Understanding and applying biological resilience, from genes to ecosystems.

The natural world is under unprecedented and accelerating pressure. Much work on understanding resilience to local and global environmental change has, so far, focussed on ecosystems. However, understanding a system's behaviour requires knowledge of its component parts and their interactions.

Inferring ecological selection from multidimensional community trait distributions along environmental gradients.

Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with the most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal the underlying abiotic and biotic drivers of community dynamics.

The community of root fungi is associated with the growth rate of Norway spruce (Picea abies).

Our study delved into the relationship between root-associated fungi, gene expression and plant morphology in Norway spruce cuttings derived from both slow-and fast-growing trees. We found no clear link between the gene expression patterns of adventitious roots and the growth phenotype, suggesting no fundamental differences in the receptiveness to fungal symbionts between the phenotypes. Interestingly, saplings from slow-growing parental trees exhibited a higher richness of ectomycorrhizal species and larger roots.

Mixed effects of a national protected area network on terrestrial and freshwater biodiversity.

Protected areas are considered fundamental to counter biodiversity loss. However, evidence for their effectiveness in averting local extinctions remains scarce and taxonomically biased. We employ a robust counterfactual multi-taxon approach to compare occupancy patterns of 638 species, including birds (150), mammals (23), plants (39) and phytoplankton (426) between protected and unprotected sites across four decades in Finland.

Effects of ignoring survey design information for data reuse.

Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: "Are aggregated databases currently providing the right information to enable effective and unbiased reuse?" We investigate this question, with a focus on designs that purposefully favor the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified.

Impacts of Oil Spills on Arctic Marine Ecosystems: A Quantitative and Probabilistic Risk Assessment Perspective.

Oil spills resulting from maritime accidents pose a poorly understood risk to the Arctic environment. We propose a novel probabilistic method to quantitatively assess these risks. Our method accounts for spatiotemporally varying population distributions, the spreading of oil, and seasonally varying species-specific exposure potential and sensitivity to oil.

A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data.

Motivation: Recent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets.

Integrating experimental and distribution data to predict future species patterns.

Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species.

Estimating the acute impacts of Arctic marine oil spills using expert elicitation.

Increasing maritime traffic in the Arctic has heightened the oil spill-related risks in this highly sensitive environment. To quantitatively assess these risks, we need knowledge about both the vulnerability and sensitivity of the key Arctic functional groups that may be affected by spilled oil. However, in the Arctic these data are typically scarce or lacking altogether.

Preparing for the unprecedented - Towards quantitative oil risk assessment in the Arctic marine areas.

The probability of major oil accidents in Arctic seas is increasing alongside with increasing maritime traffic. Hence, there is a growing need to understand the risks posed by oil spills to these unique and sensitive areas. So far these risks have mainly been acknowledged in terms of qualitative descriptions.

By-catch of grey seals (Halichoerus grypus) in Baltic fisheries--a Bayesian analysis of interview survey.

Baltic seals are recovering after a population decline. The increasing seal stocks cause notable damage to fisheries in the Baltic Sea, with an unknown number of seals drowning in fishing gear every year. Thus, sustainable seal management requires updated knowledge of the by-catch of seals--the number of specimens that die in fishing gear.

Toward integrative management advice of water quality, oil spills, and fishery in the Gulf of Finland: a Bayesian approach.

Understanding and managing ecosystems affected by several anthropogenic stressors require methods that enable analyzing the joint effects of different factors in one framework. Further, as scientific knowledge about natural systems is loaded with uncertainty, it is essential that analyses are based on a probabilistic approach. We describe in this article about building a Bayesian decision model, which includes three stressors present in the Gulf of Finland.

Probabilistic ecosystem model for predicting the nutrient concentrations in the Gulf of Finland under diverse management actions.

Many countries define legislative targets for the ecological status of aquatic ecosystems. Fulfilling these legally binding targets requires often large scale and expensive management actions. The expected benefits from alternative actions are commonly compared with deterministic ecosystem models.

Approximate inference for disease mapping with sparse Gaussian processes.

Gaussian process (GP) models are widely used in disease mapping as they provide a natural framework for modeling spatial correlations. Their challenges, however, lie in computational burden and memory requirements. In disease mapping models, the other difficulty is inference, which is analytically intractable due to the non-Gaussian observation model.