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Marine fish population fluctuations are pervasive. A primary driver of marine fish population fluctuation is annual recruitment variability. For over a century, fisheries oceanographers have tried to attribute cause to observed recruitment variability with varying degrees of success. While several hypotheses of recruitment variability can be found in the literature, most can be grouped into either food web- or transport-dependent mechanisms. The mechanistic basis of food web-dependent drivers is that larval survival, and eventual recruitment, is determined by starvation or predation mortality. Transport-dependent drivers are related to advective processes that disperse larvae into or out of habitat favorable for recruitment. In this dissertation I address these two mechanisms of recruitment variability for Shortbelly Rockfish, a highly abundant and ecologically important fish species of the California Current System. In Chapter 1, I develop and apply a bioenergetics growth model to understand how spatial variation in ocean conditions determines growth potential. I find that spatial patterns of early life stage-specific growth potential correspond with important early life history events. In Chapter 2, I introduce interannual variability in ocean conditions to the growth model and find that hotspots of spatial growth potential explain 78.6% of Shortbelly recruitment variability. This result supports the hypothesis that food web-dependent processes are important for recruitment strength. In Chapter 3, I introduce transport-dependent processes to the model framework and find that the relative importance of food limitation and transport on recruitment are dependent on interannual climate cycles (El Niño Southern Oscillation). The preponderance of recruitment drivers being context dependent amongst a backdrop of differing climate conditions may explain why understanding recruitment mechanisms has been so challenging.

In an era of increase of emerging infectious diseases, fungal pathogens have historically received little attention despite their growing importance 1–4. Fungal infectious disease emergence events began increasing dramatically in the 70s and 80s 5, and global anthropogenic change will likely cause this trend to continue. Fungal pathogens require unique methods for detection, modeling, management, and therapeutics; yet tools for understanding and mediating fungal pathogens are underdeveloped 2,4. While different in many ways from other pathogen classes, fungal pathogens nonetheless can have similarly devastating impacts on forests, crops, wildlife, and humans 1,3,6–8. The understudied nature of fungal pathogens coupled with a predicted rise in occurrence makes learning more about these organisms critical.Developing tools for monitoring fungal pathogen emergence is challenging due to their unique characteristics which set them apart from other pathogen groups. Specifically, long-lived environmental stages, saprobic reproduction, and a broad host range can make detection, risk estimation, and understanding of disease dynamics difficult 6,9. New, readily-available molecular methods for fungal detection are recent 10, and the low number of organisms often required to induce infection is poorly matched with PCR detection limits that often require relatively high loads 11.Batrachochytrium dendrobatidis (Bd) is a primary example of a fungal pathogen which has caused devastating global declines and for which new methods are required for detection and prediction. In my PhD thesis, I explore three questions: (1) can we detect Bd on environmental reservoirs; (2) can we predict Bd in amphibians using only information about the amphibian’s habitat; and (3) can Drosophila melanogaster be a vector of Bd, and be used to study invertebrate-Bd dynamics? Throughout the work presented here, I demonstrate how Bd may cause variable responses in amphibian populations (i.e., epidemic vs. endemic states), and understand more about how Bd interacts with its environment as a generalist, fungal pathogen rather than an amphibian specialist.

There are many barriers to fieldwork including cost, time, and physical ability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork in the natural sciences. Travel restrictions, concerns about our carbon footprint, and the global lockdown have extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially among graduate students and early‐career researchers. In this paper, we discuss agent‐based modeling as an open‐source, accessible, and inclusive resource to substitute for lost fieldwork during COVID‐19 and for future scenarios of travel restrictions such as climate change and economic downturn. We describe the benefits of Agent‐Based models as a teaching and training resource for students across education levels. We discuss how and why educators and research scientists can implement them with examples from the literature on how agent‐based models can be applied broadly across life science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the agent‐based modeling community in ecology and evolutionary research. Finally, we discuss the challenges facing agent‐based modeling and discuss how quantitative ecology can work in tandem with traditional field ecology to improve both methods., The COVID‐19 global lockdown may be an early harbinger for future disruptions to fieldwork under climate change scenarios. Agent‐Based models are a powerful and accessible way to surrogate lost field seasons to continue these projects or begin new ones. This innovative method is improving constantly and is an excellent tool for teaching and research in natural science.

