A CONOP9 composite taxon range chart for Ordovician conodonts from Baltoscandia: a framework for biostratigraphic correlation and maximum-likelihood biodiversity analyses

Subject.The article is aimed to evaluate of the conodont diversity dynamics at the species level in the Famennian – Serpukhovian interval.Materials and methods.The database compiled from the published and original data contains information on the stratigraphic ranges of 389 Famennian-Serpukhovian conodont species (https://1drv.ms/x/s!AvPFMTPLPc7T4nFU81CaO5UJ6nlw). Conodont zones compose the geochronological basis of the database. The conodont diversity, origination, extinction, and diversification were calculated. Dynamics of these parameters in the Late Devonian–Early Carboniferous was analyzed.Results.The Famennian-Serpukhovian conodonts demonstrate four cycles in the diversity: the early Famennian (triangularis-early postera zones), the late Famennian (late postera-praesulcata zones), the Tournaisian (sulcata-anchoralis zones), and the Visean-Serpukhovian (texanus-bollandensis zones). The cycles are separated by the low-diversity episodes. The highest diversity (80 species) is detected in the early and late marginifera zones (Famennian).Conclusions.The successive decreasing in diversity comprises interval from the late Famennian through Serpukhovian. The global events gave little influence on the conodont diversity except for the Frasnian/Famennian (about 70% conodont species became extinct) and Devonian/Carboniferous extinction events. Conodont diversity demonstrates weak dependence form the global sea level fluctuations. The transition from the green-house to ice-house climate at the beginning of the Carboniferous and successive changes in the marine ecosystems are considered as main probable cause of the decline in conodont diversity in the late Tournaisian-Serpukhovian.

Estimates of species' vital rates and an understanding of the factors affecting those parameters over time and space can provide crucial information for management and conservation. We used mark–recapture, reproductive output, and territory occupancy data collected during 1985–2013 to evaluate population processes of Northern Spotted Owls (Strix occidentalis caurina) in 11 study areas in Washington, Oregon, and northern California, USA. We estimated apparent survival, fecundity, recruitment, rate of population change, and local extinction and colonization rates, and investigated relationships between these parameters and the amount of suitable habitat, local and regional variation in meteorological conditions, and competition with Barred Owls (Strix varia). Data were analyzed for each area separately and in a meta-analysis of all areas combined, following a strict protocol for data collection, preparation, and analysis. We used mixed effects linear models for analyses of fecundity, Cormack-Jolly-...

Monitoring variation in population features such as abundance and density is essential for evaluating and implementing conservation actions. Camera trapping can be important for assessing population status and trends and is increasingly used to generate density estimates through capture–recapture models. Moreover, success in using this technique can vary seasonally given shifting animal distributions and camera encounter rates. Notwithstanding these potential advantages, a gap still exists in our understanding of the performance of such models for estimating density of cryptic Neotropical terrestrial carnivores with low encounter rate probability with cameras. In addition, scanty information is available on how sampling design can affect the accuracy and precision of density estimates for Neotropical carnivores. We evaluate the performance of spatially explicit versus nonspatial capture–mark–recapture models for estimating densities and population size of ocelots (Leopardus pardalis) within an Atlantic Forest fragment in Brazil. We conducted two spatially concurrent surveys, a random camera-trap deployment covering the entire study area and a systematic camera-trap deployment in a small portion of the study area, where trails and unpaved roads were located. We obtained 244 photographs of ocelots in the Rio Doce State Park from April 2016 to November 2017, using 54-double camera stations spaced approximately 1.5 km apart (random placement) totaling 4,320 trap-nights and 15-double camera stations spaced from 0.3–10 km apart (systematic placement) totaling 1,200 trap-nights. Using the random placement design, ocelot density estimates were similar during the dry season, 14.0 individuals/km2 (± 5.6 SE, 6.6–30.0, 95% CI) and 13.78 individuals/km2 (± 4.25 SE, 5.4–22.1, 95% CI) from spatially explicit capture–recapture and nonspatial models, respectively. Using the systematic placement design spatially explicit models had smaller and less precise ocelot density estimates than nonspatial models during the dry season. Ocelot density was 12.4 individuals/100 km2 (± 5.0 SE, 5.8–26.7, 95% CI) and 19.9 individuals/km2 (± 5.2 SE, 9.7–30.1, 95% CI) from spatially explicit and nonspatial models, respectively. During the rainy season, we found the opposite pattern. Using the systematic placement design, spatial-explicit models had higher and less precise estimates than nonspatial models. Ocelot density was 24.6 individuals/100 km2 (± 13.9 SE, 8.7–69.4, 95% CI) and 11.89 individuals/km2 (± 3.93 SE, 4.19–19.59, 95% CI) from spatially explicit and nonspatial models, respectively. During the rainy season, we could not compare models using the random placement design due to limited number of recaptures to run nonspatial models. In addition, a single recapture yielded an imprecise population density estimate using spatial models (high SE and large 95% CIs), thus precluding any comparison between nonspatial and spatially explicit models. We demonstrate relative differences and similarities between the performance of spatially explicit and nonspatial capture–mark–recapture models for estimating density and population size of ocelots and highlight that both types of capture–recapture models differ in their estimation depending on the sampling design. We highlight that performance of camera surveys is contingent on placement design and that researchers need to be strategic in camera distribution according to study objectives and logistics. This point is especially relevant for cryptic or endangered species occurring at low densities and having low detection probability using traditional sampling methods.

