Signal extraction from long-term ecological data using Bayesian and non-Bayesian state-space models

Emerging ecological time series from long-term ecological studies and remote sensing provide excellent opportunities for ecologists to study the dynamic patterns and governing processes of ecological systems. However, signal extraction from long-term time series often requires system learning (e.g., estimation of true system state) to process the large amount of information, to reconstruct system state, to account for measurement error, and to handle missing data. State-space models (SSMs) are a natural choice for these tasks and thus have received increasing attention in ecological and environmental studies. Data-based learning using SSMs that connect ecological processes to the measurement of system state becomes a useful technique in the ecological informatics toolkit. The present study illustrates the use of the Kalman filter (KF), an estimator of SSMs, with case studies of population dynamics. The examples of the SSM applications include the reconstruction of system state using the KF method and Markov chain Monte Carlo methods, estimation of measurement-error variances in the estimates of animal population abundance using basic structural models (BSMs), and estimation of missing values using the KF and Kalman smoother. Estimation of measurement-error variances by BSMs does not require knowledge of the functional form that generates the time series data. Instead, BSMs approximate the trajectory or deterministic skeleton of a system dynamics in a semi-parametric fashion, and provide a robust estimator of measurement-error variances. The present study also compares Bayesian SSMs with non-Bayesian SSMs. The joint use of the KF method or its extensions and Markov chain Monte Carlo (MCMC) methods is a promising approach to the parameter estimation of SSMs.     

Unequal catchability of male Bufo bufo within breeding populations

For 9 years, three breeding populations of the common toad Bufo bufo were censused during the breeding season. The hypothesis that catchability is equal for breeding males in each population and breeding period was tested. By comparing the lengths of all males captured with those recaptured in the same breeding period, it was shown that large males had a higher catchability than small ones. The ultimate cause of this unequal catchability remains unclear, but several causes can be rejected. Population parameters based on capture and capture – recapture data are highly influenced by this unequal catchability.     

Variation Thresholds for Extinction and Their Implications for Conservation Strategies

We examine the degree to which fitting simple dynamic models to time series of population counts can predict extinction probabilities. This is both an active branch of ecological theory and an important practical topic for resource managers. We introduce an approach that is complementary to recently developed techniques for estimating extinction risks (e.g., diffusion approximations) and, like them, requires only count data rather than the detailed ecological information available for traditional population viability analyses. Assuming process error, we use four different models of population growth to generate snapshots of population dynamics via time series of the lengths commonly available to ecologists. We then ask to what extent we can identify which of several broad classes of population dynamics is evident in the time series snapshot. Along the way, we introduce the idea of "variation thresholds," which are the maximum amount of process error that a population may withstand and still have a specified probability of surviving for a given length of time. We then show how these thresholds may be useful to both ecologists and resource managers, particularly when dealing with large numbers of poorly understood species, a common problem faced by those designing biodiversity reserves.     

Ecological inferences about marine mammals from passive acoustic data

Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods.     

Capture probability of fishes in Central European (Hungary) wadeable lowland streams

Neglect of imperfect capture efficiency leads to biased inferences on population abundance, and correspondingly, seriously affects ecological research, bioassessment, conservation, and fisheries management. To date, many research studies have studied capture efficiency of salmonid fishes, but the catchability of fishes living in non-salmonid streams has received much less attention. This paper estimates capture probability for seven fish species in densely vegetated lowland streams by using double-pass electrofishing data and an N-mixture removal model. Results show that capture probability can vary among species, and between-stream differences have a stronger influence on the abundance and the catchability than within-stream variability. Estimation uncertainty decreases with observed abundance, and the mean catchability tends to be the highest for the medium abundant species. These findings suggest that relative abundances from single-pass data are biased to a species- and habitat-specific degree. Therefore, plausible estimation of capture probability from double-pass electrofishing requires data collected from numerous sites that cover a wide range of the environmental gradient in lowland streams.     

The Chitty effect: a consequence of dynamic energy allocation in a fluctuating environment

An important biological feature of cyclic populations of voles and lemmings is phase-related changes in average body mass, with adults in high-density phases being 20-30% heavier than those in low-density phases of a cycle. This observation, called the "Chitty effect," is considered to be a ubiquitous feature of cyclic populations. It has been argued that understanding the Chitty effect is fundamental to unraveling the enigma of population cycles. However, there exists no agreement among biologists regarding the causes of the Chitty effect. Here, I propose a simple hypothesis to explain the Chitty effect, based on phase-related, dynamic allocation of energy between reproductive and somatic effort. The essence of the hypothesis is that: (1) reproduction is suppressed in animals born or raised in the later part of the increase phase by environmental factors, including social influences; (2) suppression of reproduction limits the amount of energy that is diverted for reproductive effort, and forces a disproportionately greater amount of surplus power (the energy left after the energetic costs of standard and active metabolism are met) to be allocated for somatic effort; (3) the surplus energy, above and beyond what is required for routine biological activities, will allow continuous growth and deposition of additional body mass, which causes an increase in body mass; and (4) animals grow to a larger size as a population enters the peak density phase, causing an increase in the average body mass. The Chitty effect is predicted to be most pronounced at the late increase or peak phase of a population cycle. Possible causes of reproductive suppression include direct or indirect influences of the environmental factors. The Chitty effect may be a consequence, not a cause, of population cycles in small mammals.     