The study of mammals has promoted the development and testing of many ideas in contemporary ecology. Here we address recent developments in foraging and habitat selection, source–sink dynamics, competition (both within and between species), population cycles, predation (including apparent competition), mutualism, and biological invasions. Because mammals are appealing to the public, ecological insight gleaned from the study of mammals has disproportionate potential in educating the public about ecological principles and their application to wise management. Mammals have been central to many computational and statistical developments in recent years, including refinements to traditional approaches and metrics (e.g., capture-recapture) as well as advancements of novel and developing fields (e.g., spatial capture-recapture, occupancy modeling, integrated population models). The study of mammals also poses challenges in terms of fully characterizing dynamics in natural conditions. Ongoing climate change threatens to affect global ecosystems, and mammals provide visible and charismatic subjects for research on local and regional effects of such change as well as predictive modeling of the long-term effects on ecosystem function and stability. Although much remains to be done, the population ecology of mammals continues to be a vibrant and rapidly developing field. We anticipate that the next quarter century will prove as exciting and productive for the study of mammals as has the recent one.

In this dissertation, I investigate a series of seemingly disparate topicsÑthe theorem of a mathematician working on turbulence in fluid flows, the collapse of the great California sardine fishery in the mid 20th century, competition between zooplankton grazers in a marine mesocosm, and the occurrence of influenza in the tropics. All of the work, however, is motivated by the ongoing endeavor to develop ecosystem-based approaches to fisheries management. Ecosystem-based management remains an open problem in part because the fundamental complexity of natural systems does not readily map onto the set of tools we’ve developed through our very successful accomplishments in engineering. In combination, the chapters of this thesis address this disparity by developing practical, empirical tools for studying and managing ecosystems that directly address the complex reality of nature.

The dynamic predator–prey relations in the food web are vital for understanding the function and structure of ecosystems. Dietary estimation is a research hotspot of quantitative ecology, providing key insights into predator–prey relationships. One of the most promising approaches is quantitative fatty acid signature analysis (QFASA), which is the first generation of statistical tools to estimate the quantitative trophic predator–prey relationships by comparing the fatty acid (FA) signatures among predators and their prey. QFASA has been continuously widely applied, refined and extended since its introduction. This article reviewed the research progress of QFASA from development and application. QFASA reflects the long-term diet of predator, and provides the quantitative dietary composition of predator, but it is sensitive to the metabolism of predator. The calibration coefficients (CCs) and the FA subset are two crucial parameters to explain the metabolism of predators, but the incorrect construction or improper use of CCs and the FA subset may cause bias in dietary estimation. Further study and refinement of the QFASA approach is needed to identify recommendations for which CCs and subsets of FA work best for different taxa and systems.

Barriers to fieldwork exist for many reasons such as physical ability, financial cost, and time availability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork experience in the natural science community. Travel restrictions and the global lockdown has extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially for graduate students and early-career researchers. In this paper, we discuss Agent-Based Modeling as an open-source, accessible, and inclusive resource to substitute for lost fieldwork during COVID-19 and future scenarios of travel restrictions such as climate change. We detail the process of model development with a plethora of examples from the literature on how Agent-Based Models can be applied broadly across life-science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the Agent-Based Modeling community in ecology and evolutionary research. We also describe the benefits of Agent-Based models as a teaching and training resource for students across education levels. Finally, we discuss the current challenges facing Agent-Based Modeling and discuss how the field of quantitative ecology can work in tandem with traditional field ecology to improve both methods.

