Andreas Lindén

Rapid Advance of Spring Arrival Dates in Long-Distance Migratory Birds

Several bird species have advanced the timing of their spring migration in response to recent climate change. European short-distance migrants, wintering in temperate areas, have been assumed to be more affected by change in the European climate than long-distance migrants wintering in the tropics. However, we show that long-distance migrants have advanced their spring arrival in Scandinavia more than short-distance migrants. By analyzing a long-term data set from southern Italy, we show that long-distance migrants also pass through the Mediterranean region earlier. We argue that this may reflect a climate-driven evolutionary change in the timing of spring migration.

Using the negative binomial distribution to model overdispersion in ecological count data

A Poisson process is a commonly used starting point for modeling stochastic variation of ecological count data around a theoretical expectation. However, data typically show more variation than implied by the Poisson distribution. Such overdispersion is often accounted for by using models with different assumptions about how the variance changes with the expectation. The choice of these assumptions can naturally have apparent consequences for statistical inference. We propose a parameterization of the negative binomial distribution, where two overdispersion parameters are introduced to allow for various quadratic mean-variance relationships, including the ones assumed in the most commonly used approaches. Using bird migration as an example, we present hypothetical scenarios on how overdispersion can arise due to sampling, flocking behavior or aggregation, environmental variability, or combinations of these factors. For all considered scenarios, mean-variance relationships can be appropriately described by the negative binomial distribution with two overdispersion parameters. To illustrate, we apply the model to empirical migration data with a high level of overdispersion, gaining clearly different model fits with different assumptions about mean-variance relationships. The proposed framework can be a useful approximation for modeling marginal distributions of independent count data in likelihood-based analyses.

Ecological and evolutionary dynamics under coloured environmental variation

Environmental variation is a ubiquitous component of individual, population and community processes in the natural world. Here, we review the consequences of spatio-temporally autocorrelated (coloured) environmental variation for ecological and evolutionary population dynamics. In single-species population models, environmental reddening increases (decreases) the amplitude of fluctuations in undercompensatory (overcompensatory) populations. This general result is also found in structurally more complex models (e.g. with space or species interactions). Environmental autocorrelation will also influence evolutionary dynamics as the changing environment is filtered through ecological dynamics. In the context of long-term environmental change, it becomes crucial to understand the potential impacts of different regimes of environmental variation at different scales of organization, from genes to species to communities.

Indicators of Forest Biodiversity: Which Bird Species Predict High Breeding Bird Assemblage Diversity in Boreal Forests at Multiple Spatial Scales?

Indicator species have been proposed to be used for revealing common status of ecosystems and their biodiversity. We studied breeding forest birds in southern Finland. Our aim was to find bird species combinations that would predict species richness of forest bird assemblages at several spatial scales. We evaluated statistical models that included 1–5 indicator candidate species, and ranked them according to the Bayesian information criterion. The red-breasted flycatcher Ficedula parva, the pygmy owl Glaucidium passerinum and the three-toed woodpecker Picoides tridactylus were found to be the best multiscale indicators. Models at smaller spatial scales, including several indicator species better explained the total variation in species richness. The indicators mostly revealed properties of the forest site rather than variation in species richness caused by species interactions. Our results show that a suitable set of indicator species may be a useful and quick method for the evaluation of bird diversity in forest environments.

Mischaracterising density dependence biases estimated effects of coloured covariates on population dynamics