One of the major mass extinctions of the Phanerozoic took place at the Rhaetian-Hettangian boundary. According to some researchers, it might have been preceded by a minor event at the end of the Carnian. The faunal association present in the Late Trixsic formations of Lombardy and their changes related with the lithofacies changes are analysed here. In the Carnian Gorno Formation and Val Sabbia Sandstone, five bivalve assemblages have been distinguished. From the trophic analysis of these molluscs, we observe the infaunal suspension feeders predominance in the 1st and 2nd assemblage. They are gradually replaced by epibyssate species in the following associations, closely related with the lithofacies changes. In the overlying Norian Dolomia Principale, most bivalves disappear. They are replaced by species of Neomegaladon , Isognomon and gastropods. It is pointed out that this decline is not due to mass extinction, but to the settlement of different conditions in the basin which fitted better the euryhaline bivalve assemblages. In the overlying Riva di Solto Argillite, Zu Limestone and Conchodon Dolomite, 4 bivalve assemblage zones have been distinguished. Both, the new genera appearance and the number of species increase are closely linked to the substitution of calcareous facies of peritidal platform with argillaceous and arenaceous sequences of inner basin. In the Hettangian Sedrina Limestone, the fauna treated by Gaetani (1970), rich in bivalves, is examined. The diversity and the phyletic relationship of this species with those of the Rhaetian zones are pointed out. The diversity, the origination and extinction rate are calculated for the bivalve species of Lombardy and for the Megalodontidae and Dicerocardiidae revised by Vegh-Neubrandt (1982). Eventually, some morphologic changes in the phyletic lineage of megalodontids have been pointed out too. Concluding, faunal crisis like mass extinction is not recorded by the bivalve assemblages of the Late Triassic of Lombardy and Southern Alps in general. They reflect pseudoextinctions, sudden and severe turnover, certainly depending on the changing environmental conditions. The affinity among the late Rhaetic bivalves and those of the Hettangian demostates that the big faunal crisis at the Rhaetian-Hettangian boundary in Lombardy is traceable back to a rapid faunal turnover consequent to the rifting and sinking of the carbonate platform.