Nature has inspired music since the dawn of humankind and has contributed to the creation and development of music as an art form. However, attempts to use the science of nature (i.e., quantitative ecology) to inform music as a broader art-science system is comparatively underdeveloped. In this paper an approach from biodiversity assessments is borrowed to quantify structural diversity in music scores. The approach is analogous in its nature and considers notations with distinct pitches and duration as equivalents of species in ecosystems, measures within a score as equivalents of ecosystems, and the sum of measures (i.e., the entire score) as a landscape in which ecosystems are embedded. Structural diversity can be calculated at the level of measures (&ldquo, alpha diversity&rdquo, ) and the entire score (&ldquo, gamma diversity&rdquo, ). An additional metric can be derived that quantifies the structural differentiation between measures in a score (&ldquo, beta diversity&rdquo, ). The approach is demonstrated using music scores that vary in complexity. The method seems particularly suitable for hypothesis testing to objectively identify many of the intricate phenomena in music. For instance, questions related to the variability within and between musical genres or among individual composers can be addressed. Another potential application is an assessment of ontogenetic structural variability in the works of composers during their lifetime. Such information can then be contrasted with other cultural, psychological, and historical variables, among others. This study shows the opportunities that music and ecology offer for interdisciplinary research to broaden our knowledge of complex systems of people and nature.

The linear generalized Lotka–Volterra equations (LGLVE) constitute the simplest theoretical framework for ecological communities involving different kinds of interspecific interactions ―e.g. competition, facilitation. These equations have been often criticized as being too simple to model real systems. This study has two main goals: First, to test the LGLVE as a quantitative tool for describing/explaining/predicting the equilibrium species abundances. That is, how accurately the LGLVE predict the yields of S interacting species? Second, to show analyze which quantitative predictions are possible with an incomplete knowledge of the LGLVE parameters. With this aim I compiled from the literature 33 experiments, most of them for plants, which measured species yields in monoculture, biculture and in mixtures of S >2 species. I found that, by obtaining the LGLVE parameters from the yields in monoculture and biculture experiments, the LGLVE can accurately predict the majority of the equilibrium species yields in the mixtures of S >2 species for most of the experiments. However, in many natural communities, e.g. tropical forests or plankton, S can be of the order of hundreds and estimating all the model parameters from empirical data is unfeasible. But, by estimating the mean interaction coefficient from the yields of an incomplete set of monoculture and biculture experiments, the LGLVE still can make accurate predictions. Firstly, it is possible to derive simple formulas which are able to predict the relative yield total and mean relative yield. Secondly, when we are interested in the fate or performance of a particular species, we can use a more refined approximation to predict the yield of this focal species with accuracy comparable to the one obtained when using the full set of LGLVE.

Competition in ecology is often modeled in terms of direct, negative effects of one individual on another. An example is logistic growth, modeling the effects of intraspecific competition, while the Lotka-Volterra equations for competition extend this to systems of multiple species, with varying strengths of intra- and inter-specific competition. These equations are a classic and well-used staple of quantitative ecology, providing a framework to understand species interactions, species coexistence, and community assembly. They can be derived from an assumption of random mixing of organisms, and an outcome of each interaction that removes one or more individuals. However, this framing is some-what unsatisfactory, and ecologists may prefer to think of phenomenological equations for competition as deriving from competition for a set of resources required for growth, which in turn may undergo their own complex dynamics. While it is intuitive that these frameworks are connected, and the connection is well-understood near to equilibria, here we ask the question: when can consumer dynamics alone become an exact description of a full system of consumers and resources? We identify that consumer-resource systems with this property must have some kind of redundancy in the original description, or equivalently there is one or more conservation laws for quantities that do not change with time. Such systems are known in mathematics as integrable systems. We suggest that integrability in consumer-resource dynamics can only arise in cases where each species in an assemblage requires a distinct and unique combination of resources, and even in these cases it is not clear that the resulting dynamics will lead to Lotka-Volterra competition.I acknowledge the Simons Foundation Grant #376199 and the McDonnell Foundation Grant #220020439.