Environmental effects on population growth are often quantified by coupling environmental covariates with population time series, using statistical models that make particular assumptions about the shape of density dependence. We hypothesized that faulty assumptions about the shape of density dependence can bias estimated effect sizes of temporally autocorrelated covariates. We investigated the presence of bias using Monte Carlo simulations based on three common per capita growth functions with distinct density dependent forms (θ-Ricker, Ricker and Gompertz), autocorrelated (coloured) ‘known’ environmental covariates and uncorrelated (white) ‘unknown’ noise. Faulty assumptions about the shape of density dependence, combined with overcompensatory intrinsic population dynamics, can lead to strongly biased estimated effects of coloured covariates, associated with lower confidence interval coverage. Effects of negatively autocorrelated (blue) environmental covariates are overestimated, while those of positively autocorrelated (red) covariates can be underestimated, generally to a lesser extent. Prewhitening the focal environmental covariate effectively reduces the bias, at the expense of the estimate precision. Fitting models with flexible shapes of density dependence can also reduce bias, but increases model complexity and potentially introduces other problems of parameter identifiability. Model selection is a good option if an appropriate model is included in the set of candidate models. Under the specific and identifiable circumstances with high risk of bias, we recommend prewhitening or careful modelling of the shape of density dependence.

Adaptive and nonadaptive changes in phenological synchrony

Organisms in seasonal environments are known to adjust their phenology in response to climate change (1, 2), that is, they change their schedules of seasonal occurrence and annual life-history events. In particular, the advancement of spring emergence and activities is one of the strongest and best-documented ecological responses to climate change (1, 3, 4). As the rate of advancement varies between species, for example at different trophic levels, the occurrence of interacting species may become asynchronous, altering or disrupting the ecological interactions—a phenomenon referred to as the match–mismatch hypothesis (4, 5). While many studies have documented changes in phenological synchrony, with various effects on the focal species’ populations (4, 6, 7), we still lack a general picture of how widespread changes we see in phenological synchrony and how they affect ecological communities. In PNAS, Kharouba et al. (8) address this topic in a meta-analysis on 54 pairs of interacting species. The phenologies of the studied species advanced with an average rate of ca . 4 d per decade. Importantly, phenological synchrony of the species pairs was changing at a rapid and accelerating pace, with an average of 6.1 d per decade, either toward more or toward less synchrony. This change was approximately 10 times faster compared with what happened before 1981, which was used as a baseline in this study. Climate change is recognized as a major threat to global biodiversity (9). Changes in phenological synchrony have raised serious concerns about adverse population-level consequences. Insectivorous birds and their prey (caterpillars) have for a long time been a model system for studying match–mismatch. In birds there is correlative evidence for species with increasing phenological mismatch showing more negative population trends (7, 10, 11). Despite these results, a wide range of patterns have been … [↵][1]1Email: andreas.linden{at}iki.fi. [1]: #xref-corresp-1-1

Data from: Increased male bias in eider ducks can be explained by sex-specific survival of prime-age breeders

In contrast to theoretical predictions of even adult sex ratios, males are dominating in many bird populations. Such bias among adults may be critical to population growth and viability. Nevertheless, demographic mechanisms for biased adult sex ratios are still poorly understood. Here, we examined potential demographic mechanisms for the recent dramatic shift from a slight female bias among adult eider ducks (Somateria mollissima) to a male bias (about 65% males) in the Baltic Sea, where the species is currently declining. We analysed a nine-year dataset on offspring sex ratio at hatching based on molecularly sexed ducklings of individually known mothers. Moreover, using demographic data from long-term individual-based capture-recapture records, we investigated how sex-specific survival at different ages after fledgling can modify the adult sex ratio. More specifically, we constructed a stochastic two-sex matrix population model and simulated scenarios of different survival probabilities for males and females. We found that sex ratio at hatching was slightly female-biased (52.8%) and therefore unlikely to explain the observed male bias among adult birds. Our stochastic simulations with higher survival for males than for females revealed that despite a slight female bias at hatching, study populations shifted to a male-biased adult sex ratio (> 60% males) in a few decades. This shift was driven by prime reproductive-age individuals (≥5-year-old), with sex-specific survival of younger age classes playing a minor role. Hence, different age classes contributed disproportionally to population dynamics. We argue that an alternative explanation for the observed male dominance among adults–sex-biased dispersal–can be considered redundant and is unlikely, given the ecology of the species. The present study highlights the importance of considering population structure and age-specific vital rates when assessing population dynamics and management targets.