Otso Ovaskainen

State–space models of individual animal movement

Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state-space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.

Roots and Associated Fungi Drive Long-Term Carbon Sequestration in Boreal Forest

Forest Fungi Boreal forest is one of the worlds major biomes, dominating the subarctic northern latitudes of Europe, Asia, and America. The soils of boreal forest function as a net sink in the global carbon cycle and, hitherto, it has been thought that organic matter in this sink primarily accumulates in the form of plant remains. Clemmensen et al. (p. 1615; see the Perspective by Treseder and Holden) now show that most of the stored carbon in boreal forested islands in Sweden is in fact derived from mycorrhizal mycelium rather than from plant litter. Biochemical and sequencing studies show that carbon sequestration is regulated by functional and phylogenetic shifts in the mycorrhizal fungal community. The results will need to be explicitly considered in models of the role of the boreal forest in the global carbon cycle. Reservoirs of carbon in boreal forest soils are revisited in an island chronosequence, using modeling and molecular approaches. [Also see Perspective by Treseder and Holden] Boreal forest soils function as a terrestrial net sink in the global carbon cycle. The prevailing dogma has focused on aboveground plant litter as a principal source of soil organic matter. Using 14C bomb-carbon modeling, we show that 50 to 70% of stored carbon in a chronosequence of boreal forested islands derives from roots and root-associated microorganisms. Fungal biomarkers indicate impaired degradation and preservation of fungal residues in late successional forests. Furthermore, 454 pyrosequencing of molecular barcodes, in conjunction with stable isotope analyses, highlights root-associated fungi as important regulators of ecosystem carbon dynamics. Our results suggest an alternative mechanism for the accumulation of organic matter in boreal forests during succession in the long-term absence of disturbance.

Metapopulation theory for fragmented landscapes.

We review recent developments in spatially realistic metapopulation theory, which leads to quantitative models of the dynamics of species inhabiting highly fragmented landscapes. Our emphasis is in stochastic patch occupancy models, which describe the presence or absence of the focal species in habitat patches. We discuss a number of ecologically important quantities that can be derived from the full stochastic models and their deterministic approximations, with a particular aim of characterizing the respective roles of the structure of the landscape and the properties of the species. These quantities include the threshold condition for persistence, the contributions that individual habitat patches make to metapopulation dynamics and persistence, the time to metapopulation extinction, and the effective size of a metapopulation living in a heterogeneous patch network.

Habitat-specific movement parameters estimated using mark-recapture data and a diffusion model.

I describe a diffusion model aimed at the quantitative analysis of movement in heterogeneous landscapes. The model is based on classifying a landscape into a number of habitat types, which are assumed to differ from each other in terms of the movement behavior of the focal species. In addition to habitat-specific diffusion and mortality coef- ficients, the model accounts for edge-mediated behavior, meaning biased behavior close to boundaries between the habitat types. I illustrate the model with three examples. In the first example, I examine how the strength of edge-mediated behavior and the size of a habitat patch affect the probability that an individual will immigrate to a patch, the prob- ability that an individual will emigrate from a patch, and the time that an individual is expected to spend in a patch. In the second example, I study how a dispersal corridor affects the probability that an individual will move through a landscape. In the third example, I estimate the movement parameters for a species of butterfly from mark-recapture data. In the butterfly example, I classify the landscape into habitat patches, other open areas, and forests. Edge-mediated behavior is found to have a highly significant effect on the general dispersal pattern of the butterfly: the model predicts that the density of butterflies inside habitat patches is >100 times the density of butterflies in adjacent areas.

Modeling species co‐occurrence by multivariate logistic regression generates new hypotheses on fungal interactions

Signals of species interactions can be inferred from survey data by asking if some species occur more or less often together than what would be expected by random, or more generally, if any structural aspect of the community deviates from that expected from a set of independent species. However, a positive (or negative) association between two species does not necessarily signify a direct or indirect interaction, as it can result simply from the species having similar (or dissimilar) habitat requirements. We show how these two factors can be separated by multivariate logistic regression, with the regression part accounting for species-specific habitat requirements, and a correlation matrix for the positive or negative residual associations. We parameterize the model using Bayesian inference with data on 22 species of wood-decaying fungi acquired in 14 dissimilar forest sites. Our analyses reveal that some of the species commonly found to occur together in the same logs are likely to do so merely by similar habitat requirements, whereas other species combinations are systematically either over- or underrepresented also or only after accounting for the habitat requirements. We use our results to derive hypotheses on species interactions that can be tested in future experimental work.