In the modern ecology there appears to be an increasing gap between field-based biologists and statisticians as new methods are developed to deal with more complex data. This book aims to bridge that gap with the goal of helping biologists understand state-of-the-art statistical methods for capture–recapture analysis. The editors have gathered the most complete and upto-date information on the subject and only time will tell if this reduces the gap between biologist and statistician. The book comprises of three main sections. Section one is a single chapter which acts as a general introduction to capture–recapture techniques and sets the scene for the rest of the book. It also gives a brief overview and history of capture–recapture methods from their first use in 1802 to the present day, introduces the scientific notation required for the rest of the book and gives a brief summary of the rest of the model chapters. Section two is the largest section and contains seven chapters which deal with the theoretical and statistical aspects of the main capture–recapture models. Section three consists of two chapters which give a series of examples analysed by the methods described in Section two. Chapters 2–8 introduce increasingly complex capture–recapture models from the original simple closed population model to complex multistate models. Although the authors differ between chapters, all follow a similar format and this makes it particularly easy to compare different models. Each chapter starts with a brief history of the model and its original derivation or formulation, including important researchers and crucial papers. Then follows a derivation of the parameter estimates, a list of assumptions of the method and a discussion of the estimate properties. Within each chapter potential model variations are also discussed. Finally there is a worked example using a variety of software programs that apply to the data set (e.g. JOLLY, MARK, CAPTURE), and a summary of the main points of the chapter. The editors suggest reading Chapters one and two thoroughly to understand the theoretical underpinning of the various models before moving on to subsequent chapters. I agree wholeheartedly with their suggestion, as it would be easy to become confused in later chapters without a detailed understanding of the basic models. Once the basics are well understood many will want to move immediately to the model that best fits their data set. As if predicting this possibility, Chapters 4–8 are self-contained, with each one dealing with the appropriate analysis of a specific type of data – closed populations models, open population models, tag recovery models, joint tag recovery and live resighting models and finally multistate models. From a practical viewpoint Chapter 9 is particularly useful. In it the authors use data from three longterm studies (European dippers, polar bears and mallard ducks) to illustrate the models described in earlier chapters using the computer program MARK. It is also a gentle introduction to MARK, which is the most up-to-date and commonly used capture–recapture program (see http://www.warnercnr.colostate.edu/ ~gwhite/mark/mark.htm to download the MARK program, manual and a wealth of other information). It would be helpful while reading this chapter to have the program MARK open on the computer so that you can work through each example using MARK as it’s being analysed in the book. Most examples use the European dipper data set (which is available on the web), but the polar bears are used to demonstrate a model where survival and recapture probabilities are not constant (something most field-based ecologists would have to consider) while the mallards are used to illustrate the tag recovery model. The editors have done an admirable job in trying to make complex capture–recapture models accessible to a greater range of field-based ecologists. Despite the scope and nature of this book, I feel that the best analysis methods will still be when ecologists and statisticians collaborate on a particular data set. I think many ecologists will find the time required to understand anything more than the simplest capture– recapture models prohibitive. This book will not put statisticians out of a job, rather it should allow for a more informed discussion when ecologists and statisticians collaborate on projects involving capture– recapture methods (which should be right from the start).

Conodont diversity and evolution through the latest Permian and Early Triassic upheavals