The development of non-invasive approaches for monitoring wildlife populations made it feasible to obtain ecological parameters across landscapes and populations, rather than a few locations or individuals. The two most popular and widespread non-invasive monitoring methods are camera trapping and genetic sampling. The technical development associated with data collection has been impressive, whilst analytical capabilities have lagged behind. Only recently are we getting close to exploiting the potentials of non-invasively obtained data. The objective of my thesis is to apply modern hierarchical analytical models to several sets of carnivore monitoring data to address a series of conceptually and methodologically connected problems, faced by applied ecologists. The thesis consists of four articles. Two of these include simulations, and all four articles involve model fitting and case studies. The latter target a range of species including wolverine and mesocarnivores in Scandinavia and the Himalayan brown bear. Article I quantifies detectability of mesocarnivores by camera traps and sheds light on the behavioural responses of focal species to detection devices and to olfactory lures as an important aspect of detectability. Article II incorporates multiple data sources with varying levels of information in a data-sparse situation and introduces a multiple observation process model in the spatial capture-recapture framework to estimate population parameters. This model is applied to multi-method monitoring data of a Himalayan brown bear population in Pakistan. The focus in Article III is heterogeneity in the environment and it uncovers sex-specific patterns in wolverine home range size across the species’ range in Norway using solely non-invasively collected DNA data and spatial capture-recapture models. Article IV presents and evaluates an extension of the open-population spatial capture-recapture model to improve inferences on population parameters and showcases its application on wolverine data in central Norway. Hierarchical modelling offers ecologists an intuitive multi-level approach to disentangle observation and ecological processes. All chapters of this thesis include hierarchical models that account for imperfect detection. Depending on the research question, I use these models to estimate time-to-detection of species, population abundance and density, survival, variation in home range size and inter-annual movement. The monitoring methods used during this thesis are often applied to studies of rare or elusive species and data sparsity is another important challenge addressed in this thesis. Bayesian inference Using Gibbs Sampling (BUGS) language facilitates the construction of flexible models that make the incorporation of multiple types of data into one comprehensive analysis comparatively straightforward. The articles included in this thesis showcase how hierarchical models help us use non-invasively collected data to yield answers to a range of questions in applied ecology. Tackling the associated challenges increases our ability to draw inferences that more closely describe the complexity of real-world ecological systems. Utviklingen av ikke-invasive metoder for å overvåke dyrepopulasjoner har gjort det mulig å estimere økologiske parametere på tvers av landskap og populasjoner, snarere enn noen få steder eller individer. De to mest populære og utbredte ikkeinvasive overvåkingsmetodene er viltkameraer og genetisk prøvetaking. Den tekniske utviklingen knyttet til datainnsamling har vært imponerende, mens analytiske evner har hengt etter. Først nylig har vi kommet i nærheten av å utnytte potensialet til ikke-invasivt innsamlede data. Målet med avhandlingen min er å bruke moderne hierarkiske analytiske modeller på flere sett med overvåkningsdata av rovdyr for å adressere en serie konseptuelt og metodisk koblede problemer, som anvendte økologer møter. Oppgaven består av fire artikler. To av disse inkluderer simuleringer, og alle de fire artiklene involverer modelltilpassing og case-studier på en rekke arter, inkludert jerv og mesokarnivorer i Skandinavia og Himalaya brunbjørn.

Two approaches have been classically used in disease ecology to estimate epidemiological parameters from field studies: cross-sectional sampling from unmarked individuals and longitudinal capture-recapture setups, which generally involve more limited numbers of marked individuals due to cost and logistical constraints. Although the benefits of longitudinal setups are increasingly acknowledged in the disease ecology community, cross-sectional data remain largely overrepresented in the literature, probably because of the inherent costs of longitudinal surveys. In this context, we used simulated data to compare the performances of cross-sectional and longitudinal designs to estimate the force of infection (i.e., the rate at which susceptible individuals become infected). Then, inspired from recent method developments in quantitative ecology, we explore the benefits of integrating both cross-sectional (seroprevalences) and longitudinal (individuals histories) data sets. In doing so, we investigate the effects of host species life history, antibody persistence, and degree of a priori knowledge and uncertainty on demographic and epidemiological parameters, as those are expected to affect in different ways the level of inference possible from the data. Our results highlight how those elements are important to consider in determining optimal sampling designs. In the case of long-lived species exposed to infectious agents resulting in persistent antibody responses, integrated designs are especially valuable as they benefit from the performances of longitudinal designs even with relatively small longitudinal sample sizes. As an illustration, we apply this approach to a combination of empirical and simulated data inspired from a case of bats exposed to a rabies virus. Overall, this work highlights that serology field studies could greatly benefit from the opportunity of integrating cross-sectional and longitudinal designs.

