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Scientific knowledge of species and the ecosystems they inhabit is the cornerstone of modern conservation. However, research effort is not spread evenly among taxa (taxonomic bias), which may constrain capacity to identify conservation risk and to implement effective responses. Addressing such biases requires an understanding of factors that promote or constrain the use of a particular species in research projects. To this end, we quantified conservation science knowledge of the world's extant non-marine mammal species (n = 4108) based on the number of published documents in journals indexed on Clarivate Analytics' Web of Science (TM). We use an innovative hurdle model approach to assess the relative importance of several ecological, biogeographical and cultural factors for explaining variation in research production between species. The most important variable explaining the presence/absence of conservation research was scientific capacity of countries within the range of the species, followed by body mass and years since the taxonomic description. Research volume (more than one document) was strongly associated with number of years since the data describing on that species, followed by scientific capacity within the range of species, high body mass and invasiveness. The threat status was weakly associated to explain the presence/absence and research volume in conservation research. These results can be interpreted as a consequence of the dynamic interplay between the perceived need for conservation research about a species and its appropriateness as a target of research. As anticipated, the scientific capacity of the countries where a species is found is a strong driver of conservation research bias, reflecting the high variation in conservation research funding and human resources between countries. Our study suggests that this bias could be most effectively reduced by a combination of investing in pioneering research, targeted funding and supporting research in countries with low scientific capacity and high biodiversity.

Meta-regression can be used to examine the association between effect size estimates and the characteristics of the studies included in a meta-analysis using regression-type methods. By searching for those characteristics (i.e., moderators) that are related to the effect sizes, we seek to identify a model that represents the best approximation to the underlying data generating mechanism. Model selection via testing, either through a series of univariate models or a model including all moderators, is the most commonly used approach for this purpose. Here, we describe alternative model selection methods based on information criteria, multimodel inference, and relative variable importance. We demonstrate their application using an illustrative example and present results from a simulation study to compare the performance of the various model selection methods for identifying the true model across a wide variety of conditions. Whether information-theoretic approaches can also be used not only in combination with maximum likelihood (ML) but also restricted maximum likelihood (REML) estimation was also examined. The results indicate that the conventional methods for model selection may be outperformed by information-theoretic approaches. The latter are more often among the set of best methods across all of the conditions simulated and can have higher probabilities for identifying the true model under particular scenarios. Moreover, their performance based on REML estimation was either very similar to that from ML estimation or at times even better depending on how exactly the REML likelihood was computed. These results suggest that alternative model selection methods should be more widely applied in meta-regression. ispartof: RESEARCH SYNTHESIS METHODS vol:12 issue:4 pages:537-556 ispartof: location:England status: published

Full‐subsets information theoretic approaches are becoming an increasingly popular tool for exploring predictive power and variable importance where a wide range of candidate predictors are being considered. Here, we describe a simple function in the statistical programming language R that can be used to construct, fit, and compare a complete model set of possible ecological or environmental predictors, given a response variable of interest and a starting generalized additive (mixed) model fit. Main advantages include not requiring a complete model to be fit as the starting point for candidate model set construction (meaning that a greater number of predictors can potentially be explored than might be available through functions such as dredge); model sets that include interactions between factors and continuous nonlinear predictors; and automatic removal of models with correlated predictors (based on a user defined criterion for exclusion). The function takes continuous predictors, which are fitted using smoothers via either gam, gamm (mgcv) or gamm4, as well as factor variables which are included on their own or as two‐level interaction terms within the gam smooth (via use of the “by” argument), or with themselves. The function allows any model to be constructed and used as a null model, and takes a range of arguments that allow control over the model set being constructed, including specifying cyclic and linear continuous predictors, specification of the smoothing algorithm used, and the maximum complexity allowed for smooth terms. The use of the function is demonstrated via case studies that highlight how appropriate model sets can be easily constructed and the broader utility of the approach for exploratory ecology.

Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi‐stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential‐by‐sub‐model strategies focus on one sub‐model (parameter) at a time with modeling of subsequent sub‐models dependent on the selected sub‐model structures from the previous stages. Secondary candidate set strategies model sub‐models independently and combine the top set of models from each sub‐model for selection in a final stage. Build‐up approaches define stages across sub‐models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex sub‐model structures for non‐target sub‐models. We tested the performance of different model selection strategies using four data sets and three model types. For each data set, we determined the "true" distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of "true" AIC weight we recovered under different model selection strategies. Sequential‐by‐sub‐model strategies often performed poorly. Based on our results, we recommend using a build‐up or secondary candidate sets, which were more reliable and carrying all models within 5–10 AIC of the top model forward to subsequent stages. The structure of non‐target sub‐models was less important. Multi‐stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub‐models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.

The inter-tidal zone around sewage discharges in a Southwest Atlantic shore (Mar del Plata, Argentina) is currently colonized by extensive inter-tidal reefs of the invasive spionid Boccardia proboscidea. Understanding the links between both human and natural disturbances and the massive development of non-indigenous species will help prevent marine bioinvasions, which are already favoured by global oceanic trade. We present herein predictive models for variations in the density of B. proboscidea around sewage discharges of Mar del Plata, using environmental (pH, turbidity, temperature, salinity and total organic matter content), weather (wind direction and storm records), spatial (sites) and temporal (season and year) variables. Density variations were modelled by generalized linear models, and model averaging (multimodel inference) was used to obtain predicted values. The highest predicted values of B. proboscidea density occurred at sites to the south of the sewage effluent in spring. These sites are more affected by urban effluent discharges and they showed increased B. proboscidea density when the north wind was predominant. In addition, B. proboscidea density values were higher in sites with 20–22 °C (seawater temperature), high total organic matter content in sediments and low salinity. The averaged model was only a good ‘predictive model’ for sites to the north of the outfall, but was useful as an ‘explanatory model’ in all sites. Such predictions may help to back up conservation and management policies and decisions. Fil: Garaffo, Griselda Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Jaubet, Maria Lourdes. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Sanchez, Maria A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Llanos, Elizabeth Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Vallarino, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Elias, Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina

Multimodel frameworks are common in contemporary elasmobranch growth literature. These techniques offer a proposed improvement over individual growth functions by incorporating additional candidate models with alternative characteristics. Sigmoid functions (e.g. Gompertz and logistic) are a popular alternative to the commonly used von Bertalanffy growth function (VBGF) as they are hypothesized to better suit certain taxa based on body shape (such as batoids) or reproductive mode (such as egg-layers). However, this hypothesis has never been tested. This study examined 74 elasmobranch multimodel growth studies by comparing the growth curves of their respective candidate models. Hypotheses regarding model performances were rejected as the VBGF was equally likely to fit best for all taxa and reproductive modes. Subsequently, no individual model was suited to be used a priori. Differences between candidate model fits were greatest at age zero with Gompertz and logistic functions providing estimates that were 15% and 23% larger on average than the VBGF, respectively. However, length-at-age estimates of the different models became negligible at older ages. Differences between candidate models were mostly small (≤5%), and the multimodel framework only marginally affected length-at-age estimates. However, there were cases where some candidate models provided inappropriate fits that contrasted considerably to the best fitting model. In some of these instances, a single-model framework could have yielded biologically unrealistic growth estimates. Therefore, no study could preempt whether or not it required a multimodel framework. A framework was subsequently recommended to maximize the accuracy of model fits for elasmobranch length-at-age estimates using multimodel approaches.

HFCs (heterozygosity-fitness correlations) measure the direct relationship between an individual's genetic diversity and fitness. The effects of parental heterozygosity and the environment on HFCs are currently under-researched. We investigated these in a high-density U. K. population of European badgers (Meles meles), using a multimodel capture-mark-recapture framework and 35 microsatellite loci. We detected interannual variation in first-year, but not adult, survival probability. Adult females had higher annual survival probabilities than adult males. Cubs with more heterozygous fathers had higher first-year survival, but only in wetter summers; there was no relationship with individual or maternal heterozygosity. Moist soil conditions enhance badger food supply (earthworms), improving survival. In dryer years, higher indiscriminate mortality rates appear to mask differential heterozygosity-related survival effects. This paternal interaction was significant in the most supported model; however, the model-averaged estimate had a relative importance of 0.50 and overlapped zero slightly. First-year survival probabilities were not correlated with the inbreeding coefficient (f); however, small sample sizes limited the power to detect inbreeding depression. Correlations between individual heterozygosity and inbreeding were weak, in line with published meta-analyses showing that HFCs tend to be weak. We found support for general rather than local heterozygosity effects on first-year survival probability, and g2 indicated that our markers had power to detect inbreeding. We emphasize the importance of assessing how environmental stressors can influence the magnitude and direction of HFCs and of considering how parental genetic diversity can affect fitness-related traits, which could play an important role in the evolution of mate choice.