The irreducible uncertainty of the demography-environment interaction in ecology

The interpretation of ecological data has been greatly improved by bridging the gap between ecological and statistical models. The major challenge is to separate competing hypotheses concerning demography, or other ecological relationships, and environmental variability (noise). In this paper we demonstrate that this may be an arduous, if not impossible, task. It is the lack of adequate ecological theory, rather than statistical sophistication, which leads to this problem. A reconstruction of underlying ecological processes can only be done if we are certain of either the demographic or the noise model, which is something that can only be achieved by an improved theory of stochastic ecological processes. Ignoring the fact that this is a real problem may mislead ecologists and result in erroneous conclusions about the relative importance of endogenous and exogenous factors in natural ecosystems. The lack of correct model identification may also have far-reaching consequences for population management and conservation.     

Value of long‐term ecological studies

Long‐term ecological studies are critical for providing key insights in ecology, environmental change, natural resource management and biodiversity conservation. In this paper, we briefly discuss five key values of such studies. These are: (1) quantifying ecological responses to drivers of ecosystem change; (2) understanding complex ecosystem processes that occur over prolonged periods; (3) providing core ecological data that may be used to develop theoretical ecological models and to parameterize and validate simulation models; (4) acting as platforms for collaborative studies, thus promoting multidisciplinary research; and (5) providing data and understanding at scales relevant to management, and hence critically supporting evidence‐based policy, decision making and the management of ecosystems. We suggest that the ecological research community needs to put higher priority on communicating the benefits of long‐term ecological studies to resource managers, policy makers and the general public. Long‐term research will be especially important for tackling large‐scale emerging problems confronting humanity such as resource management for a rapidly increasing human population, mass species extinction, and climate change detection, mitigation and adaptation. While some ecologically relevant, long‐term data sets are now becoming more generally available, these are exceptions. This deficiency occurs because ecological studies can be difficult to maintain for long periods as they exceed the length of government administrations and funding cycles. We argue that the ecological research community will need to coordinate ongoing efforts in an open and collaborative way, to ensure that discoverable long‐term ecological studies do not become a long‐term deficiency. It is important to maintain publishing outlets for empirical field‐based ecology, while simultaneously developing new systems of recognition that reward ecologists for the use and collaborative sharing of their long‐term data sets. Funding schemes must be re‐crafted to emphasize collaborative partnerships between field‐based ecologists, theoreticians and modellers, and to provide financial support that is committed over commensurate time frames.     

Ecology after 100 years: Progress and pseudo-progress

Has the science of ecology fulfilled the promises made by the originators of ecological science at the start of the last century? What should ecology achieve? Have good policies for environmental management flowed out of ecological science? These important questions are rarely discussed by ecologists working on detailed studies of individual systems. Until we decide what we wish to achieve as ecologists we cannot define progress toward those goals. Ecologists desire to achieve an understanding of how the natural world operates, how humans have modified the natural world, and how to alleviate problems arising from human actions. Ecologists have made impressive gains over the past century in achieving these goals, but this progress has been uneven. Some sub-disciplines of ecology are well developed empirically and theoretically, while others languish for reasons that are not always clear. Fundamental problems can be lost to view as ecologists fiddle with unimportant pseudo-problems. Bandwagons develop and disappear with limited success in addressing problems. The public demands progress from all the sciences, and as time moves along and problems get worse, more rapid progress is demanded. The result for ecology has too often been poor, short-term science and poor management decisions. But since the science is rarely repeated and the management results may be a generation or two down the line, it is difficult for the public or for scientists to decide how good or bad the scientific advice has been. In ecology over the past 100 years we have made solid achievements in behavioural ecology, population dynamics, and ecological methods, we have made some progress in understanding community and ecosystem dynamics, but we have made less useful progress in developing theoretical ecology, landscape ecology, and natural resource management. The key to increasing progress is to adopt a systems approach with explicit hypotheses, theoretical models, and field experiments on a scale defined by the problem. With continuous feedback between problems, possible solutions, relevant theory and experimental data we can achieve our scientific goals.     