Many ecological systems are organized hierarchically, and a full understanding of ecological systems is contingent on our understanding of linkages across levels in these hierarchies. Consider the hierarchy whereby individual organisms are organized into populations. The dynamics of animal populations are influenced by individual-level processes, such as establishment of home ranges and selection of habitat. These individual-level processes have important effects on demography, including reproduction, survival, emigration, and immigration, which collectively determine population dynamics. In turn, population dynamics influence how individual-level processes unfold; individual organisms are shaped by the populations they are a part of through forces such as resource competition and social interactions. Our ability to investigate both population- and individual-level processes has seen substantial growth in recent decades. Since the early 2000s, the study of animal population dynamics has been transformed due in part to the growing field of spatial capture–recapture modeling. Capture–recapture (also referred to as capture–mark–recapture, mark–recapture, and related terms) is a large set of statistical methods commonly used by ecologists to estimate abundance and related demographic parameters when it is difficult or impossible to observe (or "detect") all individuals in a population. Spatial capture–recapture is an important extension in which both demographic processes and our (imperfect) observation of these processes are modeled as spatially explicit. Spatial capture–recapture models have expanded our capacity for studying populations and have provided new insights into how populations function in space. Similarly, the study of individual movement has advanced in recent decades, owing largely to the rapid development of animal tracking technology and the simultaneous development of advanced statistical models of animal movement for tracking data. Increasingly realistic models of animal movement have provided exciting new insights about individual behavior, habitat selection, and space use. However, despite the potential for integration of these two frameworks to revolutionize our ability to understand linkages between population- and individual-level processes, there has been relatively little integration to date. In August 2019, we helped to organize a workshop at the University of Washington, with the express goal of advancing the integration of spatial capture–recapture and movement models. The papers in this Special Feature are an outgrowth of that workshop, are strongly influenced by it, or are based on independent work developed with similar goals. This collection of papers represents the state of the art in the integration of spatial capture–recapture and movement models. The papers demonstrate both the practical challenges of integrating these frameworks and the benefits of doing so, from improved demographic estimates for populations with complex movement dynamics to novel ecological insights into how environmental and social forces shape populations through space use. The papers in this Special Feature range from conceptual to applied, and each includes practical insights into how to fit these relatively complex integrated models. In their review and synthesis, McClintock et al. (2021) highlight the advantages of linking individual- and population-level process models to facilitate new and exciting inferences at the intersection of movement, population, and landscape ecology. They establish a common notation for the Special Feature and outline a general conceptual framework for the integration of spatial capture–recapture and animal movement models. They also identify potential challenges that lie ahead. Gardner et al. (2022) implement complex movement processes—such as simple random walks, correlated random walks, and habitat-driven Langevin diffusion—within spatial capture–recapture models using data augmentation in a Bayesian analysis framework. Using simulation, they demonstrate that these models can perform well with spatial capture–recapture data alone, but that as movement model complexity increases, there will be a need for more intensive location data. Thus, they also show how to integrate auxiliary data from animal-borne sensors to improve parameter estimation over models fit with only spatial capture–recapture data. Theng et al. (2022) explore the consequences of realistic animal movement for inferences arising from standard spatial capture–recapture models of closed population abundance and density. By simulating individual-level responses to internal (e.g., memory, territoriality) and external (e.g., resource dynamics) drivers as animals move through the landscape, the researchers demonstrate that spatial capture–recapture estimators of abundance can be robust to violations of assumptions induced by complex animal movement patterns as long as the resulting individual heterogeneity in detection is low. However, inferences about animal space use and home range size from standard spatial capture–recapture models can be problematic, and integrated spatial capture–recapture and animal movement models offer a potential solution. Much of the focus of the Special Feature is on animals that move independently of one another. However, in group-living species (e.g., many canids and ungulates), animal movement is statistically dependent, violating assumptions of traditional spatial capture–recapture models. To generate unbiased estimates of abundance and group size with properly estimated precision, Emmet et al. (2021) develop a group-living spatial capture–recapture model based on a clustered point process. They test their model using simulation and then apply it to camera trapping data on African wild dogs. Although their model currently requires a few restrictive assumptions (e.g., that group membership is known), we share their optimism that such requirements can be relaxed in future applications and we anticipate that their contribution will lay the groundwork for many future studies of group-living species. Focusing on landscape connectivity, Dupont et al. (2021) extend spatial capture–recapture models to accommodate a movement kernel based on so-called ecological distance instead of Euclidean distance. Unlike other integrated approaches in the Special Feature (i.e., Chandler et al., 2021; Gardner et al., 2022; Hostetter et al., 2022; McClintock et al., 2021), Dupont et al. (2021) use a step-selection model and discrete-space approximation for movement that can be fitted using maximum likelihood methods. Though it incorporates some restrictive assumptions, the model reduces computational burdens by avoiding the need to integrate over the latent movement paths during model fitting. This approach provides a straightforward modeling framework for including global positioning system (GPS) telemetry data to improve estimates of habitat-related cost functions. Hostetter et al. (2022) were motivated by the practical need to estimate the density of animals that move over large spatial areas. Polar bears (Ursus maritimus) can travel hundreds of kilometers over the course of mere days, exhibiting movement dynamics that clearly violate the standard spatial capture–recapture assumption of a bivariate normal home range and over areas that cannot be adequately sampled by available platforms. Using a combination of physical captures, resights, and telemetry data, they fit a series of integrated spatial capture–recapture movement models that specify more realistic movement processes, including simple and correlated random walks, and model the detection process over space and time conditional on movements. With this model, they provide robust estimates of movement and abundance for polar bears in the remote Chukchi Sea, as well as a framework for monitoring populations of highly mobile vertebrates in heterogeneous landscapes. Inspired by a white-tailed deer (Odocoileus virginianus) study where GPS telemetry and camera trapping were employed in the same study area, Chandler et al. (2021) develop a hierarchical model that integrates both data sets into a single analysis. By conditioning both data sets on a common movement model, the authors are able to estimate abundance and movement parameters simultaneously. Importantly, they are able to account for heterogeneous space use by different animals in a way that is typically not possible with spatial capture–recapture data alone. We suspect that their approach will be especially useful for those wishing to study the synergy between demography and animal behavior and to scale up inference about movement processes from individuals to populations. As the articles in this Special Feature illustrate, there is tremendous potential for modeling more realistic movement processes that integrate the social and environmental features of landscapes to which animals are responding while using the insights that emerge from these processes to understand demography. Our hope is that the Special Feature will inspire continued advances in integrated spatial capture–recapture movement models. The authors declare no conflict of interest.