Aim To evaluate the evidence for a small-island effect (SIE) on insular floras, considering the effects of including islands without the species of interest, temporal variability and species composition. Location Small islands in the Andros, Exuma Cays and Abaco archipelagos of the Bahamas. Methods An information-theoretic multimodel inference approach was used, comparing the fit of a linear model versus two breakpoint models to species–area data. Corrected Akaike's information criterion (AICc) scores were compared to determine the model(s) with the most support. Results Strong evidence for an SIE was found in the Andros and Exuma Cays archipelagos. At Andros, an SIE was always apparent, regardless of whether islands with no species were included or which time period was considered. In the Exuma Cays, the inclusion of islands with no species changed the inference of the existence of an SIE in one comparison, and the shape of the SIE (i.e. the best-fitting model) in two comparisons. Data from the Exuma Cays indicated that the existence of an SIE, as well as the shape of the SIE, may vary over time. Evidence for an SIE for the Abaco archipelago was equivocal, although fewer data were available. Main conclusions Even though it has been suggested that the exclusion of islands with no species may result in the erroneous detection of an SIE, the inclusion of islands with no species had little effect on the evidence for an SIE in this system. The finding that evidence for, and the shape of, the SIE can change over time suggests caution in interpreting SIE studies for systems that are not in equilibrium. Patterns of species composition as a function of island size support the hypothesis that disturbance events shape the habitat found on these islands, which limits the species pool that can survive on the smaller islands, resulting in an SIE.

Maximizing survival in reintroduced, captive‐bred animals requires evaluation to identify best practices. This is particularly true for critically endangered species like the Vancouver Island marmot, endemic to British Columbia, Canada. From 2003 to 2010, 301 captive‐bred marmots were implanted with transmitters and released at extinct colony locations and other potentially preferred sites to bolster wild populations and establish new colonies. We evaluated release success based on three criteria: (1) site fidelity in the first summer, (2) survival to hibernation in fall, and (3) survival through winter. We used generalized linear mixed models and information theory to estimate the influence on release success of sex, age, and release practices, as well as local and landscape‐level habitat attributes. Our results suggest that overwinter survival most limited release success in the first‐year postrelease. In all years, overwinter survival was lower for newly released captive‐bred marmots than for wild or previously released marmots. Release date best predicted overall success, and was positively related to site fidelity and survival to hibernation but negatively related to overwinter survival. Our findings suggest that focused attempts to optimize release dates are likely to maximize long‐term reintroduction success.

The authors present theoretical results that show how one can simulate a mixture distribution whose components live in subspaces of different dimension by reformulating the problem in such a way that observations may be drawn from an auxiliary continuous distribution on the largest subspace and then transformed in an appropriate fashion. Motivated by the importance of enlarging the set of available Markov chain Monte Carlo (MCMC) techniques, the authors show how their results can be fruitfully employed in problems such as model selection (or averaging) of nested models, or regeneration of Markov chains for evaluating standard deviations of estimated expectations derived from MCMC simulations. Les auteurs presentent des resultats theoriques qui montrent comment il est possible de simuler un melange de lois dont les composantes vivent dans des sous-espaces de dimensions differentes en reformulant le probleme de sorte que les observations puissent ětre tirees d'une loi continue auxiliaire definie sur le plus grand sous-espace et ensuite transformees de facon appropriee. Motives par l'importance d'elargir l'eventail disponible de methodes de Monte-Carlo a chaǐne de Markov (MCCM), les auteurs montrent comment leurs resultats peuvent ětre mis a profit, entre autres, dans des situations de choix (ou de compromis) entre divers modeles emboǐtes ou de regeneration de chaǐnes de Markov pour l'evaluation de l'ecart type d'estimations d'esperances deduites de simulations par MCCM.