Mosaicism in a new Eocene pufferfish highlights rapid morphological innovation near the origin of crown tetraodontiforms

Tetraodontiformes (pufferfishes and kin) is a taxonomically and structurally diverse, widely‐distributed clade of acanthomorphs, whose members often serve as models for genomics and, increasingly, macroevolutionary studies. Morphologically disparate Palaeogene fossils suggest considerable early experimentation, but these flattened specimens often preserve limited information. We present a three‐dimensionally preserved beaked tetraodontiform from the early Eocene (c. 53 Ma) London Clay Formation, UK. Approximately coeval with the oldest crown tetraodontiforms, ?Ctenoplectus williamsi gen. et sp. nov. presents an unprecedented combination of characters, pairing a fused beak‐like dentition with prominent dorsal‐fin spines that insert atop transversely‐expanded pterygiophores roofing the skull. Bayesian total‐evidence tip‐dating analysis indicates that ?Ctenoplectus represents the sister lineage of Triodontidae and highlights considerable levels of homoplasy in early tetraodontiform evolution. According to our dataset, rates of morphological character evolution were elevated at the origin of crown Tetraodontiformes, especially within gymnodonts, but declined after the principal body plans were established. Such ‘early burst’ patterns are regarded as a hallmark of adaptive radiations, but are typically associated with diversification at smaller spatiotemporal scales. However, denser sampling of Neogene and Recent taxa is needed to confirm this pattern.     

Seasonal temperature and precipitation regulate brook trout young‐of‐the‐year abundance and population dynamics

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Species traits and catchment‐scale habitat factors influence the occurrence of freshwater mussel populations and assemblages

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Evidential statistics as a statistical modern synthesis to support 21st century science

During the 20th century, population ecology and science in general relied on two very different statistical paradigms to solve its inferential problems: error statistics (also referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great deal of good science was done using these tools, but both schools suffer from technical and philosophical difficulties. At the turning of the 21st century (Royall in Statistical evidence: a likelihood paradigm. Chapman & Hall, London, 1997 ; Lele in The nature of scientific evidence: statistical, philosophical and empirical considerations. The University of Chicago Press, Chicago, pp 191–216, 2004a ), evidential statistics emerged as a seriously contending paradigm. Drawing on and refining elements from error statistics, likelihoodism, Bayesian statistics, information criteria, and robust methods, evidential statistics is a statistical modern synthesis that smoothly incorporates model identification, model uncertainty, model comparison, parameter estimation, parameter uncertainty, pre-data control of error, and post-data strength of evidence into a single coherent framework. We argue that evidential statistics is currently the most effective statistical paradigm to support 21st century science. Despite the power of the evidential paradigm, we think that there is no substitute for learning how to clarify scientific arguments with statistical arguments. In this paper we sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential statistics. We also discuss a number of misconceptions about the paradigms that have hindered practitioners, as well as some real problems with the error and Bayesian statistical paradigms solved by evidential statistics.     

Theoretical insight into three disease-related benefits of migration

Migration (seasonal round-trip movement across relatively large distances) is common within the animal kingdom. This behaviour often incurs extreme costs in terms of time, energy, and/or survival. Climate, food, predation, and breeding are typically suggested as factors favouring the evolution of migration. Although disease regulation has also been considered, few studies consider it as the primary selective pressure for migration. Our aim was to determine, theoretically, under what conditions migration could reduce the long-term disease prevalence within a population, assuming the only benefits of migration are infection-related. We created two mathematical models, one where the population migrates annually and one where the entire population remains on the breeding ground year-round. In each we simulated disease transmission (frequency-dependent and density-dependent) and quantified eventual disease prevalence. In the migration model we varied the time spent migrating, disease-related migration mortality, and the overall migration mortality. When we compared results from the two models, we found that migration generally lowered disease prevalence. We found a population was healthier if it: (1) spent more time migrating (assuming no disease transmission during migration), (2) had higher disease-induced migration mortality, and (3) had an overall higher mortality when migrating (compared to not migrating). These results provide support for two previously proposed mechanisms by which migration can reduce disease prevalence (migratory escape and migratory cull), and also demonstrate that non-selective mortality during migration is a third mechanism. Our findings indicate that migration may be evolutionarily advantageous even if the only migratory benefit is disease control.     

