological strategies to negotiate a predator-eat-predator world, and he suggests that the implications of species trade-offs are critical to making ecology a predictive science. Bridging the traditional divides between individuals, populations, and communities in ecology, Resolving Ecosystem Complexity builds a systematic foundation for thinking about natural systems.
This chapter focuses on the relationship between community diversity and community stability. Because perspectives on this topic have changed dramatically over the last 50 years, this provides a good topic to investigate if paradigm shifts have occurred in theoretical community ecology. A brief history of perspectives on the relationship between diversity and stability has been provided. A simple model that illustrates several different relationships between diversity and stability, or more properly, relationships between diversity and different definitions of stability has been presented. The goal here is to show that different perspectives on the relationship between diversity and stability are complementary rather than at odds. The history of the sometimes-rocky relationship between theory and empiricism stemming from different definitions of stability has been described. Examples of specific ideas that I think can be tested, or at least explored, to derive insights into the relationship between diversity and stability, regardless of the preferred definition of stability have been provided. Thus, the debate about the effect of diversity on stability is primarily a debate about what is the most relevant definition of stability. There does not have to be one best definition of stability; different definitions may be more appropriate in different situations, and considering multiple definitions applied to the same situation may be instructive. What is clear, however, is that stability is not a single, simple, and easily defined concept.
The conservation of terrestrial carnivores is hampered by economic conflicts between predation and livestock production. The dingo C anis dingo is the top predator in A ustralia's terrestrial ecosystems but its abundance is controlled because it preys on livestock. Dingo control (poisoning, shooting) is associated with increased densities of wild herbivores, which can lead to reduced cattle condition and fertility through competition for pasture. We investigated whether the restoration of dingoes might provide a net benefit for rangeland vegetation and the profit margins of cattle pastoralists.
We developed a dynamic, multi‐species metamodel to represent the trophic linkages and economics of a rangeland cattle enterprise. To estimate the strength of dingo‐mediated trophic cascades, we underpinned the metamodel with a detailed simulation of pasture growth, grazing pressure and cattle live‐weight gain. An economic model that calculated the costs and revenues associated with maintaining the cattle herd was used to examine trade‐offs between livestock density, kangaroo abundance, calf losses and dingo control.
We simulated the effects of dingo abundance on rangeland ecology (pasture biomass, kangaroo density) and enterprise performance (cattle live‐weight gain, gross margin). Assuming a typical stocking density for semi‐arid rangelands, we estimated that kangaroo control by an unbaited dingo population would increase pasture biomass by 53 kg ha ⁻¹ , improve gross margins by $0·83 ha ⁻¹ and reduce inter‐annual variability in profits.
The increase in pasture biomass due to dingoes was greatest at low stocking densities (that permitted high kangaroo abundance in the absence of predation), while improvement in profits was strongest at intermediate stocking densities (when cattle density was high enough to take advantage of the additional pasture biomass). At high stocking densities, the abundance of kangaroos was low, so if dingo abundance exceeded that required to control kangaroos, some dingo baiting could produce small economic gains.
Synthesis and applications . There is little incentive for pastoralists to reduce livestock densities in mixed wildlife–livestock systems unless wildlife grazing can be controlled. Our results demonstrate that top‐down herbivore control by dingoes should allow cattle pastoralists to profit from conservative stocking densities while reducing the risk of pasture over‐utilization.
The composition of a peatland plant community has considerable effect on a range of ecosystem functions. Peatland plant community structure is predicted to change under future climate change, making the quantification of the direction and magnitude of this change a research priority. We subjected intact, replicated vegetated poor fen peat monoliths to elevated temperatures, increased atmospheric carbon dioxide (CO2 ), and two water table levels in a factorial design to determine the individual and synergistic effects of climate change factors on the poor fen plant community composition. We identify three indicators of a regime shift occurring in our experimental poor fen system under climate change: nonlinear decline of Sphagnum at temperatures 8 °C above ambient conditions, concomitant increases in Carex spp. at temperatures 4 °C above ambient conditions suggesting a weakening of Sphagnum feedbacks on peat accumulation, and increased variance of the plant community composition and pore water pH through time. A temperature increase of +4 °C appeared to be a threshold for increased vascular plant abundance; however the magnitude of change was species dependent. Elevated temperature combined with elevated CO2 had a synergistic effect on large graminoid species abundance, with a 15 times increase as compared to control conditions. Community analyses suggested that the balance between dominant plant species was tipped from Sphagnum to a graminoid-dominated system by the combination of climate change factors. Our findings indicate that changes in peatland plant community composition are likely under future climate change conditions, with a demonstrated shift toward a dominance of graminoid species in poor fens.
