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Abstract Bayesian approaches to the modelling of ecological systems are increasingly popular, but there are competing methods for formal model comparisons. Here, we focus on the task of performing multimodel inference through estimating posterior model weights, which encompasses uncertainties in the choice of competing model structure into the inference outputs. Model‐based approaches such as reversible‐jump Markov chain Monte Carlo (RJ‐MCMC) are flexible and allow multimodel inference, but can be complex to implement and optimise, and so we translate a model‐based approach for ecological applications using Importance Sampling to estimate the marginal likelihood of the data given a particular model. This approach allows for model comparison through the estimation of Bayes' Factors or interpretable posterior model probabilities, yielding model weights that facilitate multimodel inference through Bayesian model averaging. We demonstrate Importance Sampling with two case study investigations in animal demography: censused analysis of banded mongoose (Mungos mungo) survival where missing data are uncommon, and capture–mark–recapture analysis of European badger (Meles meles) survival where data are commonly missing. We compare outcomes of the model comparison using the Importance Sampling approach to those obtained through single‐model inference approaches using Deviance information criteria and the Watanabe–Akaike information criteria. The results of the Importance Sampling method aligns with RJ‐MCMC model comparisons while often being more straightforward to fit and optimise, particularly if the competing models are non‐nested.

Abstract The natural and seminatural components of agricultural landscapes play a key role in maintaining a high level of biodiversity. Being the Po Valley one of the most human‐dominated and intensively cultivated landscapes in Europe, we investigated the effect of no‐crop habitats on carabid richness and composition and evaluated the role of tree row as corridor for forest carabid dispersion. Carabids were sampled with 70 pitfall traps arranged in 35 sampling plots along three parallel transects (80, 100, and 140 m long) and encompassing five different habitats: tree row, tree row edge, grassland, forest edge, and forest. We found 5,615 individuals belonging to 55 species. Despite the similarity in species richness, all the habitats investigated showed a peculiar and distinct species assemblage. The main distinction was between the "open habitat" cluster composed of grassland and tree row edge and the “forest" cluster composed of forest, tree row, and forest edge. We found that forest species are able to penetrate the grassland matrix up to 30 m from the forest edge and that a distance of no more than 60 m between tree row and forest can allow the passage of up to 50% of the forest species. Beyond this distance, the grassland matrix becomes a barrier, preventing them from reaching other suitable habitats. Our findings confirm the importance of maintaining different types of natural habitats to significantly increase biodiversity in an intensively cultivated agroecosystem and demonstrated the role of linear elements as a corridor and “stepping stones” for many forest species.

Abstract Background Invasive plants commonly occupy diverse habitats and thus must adapt to changing environmental pressures through altering their traits and economics spectra, and addressing these patterns and their drivers has an importantly ecological and/or evolutionary significance. However, few studies have considered the role of multiple biotic and abiotic factors in shaping trait variation and spectra. In this study, we determined seven leaf traits of 66 Solidago canadensis populations, and quantified the relative contributions of climate, soil properties, native plant diversity, and S. canadensis–community interactions (in total 16 factors) to leaf trait variation and spectrum with multimodel inference. Results Overall, the seven leaf traits had high phenotypic variation, and this variation was highest for leaf dry matter content and lowest for leaf carbon concentration. The per capita contribution of climate to the mean leaf trait variation was highest (7.5%), followed by soil properties (6.2%), S. canadensis–community interactions (6.1%), and native plant diversity (5.4%); the dominant factors underlying trait variation varied with leaf traits. Leaf production potential was negatively associated with leaf stress-tolerance potential, and the relative contributions to this trade-off followed in order: native plant diversity (7.7%), climate (6.9%), S. canadensis–community interactions (6.2%), and soil properties (5.6%). Climate, diversity, soil, and interactions had positive, neutral or negative effects. Conclusions Climate, soil, diversity, and interactions contribute differentially to the leaf trait variation and economics spectrum of S. canadensis, and their relative importance and directions depend on plant functional traits.

To describe the growth pattern of the bonnethead shark (Sphyrna tiburo) in the Gulf of Mexico, a von Bertalanffy (VB) model has been automatically fit, which indicated a single−phase continuous growth without oscillations, though this would generate biases if this hypothesis is not confirmed. The objective of this research was to describe the growth pattern of S. tiburo under a multimodel approach based on information theory and contrasting single−phase models (VB, Gompertz, logistic models, and variants) and biphasic models (Soriano model and variants). The VB model was not supported. The Soriano model, with the variant in growth rate (k) and including length at birth (L0), was selected with 100% supporting evidence. The hypothesis of the two−phase growth of S. tiburo with an increase in k, more than L∞, fitted to L0, is confirmed, and a correspondence was identified between growth−phase change sizes and the sizes reported in the literature for change in the juvenile–adult stages in females and for onset of reproductive maturity in males and both sexes.