Analysis of Capture–Recapture Data by McCrea and Morgan is an excellent, easy to read monograph about capture–recapture models. In this book, the authors provide a concise overview of traditional closed population capture–recapture models (Models M0, Mb, Mh, etc.), individual covariate models, and open population models such as the Cormack–Jolly– Seber, Jolly–Seber models, multi-state models, andmore recent developments such as occupancy models, state-space models, and integrated population models. The authors write “In this book we aim to cover the many modern developments in the area of capture–recapture and related models, and to set them in historical context of relevant research over the past 100 years.” The book does a good job of achieving this objective. And it is a very easy to read because it is well organized and the writing is clear and concise. I would recommend this book as a reference for the quantitative ecologist or statistician interested in knowing what’s out there. And I’m glad to have it on my bookshelf. The main topical chapters focus on specific classes of capture–recapture models and contain a mix of classical concepts and methods as well as more recent innovations. For example, the chapter on closed population models contains material on N-mixture models and also spatial capture–recapture models1. There is a strong emphasis on mark-recovery models which are important when band recoveries are available. I think chapters 11 and 12 are the strongest. Chapter 11, on state-space models (covering Gaussian time-series models and Kalman filtering), is not specifically a capture–recapture topic but sometimes used as a component of integrated population models and the concepts are of general interest in population modeling. This chapter also covers state-space formulations of CJS and

For mammals today, mountains are diverse ecosystems globally, yet the strong relationship between species richness and topographic complexity is not a persistent feature of the fossil record. Based on fossil-occurrence data, diversity and diversification rates in the intermontane western North America varied through time, increasing significantly during an interval of global warming and regional intensification of tectonic activity from 18 to 14 Ma. However, our ability to infer origination and extinction rates reliably from the fossil record is affected by variation in preservation history. To investigate the influence of preservation on estimates of diversification rates, I simulated fossil records under four alternative diversification hypotheses and six preservation scenarios. Diversification hypotheses included tectonically controlled speciation pulses, while preservation scenarios were based on common trends (e.g., increasing rock record toward the present) or derived from fossil occurrences and the continental rock record. For each scenario, I estimated origination, extinction, and diversification rates using three standard methods—per capita, three-timer, and capture–mark–recapture (CMR) metrics—and evaluated the ability of the simulated fossil records to accurately recover the underlying diversification dynamics. Despite variable and low preservation probabilities, simulated fossil records retained the signal of true rates in several of the scenarios. The three metrics did not exhibit similar behavior under each preservation scenario: while three-timer and CMR metrics produced more accurate rate estimates, per capita rates tended to better reproduce true shifts in origination rates. All metrics suffered from spurious peaks in origination and extinction rates when highly volatile preservation impacted the simulated record. Results from these simulations indicate that elevated diversification rates in relation to tectonic activity during the middle Miocene are likely to be evident in the fossil record, even if preservation in the North American fossil record was variable. Input from the past is necessary to evaluate the ultimate mechanisms underlying speciation and extinction dynamics.