Midpoint attractors and species richness: Modelling the interaction between environmental drivers and geometric constraints

We introduce a novel framework for conceptualising, quantifying and unifying discordant patterns of species richness along geographical gradients. While not itself explicitly mechanistic, this approach offers a path towards understanding mechanisms. In this study, we focused on the diverse patterns of species richness on mountainsides. We conjectured that elevational range midpoints of species may be drawn towards a single midpoint attractor – a unimodal gradient of environmental favourability. The midpoint attractor interacts with geometric constraints imposed by sea level and the mountaintop to produce taxon‐specific patterns of species richness. We developed a Bayesian simulation model to estimate the location and strength of the midpoint attractor from species occurrence data sampled along mountainsides. We also constructed midpoint predictor models to test whether environmental variables could directly account for the observed patterns of species range midpoints. We challenged these models with 16 elevational data sets, comprising 4500 species of insects, vertebrates and plants. The midpoint predictor models generally failed to predict the pattern of species midpoints. In contrast, the midpoint attractor model closely reproduced empirical spatial patterns of species richness and range midpoints. Gradients of environmental favourability, subject to geometric constraints, may parsimoniously account for elevational and other patterns of species richness.     

A life‐history perspective on the demographic drivers of structured population dynamics in changing environments

Current understanding of life‐history evolution and how demographic parameters contribute to population dynamics across species is largely based on assumptions of either constant environments or stationary environmental variation. Meanwhile, species are faced with non‐stationary environmental conditions (changing mean, variance, or both) created by climate and landscape change. To close the gap between contemporary reality and demographic theory, we develop a set of transient life table response experiments (LTREs) for decomposing realised population growth rates into contributions from specific vital rates and components of population structure. Using transient LTREs in a theoretical framework, we reveal that established concepts in population biology will require revision because of reliance on approaches that do not address the influence of unstable population structure on population growth and mean fitness. Going forward, transient LTREs will enhance understanding of demography and improve the explanatory power of models used to understand ecological and evolutionary dynamics.     

Social carry‐over effects underpin trans‐seasonally linked structure in a wild bird population

Spatial structure underpins numerous population processes by determining the environment individuals' experience and which other individuals they encounter. Yet, how the social landscape influences individuals' spatial decisions remains largely unexplored. Wild great tits (Parus major) form freely moving winter flocks, but choose a single location to establish a breeding territory over the spring. We demonstrate that individuals' winter social associations carry‐over into their subsequent spatial decisions, as individuals breed nearer to those they were most associated with during winter. Further, they also form territory boundaries with their closest winter associates, irrespective of breeding distance. These findings were consistent across years, and among all demographic classes, suggesting that such social carry‐over effects may be general. Thus, prior social structure can shape the spatial proximity, and fine‐scale arrangement, of breeding individuals. In this way, social networks can influence a wide range of processes linked to individuals' breeding locations, including other social interactions themselves.     

Translating niche features: Modelling differential exposure of Argentine reptiles to global climate change

Global climate change affects the distributions of ectotherms and may be the cause of several conservation problems, such as great displacement of climatic suitable spaces for species and, consequently, important reductions of the extent of liveable places, threatening the existence of many of them. Species exposure (and hence vulnerability) to global climate change is linked to features of their climatic niches (such as the relative position of the inhabited localities of each species in the climatic space), and therefore to characteristics of their geographic ranges (such as the extent of the distributions or altitudinal range inhabited by the species). In order to analyze the pattern of response of Argentine reptiles to global climate change, we ran phylogenetic generalized least squares models using species exposure to global climate change as a response variable, and (i) niche properties (breadth and position of the species in the climate space) and (ii) general features of the distribution of species (maximum latitude, altitudinal range, maximum elevation, distributional range and proximity to the most important dispersal barrier) as predictors. Our results suggest that the best way to explain climate change exposure is by combining breadth and position of climatic niche of the species or combining geographic features that are indicators of both niche characteristics. Our best model shows that in our study area, species with the narrowest distributional ranges that also inhabit the highest elevations are the most exposed to the effects of global climate change. In this sense, reptile species from Yungas, Puna and Andes ecoregions could be especially vulnerable to the effects of climate change. We believe that these types of models may represent an interesting tool for determining species and places particularly threatened by the effects of global climate change, which should be strongly considered in conservation planning.     

Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population

Demographic buffering allows populations to persist by compensating for fluctuations in vital rates, including disease‐induced mortality. Using long‐term data on a badger (Meles meles Linnaeus, 1758) population naturally infected with Mycobacterium bovis, we built an integrated population model to quantify impacts of disease, density and environmental drivers on survival and recruitment. Badgers exhibit a slow life‐history strategy, having high rates of adult survival with low variance, and low but variable rates of recruitment. Recruitment exhibited strong negative density‐dependence, but was not influenced by disease, while adult survival was density independent but declined with increasing prevalence of diseased individuals. Given that reproductive success is not depressed by disease prevalence, density‐dependent recruitment of cubs is likely to compensate for disease‐induced mortality. This combination of slow life history and compensatory recruitment promotes the persistence of a naturally infected badger population and helps to explain the badger's role as a persistent reservoir of M. bovis.