Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities

1. Understanding the consequences of spatial structure on ecological dynamics is a central theme in ecology. Recently, research has recognised the relevance of river and river-analogue network structures, because these systems are not only highly diverse but also rapidly changing due to habitat modifications or species invasions. 2. Much of the previous work on ecological and evolutionary dynamics in metapop- ulations and metacommunities in dendritic river networks has been either using comparative approaches or was purely theoretical. However, the use of micro- cosm experiments provides the unique opportunity to study large-scale questions in a causal and experimental framework. 3. We conducted replicated microcosm experiments, in which we manipulated the spatially explicit network configuration of a landscape and addressed how linear versus dendritic connectivity affects population dynamics, specifically the spatial distribution of population densities, and movement behaviour of the protist model organism Tetrahymena pyriformis. We tracked population densities and individual-level movement behaviour of thousands of individuals over time. 4. At the end of the experiment, we found more variable population densities between patches in dendritic networks compared to linear networks, as pre- dicted by theory. Specifically, in dendritic networks, population densities were higher at nodes that connected to headwaters compared to the headwaters themselves and to more central nodes in the network. These differences follow theoretical predictions and emerged from the different network topologies per se. These differences in population densities emerged despite weakly density- dependent movement. 5. We show that differences in network structure alone can cause characteristic spatial variation in population densities. While such differences have been postu- lated by theoretical work and are the underlying precondition for differential dis- persal evolution in heterogeneous networks, our results may be the first experimental demonstration thereof. Furthermore, these population-level dynam- ics may affect extinction risks and can upscale to previously shown metacommu- nity level diversity dynamics. Given that many species in natural river systems exhibit strong spatiotemporal patterns in population densities, our work suggests that abundance patterns should not only be addressed from a local environmental perspective, but may be the outcome of processes that are inher- ently driven by the respective habitat network structure

Evolution of dispersal distance: Maternal investment leads to bimodal dispersal kernels

Since dispersal research has mainly focused on the evolutionary dynamics of dispersal rates, it remains unclear what shape evolutionarily stable dispersal kernels have. Yet, detailed knowledge about dispersal kernels, quantifying the statistical distribution of dispersal distances, is of pivotal importance for understanding biogeographic diversity, predicting species invasions, and explaining range shifts. We therefore examine the evolution of dispersal kernels in an individual-based model of a population of sessile organisms, such as trees or corals. Specifically, we analyze the influence of three potentially important factors on the shape of dispersal kernels: distance-dependent competition, distance-dependent dispersal costs, and maternal investment reducing an offspring's dispersal costs through a trade-off with maternal fecundity. We find that without maternal investment, competition and dispersal costs lead to unimodal kernels, with increasing dispersal costs reducing the kernel's width and tail weight. Unexpectedly, maternal investment inverts this effect: kernels become bimodal at high dispersal costs. This increases a kernel's width and tail weight, and thus the fraction of long-distance dispersers, at the expense of simultaneously increasing the fraction of non-dispersers. We demonstrate the qualitative robustness of our results against variations in the tested parameter combinations

Landscape configuration is a major determinant of home range size variation

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVINInternational audienceMost animals restrain their movement activities to familiar areas which leads to home ranges. Although understanding both establishment and shifts of home ranges is highly relevant for basic science and conservation, pinpointing the factors that shape the dynamics of home ranges remains a challenge. Evidently home ranges are influenced by the underlying landscape. Landscape composition, i.e., the fraction of different land cover types, has recently been shown to affect home range size. Yet, the explicit spatial configuration of the landscape, a factor which is known to be of central importance in spatial ecology, is not taken into account by most studies. We quantify the effect of landscape configuration on summer home range sizes across multiple spatio-temporal scales using GPS data from two behaviorally distinct ungulate species, red (Cervus elaphus) and roe deer (Capreolus capreolus), in the Bavarian Forest National Park, Germany. We show that the spatial configuration of the landscape is the dominant factor explaining home range size. Furthermore, we find that the shape of the relationship between home range size and landscape configuration depends on a species' habitat requirements: while roe deer decrease their home range size with increasing landscape patchiness, the relationship is hump-shaped for red deer. Our results are robust at all tested spatio-temporal scales

Dynamic species classification of microorganisms across time, abiotic and biotic environments-A sliding window approach.