Simple SummaryThe current rates of climate change are unprecedented, and biological responses to these changes are occurring rapidly at the ecosystem, community, and species levels. Climate change is a significant threat to freshwater biodiversity, resulting in higher extinction and local extinction rates among freshwater species than terrestrial taxa. In the current era of rapid environmental change and species loss, environmental DNA (eDNA) offers a more efficient and rapid method to monitor and conserve freshwater biodiversity. The use of eDNA metabarcoding has been recognized as a powerful technique for obtaining extensive data on biodiversity.Freshwater ecosystems are a significant entity that govern the livelihood of people and are an important source of food, employment, and recreation. However, climate change is impacting freshwater ecosystems by altering their natural habitats. The purpose of this review is to highlight the vulnerability of freshwater fish to climate change. Climate change is invariably affecting natural ecosystems everywhere and in every part of the world, but these threats are more severe in Pakistan. Freshwater fish are important biotic drivers of freshwater ecosystems. Unfortunately, uncertain climate changes and anthropogenic activities have led to a decline in the diversity of these fishes. Rising temperatures, melting glaciers, changes in seasonal patterns, disturbances in the natural flow of rivers, pollution, and invasive species are major threats to native freshwater fish fauna, leading to a decline in fish diversity and population. Tor putitora, Glyptothorax kashmirensis, and Triplophysa kashmirensis are some of the species that are critically endangered in Pakistan due to these factors. In recent decades, insufficient attention has been paid to the freshwater ecosystem. This review of threats to the endemic fish species in this region is presented so that the government and policymakers can use this information as part of their management and conservation policy, thus safeguarding Pakistan’s fish industry. Environmental DNA (eDNA) biomonitoring is a new technique for assessing biodiversity and species distribution and can be useful for conserving biodiversity in this region. Another purpose of this review is to introduce this new conservation strategy to Pakistan.

Abstract1. Species richness is the most widely used biodiversity index, but can be hard to measure. Many species remain undetected, hence raw species counts will often underestimate true species richness. In contrast, capture–recapture methods estimate true species richness and correct for imperfect and varying detectability.2. Detectability is a crucial quantity that provides the link between a species count and true species richness. For insects, it has hardly ever been estimated, although this is required for the interpretation of species counts.3. In the Swiss butterfly monitoring programme about 100 transect routes are surveyed seven times a year using a highly standardised protocol. In July 2003, control observers made two additional surveys on 38 transects. Data from these 38 quadrats were analysed to see whether currently available capture–recapture models can provide quadrat‐specific estimates of species richness, and to estimate species detectability in relation to transect, observer, survey, region, and abundance.4. Species richness over the entire season cannot be estimated using current capture–recapture methods. The species pool was open, preventing use of closed population models, and detectability varied by species, preventing use of current open population models. Assuming a closed species pool during two mid‐season (July) surveys, a Jackknife capture–recapture method was used that accounts for heterogeneity to estimate mean detectability and species richness.5. In every case, more species were present than were counted. Mean species detectability was 0.61 (SE 0.01) with significant differences between observers (range 0.37–0.83). Species‐specific detection at timet+ 1 was then modelled for those species seen attfor three mid‐season surveys. Detectability averaged 0.50 (range 0.17–0.81) for individual species and 0.65, 0.44, and 0.42 for surveys. Abundant species were detected more easily, although this relationship explained only 5% of variation in species detectability.6. These are important, although not entirely unexpected, results for species richness estimation of short‐lived animals. Raw counts of species may be misleading species richness indicators unless many surveys are conducted. Monitoring programmes should be calibrated, i.e. the assumption of constant detectability over dimensions of interest needs to be tested. The development of capture–recapture or similar models that can cope with both open populations and heterogeneous species detectability to estimate species richness should be a research priority.