An evaluation of the state of spatial point pattern analysis in ecology

Over the last two decades spatial point pattern analysis (SPPA) has become increasingly popular in ecological research. To direct future work in this area we review studies using SPPA techniques in ecology and related disciplines. We first summarize the key elements of SPPA in ecology (i.e. data types, summary statistics and their estimation, null models, comparison of data and models, and consideration of heterogeneity); second, we review how ecologists have used these key elements; and finally, we identify practical difficulties that are still commonly encountered and point to new methods that allow current key questions in ecology to be effectively addressed. Our review of 308 articles published over the period 1992–2012 reveals that a standard canon of SPPA techniques in ecology has been largely identified and that most of the earlier technical issues that occupied ecologists, such as edge correction, have been solved. However, the majority of studies underused the methodological potential offered by modern SPPA. More advanced techniques of SPPA offer the potential to address a variety of highly relevant ecological questions. For example, inhomogeneous summary statistics can quantify the impact of heterogeneous environments, mark correlation functions can include trait and phylogenetic information in the analysis of multivariate spatial patterns, and more refined point process models can be used to realistically characterize the structure of a wide range of patterns. Additionally, recent advances in fitting spatially‐explicit simulation models of community dynamics to point pattern summary statistics hold the promise for solving the longstanding problem of linking pattern to process. All these newer developments allow ecologists to keep up with the increasing availability of spatial data sets provided by newer technologies, which allow point patterns and environmental variables to be mapped over large spatial extents at increasingly higher image resolutions.     

Climate change,phenological shifts,eco-evolutionary responses and population viability: toward a unifying predictive approach

The debate on emission targets of greenhouse gasses designed to limit global climate change has to take into account the ecological consequences. One of the clearest ecological consequences is shifts in phenology. Linking these shifts to changes in population viability under various greenhouse gasses emission scenarios requires a unifying framework. We propose a box-in-a-box modeling approach that couples population models to phenological change. This approach unifies population modeling with both ecological responses to climate change as well as evolutionary processes. We advocate a mechanistic embedded correlative approach, where the link from genes to population is established using a periodic matrix population model. This periodic model has several major advantages: (1) it can include complex seasonal behaviors allowing an easy link with phenological shifts; (2) it provides the structure of the population at each phase, including the distribution of genotypes and phenotypes, allowing a link with evolutionary processes; and (3) it can incorporate the effect of climate at different time periods. We believe that the way climatologists have approached the problem, using atmosphere–ocean coupled circulation models in which components are gradually included and linked to each other, can provide a valuable example to ecologists. We hope that ecologists will take up this challenge and that our preliminary modeling framework will stimulate research toward a unifying predictive model of the ecological consequences of climate change.     

Assessing variation in life‐history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists

Mixed models are now well‐established methods in ecology and evolution because they allow accounting for and quantifying within‐ and between‐individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi‐modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life‐history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life‐history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long‐term studies of large mammals to illustrate the potential of using mixture models for assessing within‐population variation in life‐history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life‐history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users.     

A general framework for modeling population abundance data

Time-series data resulting from surveying wild animals are often described using state-space population dynamics models, in particular with Gompertz, Beverton-Holt, or Moran-Ricker latent processes. We show how hidden Markov model methodology provides a flexible framework for fitting a wide range of models to such data. This general approach makes it possible to model abundance on the natural or log scale, include multiple observations at each sampling occasion and compare alternative models using information criteria. It also easily accommodates unequal sampling time intervals, should that possibility occur, and allows testing for density dependence using the bootstrap. The paper is illustrated by replicated time series of red kangaroo abundances, and a univariate time series of ibex counts which are an order of magnitude larger. In the analyses carried out, we fit different latent process and observation models using the hidden Markov framework. Results are robust with regard to the necessary discretization of the state variable. We find no effective difference between the three latent models of the paper in terms of maximized likelihood value for the two applications presented, and also others analyzed. Simulations suggest that ecological time series are not sufficiently informative to distinguish between alternative latent processes for modeling population survey data when data do not indicate strong density dependence.     

Is relative abundance a good indicator of population size? Evidence from fragmented populations of a specialist butterfly (Lepidoptera: Lycaenidae)

A common task for conservation biologists and ecologists is to establish how many individuals there are in a population, usually within a defined area of habitat. Estimates of both absolute and relative population sizes are widely used in many aspects of population conservation and management. Mark–recapture studies are appropriate for estimating the absolute population sizes of a wide range of animals, in both open and closed populations, while relative abundances can be estimated from a variety of survey methods. Relative abundances are often used in a comparative way to compare both population size and fluctuations in abundance. Here, we used transect counts and capture–recapture studies to estimate the relative abundances and population sizes of a specialist butterfly, Theclinesthes albocincta (Lycaenidae) in three habitat fragments, over two consecutive years. The sizes of the three populations differed significantly between sites and were highly variable between years. One population was extremely small and is likely to become locally extinct. We found that estimates of relative abundance were highly correlated with estimates of population size (r ² = 0.88, P = 0.017) derived from the open population models. The combination of transect counts and capture–recapture studies used in this study appears to be a very informative tool for the conservation and management of this butterfly species and could be extended to other insects.     

