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The Skellam model describes discrete‐time population dynamics of a single species assuming uniform (i.e., random or Poissonian) individual distribution and intraspecific contest competition. Apart from studies on individual‐based models derived from first principles it has been rarely applied in ecological research although in specific situations it may be more appropriate than, for instance, the frequently used Ricker model, which is derived assuming scramble competition among the individuals. In this article, we offer an insight into the first principles underlying the Skellam model and provide an alternative parameterization of the model in terms of two commonly used parameters: intrinsic rate of population increase and carrying capacity. We also provide guidelines and software for fitting the Skellam model to discrete population time series data. In light of these findings, the Skellam model may be a useful alternative for a range of purposes where it has been earlier overlooked. [ABSTRACT FROM AUTHOR]

Benthic species and communities are linked to pelagic zooplankton through life‐stages encompassing both benthic and pelagic habitats and through a mutual dependency on primary producers as a food source. Many zooplankton taxa contribute to the sedimentary system as benthic eggs. Our main aim was to investigate the nature of the population level biotic interactions between and within these two seemingly independent communities, both dependent on the pelagic primary production, while simultaneously accounting for environmental drivers (salinity, temperature, and oxygen conditions). To this end, we applied multivariate autoregressive state‐space models to long (1966–2007) time series of annual abundance data, comparing models with and without interspecific interactions, and models with and without environmental variables included. We were not able to detect any direct coupling between sediment‐dwelling benthic taxa and pelagic copepods and cladocerans on the annual scale, but the most parsimonious model indicated that interactions within the benthic community are important. There were also positive residual correlations between the copepods and cladocerans potentially reflecting the availability of a shared resource or similar seasonal dependence, whereas both groups tended to correlate negatively with the zoobenthic taxa. The most notable single interaction within the benthic community was a tendency for a negative effect of Limecola balthica on the amphipods Monoporeia affinis and Pontoporeia femorata which can help explain the observed decrease in amphipods due to increased competitive interference. [ABSTRACT FROM AUTHOR]

Abiotic variables subject to global change are known to affect plankton biomasses, and these effects can be species-specific. Here, we investigate the environmental drivers of annual biomass using plankton data from the Gulf of Finland in the northern Baltic Sea, spanning years 1993–2016. We estimated annual biomass time-series of 31 nanoplankton and microplankton species and genera from day-level data, accounting for the average phenology and wind. We found wind effects on day-level biomass in 16 taxa. We subsequently used state-space models to connect the annual biomass changes with potential environmental drivers (temperature, salinity, stratification, ice cover and inorganic nutrients), simultaneously accounting for temporal trends. We found clear environmental effects influencing the annual biomasses of Dinobryon faculiferum , Eutreptiella spp. Protoperidinium bipes , Pseudopedinella spp. Snowella spp. and Thalassiosira baltica and indicative effects in 10 additional taxa. These effects mostly concerned temperature, salinity or stratification. Together, these 16 taxa represent two-thirds of the summer biomass in the sampled community. The inter-annual variability observed in salinity and temperature is relatively low compared to scenarios of predicted change in these variables. Therefore, the potential impacts of the presented effects on plankton biomasses are considerable. [ABSTRACT FROM AUTHOR]

Studying aquatic population dynamics using spatio-temporal monitoring data is associated with a number of challenges and choices. One can let several samples represent the same population over larger areas, or alternatively model the dynamics of each sampling location in continuous space. We analysed the spatio-temporal population dynamics of six phytoplankton taxa in the Baltic Sea applying multivariate state-space models with first-order density dependence. We compared three spatial scales and three models for spatial correlation between predefined subpopulations using information theoretic model selection. We hypothesised that populations close to each other display similar dynamic properties and spatial synchrony decreasing with the distance. We further hypothesize that intermediate-scale grouping of data into subpopulations may parsimoniously represent such dynamics. All taxa showed constant density dependence across space and strong spatial synchrony, consistently requiring a parameter for spatial correlation whenever models included several population states. The most parsimonious spatial structure varied between taxa, most often being one panmictic population or ten intercorrelated population states. Evidently, longer time-series, containing more information, provide more options for modelling detailed spatio-temporal patterns. With a few decade-long plankton time-series data, we encourage determining the appropriate spatial scale on biological grounds rather than model fit. [ABSTRACT FROM AUTHOR]

Aim: The temporal structure (colour) of environmental variation influences population fluctuations, extinction risk and community stability. However, it is unclear whether environmental covariates linked to population fluctuations are distinguishable from a purely random process (white noise). We aimed to estimate colour coefficients and relative support for three models commonly representing coloured stochastic processes, in environmental series linked to terrestrial animal population fluctuations. Location: North America and Eurasia. Time period: 1901–2002. Major taxa studied: Birds, insects and mammals. Methods: We analysed multiple abiotic environmental covariates, comparing point estimates and confidence intervals of temporal structure in competing models fitted using white noise, autoregressive [AR(1)] and 1/f processes in the time domain and the frequency domain (where time series were analysed after decomposition into different sinusoidal frequencies and their relative powers). All animal time series were sampled annually for ≤ 50 years, potentially inflating type II errors. We also considered 101‐year series of matched environmental covariates, performing a statistical power analysis evaluating our ability to draw robust conclusions. Results: Temperature‐related variables were associated with the largest fraction of population fluctuations. Ninety‐three per cent of shorter environmental series were indistinguishable from white noise, limited by time‐series length and associated with wide confidence intervals. The longer environmental series analysed in the time domain offered sufficiently high statistical power to identify correctly colour estimates ≥ |0.27|, indicating that 20% of series were best described by a slightly reddened noise process. Main conclusions: Focusing on the short time‐scales typically available for ecologists, most environmental variables associated with terrestrial animal population fluctuations are best characterized by white noise processes, although type II errors are common. The correct detection of intermediately coloured noise with power 0.8 requires ≥ 16 data points in the time domain or ≥ 47 points in the frequency domain. Over longer time‐scales, where type II errors are less likely, one‐fifth of populations are associated with coloured (often reddened) variables. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [Copyright &y& Elsevier]