In recent years, tissue-resident memory T cells (TRM) have emerged as essential components of immunological memory. Following antigenic challenge, TRM remain in nonlymphoid tissues and defend against re-exposure. Although accumulating evidence suggests important roles for TRM in mediating protective immunity, fundamental aspects of the population biology of TRM remain poorly understood. In this article, we discuss how results from different systems shed light on the ecological dynamics of TRM in mice and humans. We highlight the importance of dissecting processes contributing to TRM maintenance, and how these might vary across phenotypically and spatially heterogeneous subsets. We also discuss how the diversity of TRM communities within specific tissues may evolve under competition and in response to antigenic perturbation. Throughout, we illustrate how mathematical models can clarify inferences obtained from experimental data and help elucidate the homeostatic mechanisms underpinning the ecology of TRM populations.

Here, I argue that we should abandon the division between "field ecologists" and "modelers," and embrace modeling and empirical research as two powerful and often complementary approaches in the toolbox of 21st century ecologists, to be deployed alone or in combination depending on the task at hand. As empirical research has the longer tradition in ecology, and modeling is the more recent addition to the methodological arsenal, I provide both practical and theoretical reasons for integrating modeling more deeply into ecosystem research. Empirical research has epistemological priority over modeling; however, that is, for models to realize their full potential, and for modelers to wield this power wisely, empirical research is of fundamental importance. Combining both methodological approaches or forming "super ties" with colleagues using different methods are promising pathways to creatively exploit the methodological possibilities resulting from increasing computing power. To improve the proficiency of the growing group of model users and ensure future innovation in model development, we need to increase the modeling literacy among ecology students. However, an improved training in modeling must not curtail education in basic ecological principles and field methods, as these skills form the foundation for building and applying models in ecology.

This Data Management Plan (DMP) was created for Ethan White's Moore Investigator in Data Driven Discovery award. It describes the management and sharing of all data and code associated with Gordon and Betty Moore Foundation grant GBMF4563 (White 2014). This includes raw data collected as part of the proposal, data compilations, and software. Research associated with this award is related to data-intensive approaches to studying ecological systems and the development of software for automating the cleaning, restructuring, and integration of heterogenous data sources. Detailed descriptions of data and software management, archiving, and publishing are provided.

Across the tropics, millions of rural families rely on non-timber forest products for protein, subsistence, and other financial or cultural uses. Often, communities exploit biotically dispersed trees and their mammalian or avian seed disperser. Empirical findings have indicated that many plant and animal resources are overexploited, presenting challenges for biodiversity conservation and sustainable rural livelihoods. However, there has been limited research investigating the impacts of harvest that targets both seed dispersers and zoochoric trees. We formulated a discrete-time model for interacting seed dispersers and plants under harvest. We found that the more dependent species will dictate the sustainable threshold level of harvest, and that higher levels of dependence could drive the species pair to local extinction. We illustrated the application of sensitivity analysis to our modeling framework in order to facilitate future analyses and applications using this approach.

The abundance or yield of a species is an ecological quantity of paramount importance when making management and conservation decisions. The linear generalized Lotka–Volterra equations (LGLVE) constitute the simplest theoretical framework for predicting such yields. However, in many ecological communities (e.g. tropical forests), the number of coexisting species S can be very large and estimating the entire interaction matrix A from experiments is practically unfeasible. Here, I introduce a method for predicting the yield of a particular species of interest, or focal species. The method proposed here only requires data on the interaction coefficients of the focal species k, referring to the elements of row and column k of the approximate focal interaction matrix A(k). These interaction coefficients can be obtained from measuring the yields of species in monoculture (S experimental treatments) and in biculture experiments involving the focal species (S − 1 treatments). The remaining elements of A(k) are considered equal to −1. I compare the species yields predicted by the focal species method against a data set of experimental studies I compiled from the literature across several taxa −plants, algae, protozoa and crustacean. The accuracy of this method is slightly lower than that attained when using the entire Lotka-Volterra interaction matrix. The advantage of this method is that it requires a much smaller number of experimental treatments to estimate parameters (of the order of S rather than of S2). Therefore, it can be a useful quantitative tool for applied sciences like agriculture or fisheries.