Cited By (since 1996):2, Export Date: 26 November 2013, Source: Scopus
The loss of a species from an ecological community can set up a cascade of secondary extinctions that in the worst case could lead to the collapse of the community. Both deterministic and stochastic mechanisms may be involved in such secondary extinc- tions. To investigate the extent of secondary extinctions in ecological communities following the loss of a species, we here develop a community viability analysis. We introduce a measure called the ''quasi-collapse risk'' that is defined as the probability that the number of species in a community falls below some defined value within a fixed period of time following the loss of a species. We develop deterministic and stochastic methods for finding post-extinction communities. We use these methods to investigate the relationship between diversity (species richness) and quasi-collapse risks in model communities. It is shown that, in a deterministic context, communities with more species within trophic levels have a larger fraction of species remaining in post-extinction communities. This benefit of species richness is to a large extent lost in the presence of demographic stochasticity. The reason for this is a negative relationship between population density and species diversity. We also show that communities become increasingly triangular in shape as secondary extinctions take place, due to greater extinction risk of species at higher trophic levels. We argue that this new approach holds some promise for identifying fragile ecosystems and keystone
Most population viability analyses (PVA) assume that the effects of species interactions are subsumed by population-level parameters. We examine how robust five commonly used PVA models are to violations of this assumption. We develop a stochastic, stage-structured predator-prey model and simulate prey population vital rates and abundance. We then use simulated data to parameterize and estimate risk for three demographic models (static projection matrix, stochastic projection matrix, stochastic vital rate matrix) and two time series models (diffusion approximation [DA], corrupted diffusion approximation [CDA]). Model bias is measured as the absolute deviation between estimated and observed quasi-extinction risk. Our results highlight three generalities about the application of single-species models to multi-species conservation problems. First, our collective model results suggest that most single-species PVA models overestimate extinction risk when species interactions cause periodic variation in abundance. Second, the DA model produces the most (conservatively) biased risk forecasts. Finally, the CDA model is the most robust PVA to population cycles caused by species interactions. CDA models produce virtually unbiased and relatively precise risk estimates even when populations cycle strongly. High performance of simple time series models like the CDA owes to their ability to effectively partition stochastic and deterministic sources of variation in population abundance.
Intensified exploitation of natural populations and habitats has led to increased mortality rates and decreased abundances of many species. There is a growing concern that this might cause critical abundance thresholds of species to be crossed, with extinction cascades and state shifts in ecosystems as a consequence. When increased mortality rate and decreased abundance of a given species lead to extinction of other species, this species can be characterized as functionally extinct even though it still exists. Although such functional extinctions have been observed in some ecosystems, their frequency is largely unknown. Here we use a new modelling approach to explore the frequency and pattern of functional extinctions in ecological networks. Specifically, we analytically derive critical abundance thresholds of species by increasing their mortality rates until an extinction occurs in the network. Applying this approach on natural and theoretical food webs, we show that the species most likely to go extinct first is not the one whose mortality rate is increased but instead another species. Indeed, up to 80% of all first extinctions are of another species, suggesting that a species' ecological functionality is often lost before its own existence is threatened. Furthermore, we find that large-bodied species at the top of the food chains can only be exposed to small increases in mortality rate and small decreases in abundance before going functionally extinct compared to small-bodied species lower in the food chains. These results illustrate the potential importance of functional extinctions in ecological networks and lend strong support to arguments advocating a more community-oriented approach in conservation biology, with target levels for populations based on ecological functionality rather than on mere persistence.
Preservation of wild populations implies their persistence, but biological persistence is a relative term. This paper outlines
criteria for what constitutes successful preservation and the methods available for determining population sizes and their
area requirements to meet these criteria.
Maintenance of overall ecosystem complexity is perceived as critical to the sustainability of ecosystem use. The development of an operational basis for an ecosystem approach to fisheries, however, faces many difficulties. On the research side, the challenge is in defining proper long-term, ecosystem-related objectives; determining meaningful reference values and indicators for desirable or undesirable states of the ecosystem; and developing appropriate data collection, analytical tools and models. The "viability" concept developed in economics by Jean-Pierre Aubin can be used to assist in the definition, selection of, and interaction among long-term objectives at an ecosystem level. It recognizes that ecosystems are complex assemblages of interacting and self-organizing natural and human components that cannot be predicted. Viability models define an ensemble of "viable states", in contrast to undesirable states defined as such by ecological, economic, and/or social constraints. These constraints can be derived from fisheries objectives, conservation principles, scientific results of modelling, or precautionary principles, and correspond to limit reference points to be avoided. Viability theory does not attempt to choose any "optimal solution" according to given criteria, but selects "viable evolutions". These evolutions are compatible with the constraints in the sense that they satisfy them at each time and can be delineated by the viability kernel. The southern Benguela marine ecosystem is presented as a first attempt for the application of this theory. In defining ecosystem-based objectives (and related issues such as target reference points), it seems more difficult to reach consensus among stakeholders on what is desirable than on what is undesirable (e.g. biological or economic collapse, species extinction, displacement of local rural communities). Expressed in the negative form or as limit reference points, ecosystem-based constraints can be considered simultaneously with current target reference points, such as maximum sustainable yield, using viability models. The viability approach can help to progressively integrate ecosystem considerations, such as conservation, into fisheries management. © 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.