We still have much to learn about the evolution of taxonomic diversity gradients through geologic time. For example, have latitudinal gradients always been as steep as they are now, or is this a phenomenon linked to some form of Cenozoic global climatic differentiation? The fossil record offers potential to address this sort of problem, and this study reconstructs latitudinal diversity gradients for the last (Tithonian) stage of the Jurassic period using marine bivalves. At this time of relative global warmth, bivalves were cosmopolitan in their distribution and the commonest element within macrobenthic assemblages.Analysis of 31 regional bivalve faunas demonstrates that Tithonian latitudinal gradients were present in both hemispheres, though on a much smaller magnitude than today. The record of the Northern Hemisphere gradient is more complete and shows a steep fall in values at the tropical/temperate boundary; the Southern Hemisphere gradient exhibits a more regular decline in diversity with increasing latitude.Tithonian latitudinal gradients were underpinned by a tropical bivalve fauna that comprises almost equal numbers of epifaunal and infaunal taxa. The epifaunal component was dominated by three pteriomorph families, the Pectinidae, Limidae and Ostreidae, that may be regarded as a long-term component of tropical bivalve diversity. Of the mixture of older and newer “heteroconch” families that formed the bulk of the infaunal component, the latter radiated spectacularly through the Late Cretaceous and Cenozoic to dominate tropical bivalve faunas at the present day. This pulse of heteroconch diversification, which was a major cause of the steepening of the bivalve latitudinal gradient, provides important evidence that rates of speciation may be negatively correlated with latitude.Nevertheless, we cannot exclude the possibility that elevated extinction rates in the highest latitudes also contributed to the marked steepening of bivalve latitudinal gradients over the last 150 Myr. Rates of extinction within epifaunal bivalve taxa appear to have been higher in these regions through the Cretaceous period, but this was largely before any significant global climatic deterioration. Infaunal bivalve clades have had differential success over this time period in the polar regions. Whereas, in comparison with the Tropics, heteroconchs are very much reduced in numbers today, the anomalodesmatans are much better represented, and the protobranchs have positively thrived. We are beginning to appreciate that low temperature per se may not be a primary cause of elevated rates of extinction. Food supply may be an equally important control on both rates of speciation and extinction; those bivalves that have been able to adapt to the extreme seasonality of food supply have flourished in the polar regions.

SummaryAlthough litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single‐species decay. Recently, litter‐mixing studies have tested whether monoculture data can be applied to mixed‐litter systems. These studies have mainly attempted to detect non‐additive effects of litter mixing, which address potential consequences of random species loss – the focus is not on which species are lost, but the decline in diversityper se.Under global change, species loss is likely to be non‐random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non‐additivity) on decomposition rates are of interest.To examine potential impacts of non‐random species loss on ecosystems, we studied additive and non‐additive effects of litter mixing on decomposition. A full‐factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non‐additive effects into those caused by richness and/or composition.This approach addresses questions key to understanding the potential effects of species loss on ecosystem processes. If additive effects dominate, the consequences for decomposition dynamics will be predictable based on our knowledge of individual species, but not statistically predictable if non‐additive effects dominate.We found additive (identity) effects on mass loss and non‐additive (composition) effects on litter nitrogen dynamics, suggesting that non‐random species loss could significantly affect this system. We were able to identify the species responsible for effects that would otherwise have been considered idiosyncratic or absent when analysed by the methods used in previous work.Synthesis. We observed both additive and non‐additive effects of litter‐mixing on decomposition, indicating consequences of non‐random species loss. To predict the consequences of global change for ecosystem functioning, studies should examine the effects of both random and non‐random species loss, which will help identify the mechanisms that influence the response of ecosystems to environmental change.

Wildlife management and conservation is becoming ever more complex. Concomitantly, managers are in need of simple quantitative approaches and tools that could help them to make better management decisions. We present an approach that consists of generating a single simulated data set of expected data using a reference model, to which various capture–recapture models can be fitted. Using the general-purpose capture–recapture software M-SURGE, we apply this approach to bias, precision, and power calculations. After a quick statistical background refresher, we illustrate this approach with 3 simple examples: 1) the bias induced on survival by capture heterogeneity, 2) the precision of an estimate in the context of a reward-band study, and 3) the power of a test for detecting compensatory mortality. We believe this numerical approach based on expected values can potentially be applied to complex cases, thus making it possible to deal with real situations. We advocate that bias, precision, and power c...

Controls on the Sulfur Isotopic Composition of Carbonate-Associated Sulfate