© 2015 by the Ecological Society of America. The role of theory within ecology has changed dramatically in recent decades. Once primarily a source of qualitative conceptual framing, ecological theories and models are now often used to develop quantitative explanations of empirical patterns and to project future dynamics of specific ecological systems. In this essay, I recount my own experience of this transformation, in which accelerating computing power and the widespread incorporation of stochastic processes into ecological theory combined to create some novel integration of mathematical and statistical models. This stronger integration drives theory towards incorporating more biological realism, and I explore ways in which we can grapple with that realism to generate new general theoretical insights. This enhanced realism, in turn, may lead to frameworks for projecting ecological responses to anthropogenic change, which is, arguably, the central challenge for 21st-century ecology. In an era of big data and synthesis, ecologists are increasingly seeking to infer causality from observational data; but conventional biometry provides few tools for this project. This is a realm where theorists can and should play an important role, and I close by pointing towards some analytical and philosophical approaches developed in our sister discipline of economics that address this very problem. While I make no grand prognostications about the likely discoveries of ecological theory over the coming century, you will find in this essay a scattering of more or less far-fetched ideas that I, at least, think are interesting and (possibly) fruitful directions for our field.

Diet estimation is an important field within quantitative ecology, providing critical insights into many aspects of ecology and community dynamics. Quantitative fatty acid signature analysis (QFASA) is a prominent method of diet estimation, particularly for marine mammal and bird species. Investigators using QFASA commonly use computer simulation to evaluate statistical characteristics of diet estimators for the populations they study. Similar computer simulations have been used to explore and compare the performance of different variations of the original QFASA diet estimator. In both cases, computer simulations involve bootstrap sampling prey signature data to construct pseudo-predator signatures with known properties. However, bootstrap sample sizes have been selected arbitrarily and pseudo-predator signatures therefore may not have realistic properties. I develop an algorithm to objectively establish bootstrap sample sizes that generates pseudo-predator signatures with realistic properties, thereby enhancing the utility of computer simulation for assessing QFASA estimator performance. The algorithm also appears to be computationally efficient, resulting in bootstrap sample sizes that are smaller than those commonly used. I illustrate the algorithm with an example using data from Chukchi Sea polar bears ( Ursus maritimus ) and their marine mammal prey. The concepts underlying the approach may have value in other areas of quantitative ecology in which bootstrap samples are post-processed prior to their use.

Density management decision-support systems (e.g., modular-based structural stand density management models (SSDMMs)), which are built upon the modeling platform used to develop stand density management diagrams, incorporate a number of functional relationships derived from forest production theory and quantitative ecology. Empirically, however, the ecological integrity of these systems has not been verified and hence the degree of their compliance with expected ecological axioms is unknown. Consequently, the objective of this study was to evaluate the ecological integrity of six SSDMMs developed for black spruce (Picea mariana) and jack pine (Pinus banksiana) stand-types (natural-origin and planted upland black spruce and jack pine stands, upland natural-origin black spruce and jack pine mixtures, and natural-origin lowland black spruce stands). The assessment included the determination of the biological reasonableness of model predictions by determining the degree of consistency between predicted developmental patterns and those expected from known ecological axioms derived from even-aged stand dynamics theoretical constructs, employing Bakuzis graphical matrices. Although the results indicated the SSDMMs performed well, a notable departure from expectation was a possible systematic site quality effect on the asymptotic yield-density relationships. Combining these results with confirmatory evidence derived from the literature suggest that the site-invariant self-thinning axiom may be untenable for certain stand-types.