Robustness of early warning signals of regime shifts in time-delayed ecological models

Various ecological and other complex dynamical systems may exhibit abrupt regime shifts or critical transitions, wherein they reorganize from one stable state to another over relatively short time scales. Because of potential losses to ecosystem services, forecasting such unexpected shifts would be valuable. Using mathematical models of regime shifts, ecologists have proposed various early warning signals of imminent shifts. However, their generality and applicability to real ecosystems remain unclear because these mathematical models are considered too simplistic. Here, we investigate the robustness of recently proposed early warning signals of regime shifts in two well-studied ecological models, but with the inclusion of time-delayed processes. We find that the average variance may either increase or decrease prior to a regime shift and, thus, may not be a robust leading indicator in time-delayed ecological systems. In contrast, changing average skewness, increasing autocorrelation at short time lags, and reddening power spectra of time series of the ecological state variable all show trends consistent with those of models with no time delays. Our results provide insights into the robustness of early warning signals of regime shifts in a broader class of ecological systems.     

Catchability: a key parameter for fish stock assessment

Summary Catchability is a concept in fishery biology which reflects the efficiency of a particular fishery. Its quantitative magnitude is expressed by the catchability coefficient, which relates the biomass abundance to the capture or fishing mortality. This paper is a comprehensive review of catchability including the development of our knowledge, interpretation and estimation.Catchability patterns indicate that the catchability coefficient has been used in two main lines: (a) increased efficiency of fishing effort and (b) its relation to population fishery processes for assessment and management purposes. It involves various aspects of the fishery, such as individual and population biology, characteristics of the fishing gear, amount of fishing, fishing strategies, and environmental fluctuation, among others.The concept is proposed of an integrated model of the catchability coefficient, which incorporates various of the aspects mentioned above. It is illustrated with two examples of its application: the red grouper (Epinephelus morio) fishery from the Campeche Bank, Gulf of Mexico, and the sardine (Sardinops caeruleus) fishery from the Gulf of California.     

The importance of ecological research for ecosystem management: The case of browsing by swamp wallabies (Wallabia bicolor) in commercially harvested native forests

Summary Ecosystem management often proceeds within the context of sub‐optimal relationships between ecologists and ecosystem managers, and management outcomes could be improved with greater collaboration between members of these disciplines. This paper identifies an ecosystem management problem resulting from the interaction between timber harvesting and browsing wallabies, and this case study is used to exemplify how ecological data and expertise can contribute to the process of ecosystem management. It is argued that appropriate use of existing ecological data, establishment of strategic new research and the implementation of management actions as experimental hypothesis tests can facilitate achievement of management objectives, but greater collaboration between ecologists and managers is required before this can occur. Reasons for sub‐optimal relationships are outlined, and the potential for structural change within large State‐run ecosystem management agencies to improve interactions between managers and ecologists is discussed.     

Ecological engineering: First steps towards economic analysis

Ecosystems fulfill a large number of ecological and economic functions. Increasing economic activity and population pressure are threatening the quality of ecosystems, and strengthen the need for ecological engineering. In this article some of the economic aspects of ecological engineering are discussed. In particular, the various methods to establish the economic costs and benefits of ecological engineering. For benefit estimation various methods are developed, including the contingent valuation method, the hedonic pricing method, the travel cost method and the shadow project method. The economic background, and the applicability and appropriateness of these methods are discussed. Next, the results of several studies on wetland valuation are shortly presented. It is concluded that the full socio-economic assessment of ecological engineering is still to be developed, and that further cooperation between ecologists and economists is required to make progress in this field that is rapidly gaining importance in policy making, both at the European and the global level.     

Spatial distribution of red grouper Epinephelus morio (Serranidae) catchability on the Campeche Bank of Mexico

Distribution patterns of catchability were evaluated for the red grouper Epinephelus morio (Serranidae) on the Campeche Bank off the northern Yucatan coast of Mexico. Catchability‐at‐length values were estimated with a method based on a transition matrix. Five years of catch and effort data for the Mexican mid‐fleet were used for the estimation procedure. Two main patterns were found within the bank. The first occurred in the deepest areas and in the eastern part of the bank, where catchability increased with size. The second was close to the coasts, where lower values of catchability were observed, even in large sizes. These results suggest that the main area of aggregation for reproduction with higher values of catchability is in the eastern part of the bank (22–23°N, 87–88°W). Catchability differences suggest that spatial management policies must be implemented to allow depleted stock to recover.