In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions.

Summary Environmental DNA is a survey tool with rapidly expanding applications for assessing the presence of a species at surveyed sites. Environmental DNA methodology is known to be prone to false negative and false positive errors at the data collection and laboratory analysis stages. Existing models for environmental DNA data require augmentation with additional sources of information to overcome identifiability issues of the likelihood function and do not account for environmental covariates that predict the probability of species presence or the probabilities of error. We present a novel Bayesian model for analysing environmental DNA data by proposing informative prior distributions for logistic regression coefficients that enable us to overcome parameter identifiability, while performing efficient Bayesian variable selection. Our methodology does not require the use of transdimensional algorithms and provides a general framework for performing Bayesian variable selection under informative prior distributions in logistic regression models.

Summary1. Data from 'citizen science' surveys are increasingly valuable in identifying declines in widespread species, but require special attention in the case of invertebrates, with considerable variation in number, seasonal flight patterns and, potentially, voltinism. There is a need for reliable and more informative methods of inference in such cases. 2. We focus on data consisting of sample counts of individuals that are not uniquely identifiable, collected at one or more sites. Arrival or emergence and departure or death of individuals take place during the study. We introduce a new modelling approach, which borrows ideas from the 'stopover' capture-recapture literature, that permits the estimation of parameters of interest, such as mean arrival times and relative abundance, or in some cases, absolute abundance, and the comparison of these between sites. 3. The model is evaluated using an extensive simulation study which demonstrates that the estimates for the parameters of interest obtained by the model are reliable, even when the data sets are sparse, as is often the case in reality. 4. When applied to data for the common blue butterfly Polyommatus icarus at a large number of sites, the results suggest that mean emergence times, as well as the relative sizes of the broods, are linked to site northing, and confirm field experience that the species is bivoltine in the south of the UK but practically univoltine in the north. 5. Synthesis and applications. Our proposed 'stopover' model is parameterized with biologically informative constituents: times of emergence, survival rate and relative brood sizes. Estimates of absolute or relative abundance that can be obtained alongside these underlying variables are robust to the presence of missing observations and can be compared in a statistically rigorous framework. These estimates are direct indices of abundance, rather than 'sightings', implicitly adjusted for the possible presence of repeat sightings during a season. At the same time, they provide indices of change in demographic and phenological parameters that may be of use in identifying the factors underlying population change. The model is widely applicable and this will increase the utility of already valuable and influential long-standing surveys in monitoring the effects of environmental change on phenology or abundance.

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. (2015) Reproductive consequences of the timing of seasonal movements in a non-migratory wild bird population. Ecology, 96 (6 Abstract. Animal movement patterns, whether related to dispersal, migration, or ranging behaviors, vary in time. Individual movements reflect the outcomes of interactions between an individual's condition and a multitude of underlying ecological processes. Theory predicts that when competition for breeding territories is high, individuals should arrive at breeding sites earlier than what would otherwise be optimal for breeding in the absence of competition. This is because priority at a site can confer significant competitive advantages leading to better breeding outcomes. Empirical data from long-distance migrants support this theory. However, it has not been tested within the context of fine-scale movements in nonmigratory populations. We assessed the effect of arrival time at a breeding site on reproductive outcomes in an intensively monitored resident population of Great Tits (Parus major). The population was monitored passively, via passive integrated transponder (PIT) tag loggers, and actively, via catching, during breeding and nonbreeding seasons. We developed new capture-recaptureresight models that use both data types to model breeding outcome conditional on the unknown individual arrival times. In accordance with theory, individuals arrived at the woods synchronously in waves that were large at the beginning of the nonbreeding season and small toward the end, with very few arrivals in the intervening period. There was a strong effect of arrival time on the probability of breeding; the earlier an individual arrived, the more likely it was to successfully establish a nest that reached the incubation period. However, once nests were established, they had equal probabilities of failing early, regardless of arrival time. Finally, there was moderate evidence of a negative effect of arrival time on the probability of successfully fledging nestlings. These empirical findings are consistent with theoretical models that suggest an important role for competition in shaping fine-scale seasonal movements. Our capture-recapture-resight models are extensible and suitable for a variety of applications, particularly when the goal is to estimate the effects of unobservable arrival times on subsequent ecological outcomes.