In the paper, the behaviour of the model Lotka–Volterra, describing the ratio of populations of predator and victim in the elementary link in the food chain by means of the numerical experiment was investigated. This model is usually used as the basis of most models of quantitative ecology. We investigated the sensitivity of the model to three-time abrupt change of quantitative parameters of reproduction of the victim and mortality of the predator. Constructed phase portraits demonstrate the changes in system parameters and initial conditions. Parametric analysis of the influence of model parameters on the abundance of populations. It is shown that the main influence on the phase trajectory has not the change of quantitative parameters of the system, but the moment of sudden impact. If the instant abrupt increase of speed of reproduction coincides with the growth stage of victim population, such exposure seems to be pushing the vibrating system in the direction of rocking, while the impulse produced in the opposite phase lead to a decline of the oscillation amplitude. One of the conclusions is that the actual manifestation is impossible to determine at what stage the impact occurred and refers it to the predator or the victim. Another conclusion is that systems, including trophic chains, are prone to multiple stationary states, bifurcations and chaotic jumps from one limit cycle to another. Also, the model of the competition of several species for food resources with the logistic function of growth was calculated. It is shown that because of the implementation of the evolutionary principle of “all or nothing” survive only species with the highest value of the parameter ratio of the constants of procreation and destruction.

book reviews ISSN 1948-6596 books noted with interest Dispersal, Individual Movement and Spatial Ecology: A Mathematical Perspective Mark A. Lewis, Philip K. Maini & Sergei V.Petrovskii 2013, Springer, 385pp. £62.99 (Paperback) ISBN: 978-3-642-35496-0 http://www.springer.com Understanding how the behav- iour of individuals leads to the distribution of populations in space is a challeng- ing topic, and one that has attracted the interest of mathematicians, both theoretical and applied. The sophistication of the approaches presented here goes far beyond the standard in the current biological literature and is not for the faint- hearted or mathematically illiterate. As we in- creasingly recognise that the emergent properties of natural systems depend upon the actions of individuals, it is worth engaging with this research and bridging the divide between disciplines. Chap- ters on invasions and range shifts will be of most relevance to biogeographers. Earth and Life: Global biodiversity, extinction inter- vals and biogeographic perturbations through time John A. Talent (editor) 2012, Springer, 1107 pp. £90 (Hardback), ISBN: 978-90-481-3427-4 http://www.springer.com The key to understanding future changes in the natural world is contained in the past, a perspective to which this substantial tome makes an impressive contribu- tion. An open editorial approach has led to highly variable chapters, best viewed as a series of 36 reviews which range from overarching summaries to focussed investigations of particular time peri- ods, taxa or localities. Together they succeed in documenting not only the ever-increasing diver- sity of life but its many setbacks, probing far be- yond the standard 'big five' mass extinctions and explaining not only what was lost (and why) but what survived and the long-term repercussions. Encyclopedia of Modern Coral Reefs: Struc- ture, Form and Process David Hopley (editor) 2011, Springer, 1236 pp. £359.30 (Hardback) ISBN: 978-90-481-2638-5 http://www.springer.com Another addition to Springer's range of mammoth tomes ad- dressing important fields across the sciences, this cov- ers the geology, biotic composition and physical geog- raphy of coral reefs around the world though 260 fully referenced entries. These are dedicated to reef features, descriptions of specific regions, methodo- logical approaches, and biographies of notable figures in the history of coral reef science from Darwin on- wards. Presented largely from the perspective of earth sciences, this will nonetheless prove to be a valuable reference for biogeographers working in these systems. Climate change has become a domi- nant theme of reef research in recent years and is well-represented here. Sharks of the World: A Fully Illustrated Guide David A. Ebert, Sarah Fowler & Leonard Compagno 2013, Wild Nature Press, 528 pp. £45 (Hardback) ISBN: 978-0-9573946-0-5 http://wildnaturepress.com The importance of predation by sharks in regulating marine systems is now well established, as are the threats to their persistence, thanks to the recent completion of the conservation status of all species by IUCN. What has been lacking to date, however, has been a global-scale assessment of the distribution of the more than 500 shark species (over a third de- scribed in the last decade). After an outline of their overall biology and taxonomy, the appearance, distri- bution, characteristic habitat and behaviour of each species is outlined. Excellent illustrations make this useful as a field guide as well as a data mine. Markus Eichhorn University of Nottingham, UK frontiers of biogeography 5.3, 2013 — © 2013 the authors; journal compilation © 2013 The International Biogeography Society

The problem of quantifying the overlap of Hutchinsonian niches has received much attention lately, in particular in quantitative ecology, from where it also originates. However, the niche concept has the potential to also be useful in many other application areas, as for example in economics. We are presenting a fully nonparametric, robust solution to this problem, along with exact shortcut formulas based on rank-statistics, and with a rather intuitive probabilistic interpretation. Furthermore, by deriving the asymptotic sampling distribution of the estimators, we are proposing the first asymptotically valid inference method, providing confidence intervals for the niche overlap. The theoretical considerations are supplemented by simulation studies and a real data example.