Flight metabolic rate and Pgi genotype influence butterfly dispersal rate in the field.

Dispersal is a key life-history trait, especially in species inhabiting fragmented landscapes. The process of dispersal is affected by a suite of morphological, physiological, and behavioral traits, all of which have a more or less complex genetic basis and are affected by the prevailing environmental conditions. To be able to identify genetic and phenotypic effects on dispersal, movements have to be recorded over relevant spatial and temporal scales. We used harmonic radar to track free-flying Glanville fritillary butterflies (Melitaea cinxia) released in the field and reconstructed their flight tracks for several hours. Flight track lengths for individual butterflies ranged from tens of meters to several kilometers. Butterflies were most mobile at midday and in intermediate temperatures. Flight metabolic rate (MR), measured prior to the tracking, explained variation in mobility at all scales studied. One-third of the variation in the distance moved in one hour could be attributed to variation in flight MR. Heterozygous individuals at a single nucleotide polymorphism in the phosphoglucose isomerase (Pgi) gene moved longer distances in the morning and at lower ambient temperatures than homozygous individuals. A similar genotype x temperature interaction was found to affect the metabolic rate. Our results establish connections from molecular variation in a single gene to flight physiology and movement behavior at the landscape level. These results indicate a fitness advantage to the heterozygous genotype in low temperatures and suggest a mechanism by which varying environmental conditions maintain genetic polymorphism in populations.

So Many Variables: Joint Modeling in Community Ecology.

Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by example and discuss recent computation tools and future directions.

4 – Metapopulation Dynamics in Highly Fragmented Landscapes

Publisher Summary This chapter focuses on models that are the hardcore of ecological metapopulation theory: stochastic patch occupancy models (SPOMs) and their deterministic approximations, the Levins model. There are several reasons for devoting an entire chapter to these models. In a broader biological perspective, the SPOMs belong to “island models,” which have played an important role not only in population and community ecology, but also in population genetics and evolutionary studies. These models often allow a rigorous mathematical analysis, an obvious advantage for theory. SPOMs are also good models for real metapopulations living in highly fragmented landscapes, to the extent that they can be parameterized with empirical data and turned into tools that hold substantial promise for conservation, landscape planning, and management.

Community-level phenological response to climate change

Climate change may disrupt interspecies phenological synchrony, with adverse consequences to ecosystem functioning. We present here a 40-y-long time series on 10,425 dates that were systematically collected in a single Russian locality for 97 plant, 78 bird, 10 herptile, 19 insect, and 9 fungal phenological events, as well as for 77 climatic events related to temperature, precipitation, snow, ice, and frost. We show that species are shifting their phenologies at dissimilar rates, partly because they respond to different climatic factors, which in turn are shifting at dissimilar rates. Plants have advanced their spring phenology even faster than average temperature has increased, whereas migratory birds have shown more divergent responses and shifted, on average, less than plants. Phenological events of birds and insects were mainly triggered by climate cues (variation in temperature and snow and ice cover) occurring over the course of short periods, whereas many plants, herptiles, and fungi were affected by long-term climatic averages. Year-to-year variation in plants, herptiles, and insects showed a high degree of synchrony, whereas the phenological timing of fungi did not correlate with any other taxonomic group. In many cases, species that are synchronous in their year-to-year dynamics have also shifted in congruence, suggesting that climate change may have disrupted phenological synchrony less than has been previously assumed. Our results illustrate how a multidimensional change in the physical environment has translated into a community-level change in phenology.

Making more out of sparse data: hierarchical modeling of species communities

Community ecologists and conservation biologists often work with data that are too sparse for achieving reliable inference with species-specific approaches. Here we explore the idea of combining species-specific models into a single hierarchical model. The community component of the model seeks for shared patterns in how the species respond to environmental covariates. We illustrate the modeling framework in the context of logistic regression and presence-absence data, but a similar hierarchical structure could also be used in many other types of applications. We first use simulated data to illustrate that the community component can improve parameterization of species-specific models especially for rare species, for which the data would be too sparse to be informative alone. We then apply the community model to real data on 500 diatom species to show that it has much greater predictive power than a collection of independent species-specific models. We use the modeling approach to show that roughly one-third of distance decay in community similarity can be explained by two variables characterizing water quality, rare species typically preferring nutrient-poor waters with high pH, and common species showing a more general pattern of resource use.