Abstract In the “digital conservation” age, big data from Earth observations and from social media have been increasingly used to tackle conservation challenges. Here, we combined information from those two digital sources in a multimodel inference framework to identify, map, and predict the potential for nature's cultural contributions to people in two contrasting UNESCO biosphere reserves: Doñana and Sierra Nevada (Spain). The content analysis of Flickr pictures revealed different cultural contributions, according to the natural and cultural values of the two reserves. Those contributions relied upon landscape variables computed from Earth observation data: the variety of colors and vegetation functioning that characterize Doñana landscapes, and the leisure facilities, accessibility features, and heterogeneous landscapes that shape Sierra Nevada. Our findings suggest that social media and Earth observations can aid in the cost‐efficient monitoring of nature's contributions to people, which underlie many Sustainable Development Goals and conservation targets in protected areas worldwide.

The age and growth rate of the giant electric ray, Narcine entemedor, was estimated using growth bands deposited in the vertebral centra of 245 specimens. Differences in size and age distribution were found between the sexes, a pattern that suggests the annual deposition of band pairs, possibly occurring in April. Multimodel inference and back-calculation were performed to three age data sets of females considering their reproductive cycle and time of capture, among which the von Bertalanffy growth function was found to be the most appropriate (L∞ = 81.87 cm TL, k = 0.17 year−1). Our research supports the idea that age can be determined via biological features such as birth date and growth band periodicity. We concluded that N. entemedor is of a moderate body size, moderate longevity and is a fast-growing elasmobranch species.

Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index ϕ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.

Abstract 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.

Background Attention has recently been drawn to the issue of transboundary invasions, where species introduced and naturalized in one country cross international borders and become problematic in neighbouring countries. Robust modelling frameworks, able to identify the environmental drivers of invasion and forecast the current and future potential distribution of invasive species, are needed to study and manage invasions. Limitations due to the lack of species distribution and environmental data, or assumptions of modelling tools, often constrain the reliability of model predictions. Methods We present a multiscale spatial modelling framework for transboundary invasions, incorporating robust modelling frameworks (Multimodel Inference and Ensemble Modelling) to overcome some of the limitations. The framework is illustrated using Hakea sericea Schrad. (Proteaceae), a shrub or small tree native to Australia and invasive in several regions of the world, including the Iberian Peninsula. Two study scales were considered: regional scale (western Iberia, including mainland Portugal and Galicia) and local scale (northwest Portugal). At the regional scale, the relative importance of environmental predictors sets was evaluated and ranked to determine the main general drivers for the species distribution, while the importance of each environmental predictor was assessed at the local scale. The potential distribution of H. sericea was spatially projected for both scale areas. Results Model projections for western Iberia suggest that a large area is environmentally suitable in both Portugal and Spain. Climate and landscape composition sets were the most important determinants of this regional distribution of the species. Conversely, a geological predictor (schist lithology) was more important in explaining its local-scale distribution. Conclusions After being introduced to Portugal, H. sericea has become a transboundary invader by expanding in parts of Galicia (Spain). The fact that a larger area is predicted as environmentally suitable in Spain raises concerns regarding its potential continued expansion. This highlights the importance of transboundary cooperation in the early management of invasions. By reliably identifying drivers and providing spatial projections of invasion at multiple scales, this framework provides insights for the study and management of biological invasions, including the assessment of transboundary invasion risk.

Abstract 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.

Forest fragmentation in agroecosystems is linked to declines of avian species worldwide. Agriculture has greatly reduced native forest cover in east-central Argentina. Assessing the influence of fragmentation on forest bird populations is vital to inform reliable conservation and management strategies for the Espinal region of Argentina. We determined the relationships of vegetation structure within native forest patches, size and shape of these patches (patch scale), composition and spatial configuration (at landscape scale) to relative abundance of 17 forest bird species during austral fall-winter and spring-summer seasons. Birds were sampled from 2007-2009 in 45 forest patches within three landscape mosaics (30 x 30 km) representing a gradient of native forest fragmentation in east-central Argentina. We used an information-theoretic approach and approximated model inference to examine the effect of predictor environmental variables at two spatial scales on patterns of forest bird abundance. Density of trees within forest patches was the main predictor of bird abundance at the patch scale. Amount of forest and spatial configuration were also important. The abundance of several bird species was greater in patches with high density of trees and landscapes characterized by more forest cover and larger patches in close proximity. We found two main avian response patterns to forest fragmentation and patch characteristics. This information contributes reliable knowledge for the design of conservation measures of agricultural landscapes in the Espinal region of Argentina.

The tambaqui, Colossoma macropomum, is one of the most commercially valuable Amazonian fish species, and in the floodplains of the region, they are caught in both rivers and lakes. Most growth studies on this species to date have adjusted only one growth model, the von Bertalanffy, without considering its possible uncertainties. In this study, four different models (von Bertalanffy, Logistic, Gompertz and the general model of Schnüte-Richards) were adjusted to a data set of fish caught within lakes from the middle Solimões River. These models were adjusted by non-linear equations, using the sample size of each age class as its weight. The adjustment evaluation of each model was based on the Akaike Information Criterion (AIC), the variation of AIC between the models (Δi) and the evidence weights (wi). Both the Logistic (Δi = 0.0) and Gompertz (Δi = 1.12) models were supported by the data, but neither of them was clearly superior (wi, respectively 52.44 and 29.95%). Thus, we propose the use of an averaged-model to estimate the asymptotic length (L∞). The averaged-model, based on Logistic and Gompertz models, resulted in an estimate of L∞=90.36, indicating that the tambaqui would take approximately 25 years to reach average size.