Sound taxonomy is a major requirement for quantitative environmental reconstruction using biological data. Transfer function performance should theoretically be expected to decrease with reduced taxonomic resolution. However for many groups of organisms taxonomy is imperfect and species level identification not always possible. We conducted numerical experiments on five testate amoeba water table (DWT) transfer function data sets. We sequentially reduced the number of taxonomic groups by successively merging morphologically similar species and removing inconspicuous species. We then assessed how these changes affected model performance and palaeoenvironmental reconstruction using two fossil data sets. Model performance decreased with decreasing taxonomic resolution, but this had only limited effects on patterns of inferred DWT, at least to detect major dry/wet shifts. Higher-resolution taxonomy may however still be useful to detect more subtle changes, or for reconstructed shifts to be significant.

Summary The number of published meta-analyses in plant ecology has increased greatly over the last two decades. Meta-analysis has made a significant contribution to the field, allowing review of evidence for various ecological hypotheses and theories, estimation of effects of major environmental drivers (climate change, habitat fragmentation, invasive species, air pollution), assessment of management and conservation strategies, and comparison of effects across different temporal and spatial scales, taxa and ecosystems, as well as research gap identification. We identified 322 meta-analyses published in the field of plant ecology between 1996 and 2013 in 95 different journals and assessed their methodological and reporting quality according to standard criteria. Despite significant recent developments in the methodology of meta-analysis, the quality of published meta-analyses was uneven and showed little improvement over time. We found many cases of imprecise and inaccurate usage of the term ‘meta-analysis’ in plant ecology, particularly confusion between meta-analysis and vote counting and incorrect application of statistical techniques designed for primary studies to meta-analytical data, without recognition of the violation of statistical assumptions of the analyses. Methodological issues for meta-analyses in plant ecology include incomplete reporting of search strategy used to retrieve primary studies, failure to test for possible publication bias and to conduct sensitivity analysis to test the robustness of the results, as well as lack of availability of the data set used for the analyses. The use of meta-analysis is particularly common in community ecology, ecophysiology and ecosystem ecology, but meta-analyses in ecophysiology are more likely not to meet standard quality criteria than papers in other subdisciplines. Fewer meta-analyses have been conducted in plant population ecology. Synthesis. Over the past two decades, plant ecologists have embraced meta-analysis as a statistical tool to combine results across studies, and much has been learned as a result. However, as the popularity and usage of meta-analysis in the field of plant ecology has grown, establishment of quality standards, as has been done in other disciplines, becomes increasingly important. In order to improve the quality of future meta-analyses in plant ecology, we suggest adoption of a checklist of quality criteria for meta-analysis for use by research synthesists, peer reviewers and journal editors.

Common people and even scientists think of ecology as a discipline that exclusively studies wildlife and topics related to environmental pollution. My friends both within and outside the scientific community are often ba2ed when they hear that I am a physicist doing research in ecology. The aim of this series of articles is to emphasize the less known fact that theory and mathematics have been central to ecology since the inception of this relatively young scientific field. In this first article, I will talk about the following three points. First, I will discuss how the emphasis of the basic science of ecology is much broader than its applied aspects involving the conservation of natural ecosystems. Second, I will discuss a fundamental parallel between statistical physics and ecology that arises because both disciplines emphasize macroscopic systems (e.g., magnetic materials in physics or flocks of birds in ecology) as collectives of interacting units that are more than the sum of their constituents. What makes them fascinating is that interactions at small scales typically give rise to unexpected properties at larger scales. Finally, I will discuss how ecology offrs a new and rich set of challenges to mathematically trained scientists because of variations among biological organisms and the role of natural selection in shaping ecological systems, both of which have no parallels in the physical sciences.