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology

Eco-evolutionary dynamics in fragmented landscapes

Peer reviewedPostprin

Metaecosystem dynamics drive community composition in experimental, multi‐layered spatial networks

Cross‐ecosystem subsidies are studied with a focus on resource exchange at local ecosystem boundaries. This perspective ignores regional dynamics that can emerge via constraints imposed by the landscape, potentially leading to spatially‐dependent effects of subsidies and spatial feedbacks. Using miniaturized landscape analogues of river dendritic and terrestrial lattice spatial networks, we manipulated and studied resource exchange between the two whole networks. We found community composition in dendritic networks depended on the resource pulse from the lattice network, with the strength of this effect declining in larger downstream patches. In turn, this spatially‐dependent effect imposed constraints on the lattice network with populations in that network reaching higher densities when connected to more central patches in the dendritic network. Consequently, localized cross‐ecosystem fluxes, and their respective effects on recipient ecosystems, must be studied in a perspective taking into account the explicit spatial configuration of the landscape

Selection on growth rate and local adaptation drive genomic adaptation during experimental range expansions in the protist Tetrahymena thermophila

1. Populations that expand their range can undergo rapid evolutionary adaptation of life-history traits, dispersal behaviour and adaptation to the local environment. Such adaptation may be aided or hindered by sexual reproduction, depending on the context. 2. However, few empirical and experimental studies have investigated the genetic basis of adaptive evolution during range expansions. Even less attention has been given to the question how sexual reproduction may modulate such adaptive evolution during range expansions. 3. We here studied genomic adaptation during experimental range expansions of the protist Tetrahymena thermophila in landscapes with a uniform environment or a pH gradient. Specifically, we investigated two aspects of genomic adaptation during range expansion. First, we investigated adaptive genetic change in terms of the underlying numbers of allele frequency changes from standing genetic variation and de novo variants. We focused on how sexual reproduction may alter this adaptive genetic change. Second, we identified genes subject to selection caused by the expanding range itself, and directional selection due to the presence or absence of the pH gradient. We focused this analysis on alleles with large frequency changes that occurred in parallel in more than one population to identify the most likely candidate targets of selection. 4. We found that sexual reproduction altered adaptive genetic change both in terms of de novo variants and standing genetic variation. However, sexual reproduction affected allele frequency changes in standing genetic variation only in the absence of long-distance gene flow. Adaptation to the range expansion affected genes involved in cell divisions and DNA repair, whereas adaptation to the pH gradient additionally affected genes involved in ion balance and oxidoreductase reactions. These genetic changes may result from selection on growth and adaptation to low pH. 5. In the absence of gene flow, sexual reproduction may have aided genetic adaptation. Gene flow may have swamped expanding populations with maladapted alleles, thus reducing the extent of evolutionary adaptation during range expansion. Sexual reproduction also altered the genetic basis of adaptation in our evolving populations via de novo variants, possibly by purging deleterious mutations or by revealing fitness benefits of rare genetic variants

Genetics of Dispersal

Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal-related phenotypes or evidence for the micro-evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment-dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non-additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non-equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context-dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits.Peer reviewe

Dispersal: a matter of scale

Population density around the natal site is often invoked as an explanation for variation in dispersal distance, with the expectation that competition for limiting resources, coupled with increased intra‐specific aggression at high densities, should drive changes in dispersal distances. However, tests of the density‐dependent dispersal hypothesis in long‐lived vertebrates have yielded mixed results. Furthermore, conclusions from dispersal studies may depend on the spatial and temporal scales at which density and dispersal patterns are examined, yet multi‐scale studies of dispersal are rare. Here, we present the findings of a long‐term study examining factors influencing natal dispersal distances for the non‐migratory population of Peregrine Falcons (Falco peregrinus) in the British Isles across distinct spatial and temporal scales. Our smallest scale study included Peregrines ringed as nestlings and subsequently recaptured alive in south Scotland–north England, an area that was intensively studied during the time periods 1974–1982 and 2002–2016. Second, we examined dispersal patterns of birds ringed as nestlings in south Scotland–north England, but subsequently recaptured alive or recovered dead anywhere in the British Isles. Finally, we examined the natal dispersal patterns for Peregrines ringed and recaptured or recovered anywhere in the British Isles from 1964 to 2016. Consistent with prior findings, females dispersed farther than males across all scales. However, the patterns of dispersal were strongly scale dependent. Specifically, we found a lack of a discernible relationship between index of density and dispersal distance in the limited study area, but when region‐wide recaptures and recoveries were included in the analyses, a negative relationship was revealed. Our results suggest that conclusions of dispersal studies may be scale dependent, highlighting the importance of spatial and temporal scales in examining and interpreting the relationship between population density and dispersal patterns