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Negative phototactic response to UVR in three cosmopolitan rotifers: a video analysis approach
(2019)
Mathematical analysis of partial differential equations (PDEs) has led to many insights regarding the effect of organism movements on spatial population dynamics. However, their use has mainly been confined to the community of mathematical biologists, with less attention from statistical and empirical ecologists. We conjecture that this is principally due to the inherent difficulties in fitting PDEs to data. To help remedy this situation, in the context of movement ecology, we show how the popular technique of step selection analysis (SSA) can be used to parametrize a class of PDEs, calleddiffusion-taxismodels, from an animal's trajectory. We examine the accuracy of our technique on simulated data, then demonstrate the utility of diffusion-taxis models in two ways. First, for non-interacting animals, we derive the steady-state utilization distribution in a closed analytic form. Second, we give a recipe for deriving spatial pattern formation properties that emerge from interacting animals: specifically, do those interactions cause heterogeneous spatial distributions to emerge and if so, do these distributions oscillate at short times or emerge without oscillations? The second question is applied to data on concurrently tracked bank volesMyodes glareolus. Our results show that SSA can accurately parametrize diffusion-taxis equations from location data, providing the frequency of the data is not too low. We show that the steady-state distribution of our diffusion-taxis model, where it is derived, has an identical functional form to the utilization distribution given by resource selection analysis (RSA), thus formally linking (fine scale) SSA with (broad scale) RSA. For the bank vole data, we show how our SSA-PDE approach can give predictions regarding the spatial aggregation and segregation of different individuals, which are difficult to predict purely by examining results of SSA. Our methods provide a user-friendly way into the world of PDEs, via a well-used statistical technique, which should lead to tighter links between the findings of mathematical ecology and observations from empirical ecology. By providing a non-speculative link between observed movement behaviours and space use patterns on larger spatio-temporal scales, our findings will also aid integration of movement ecology into understanding spatial species distributions.
Identifying behavioral mechanisms that underlie observed movement patterns is difficult when animals employ sophisticated cognitive‐based strategies. Such strategies may arise when timing of return visits is important, for instance to allow for resource renewal or territorial patrolling. We fitted spatially explicit random‐walk models to GPS movement data of six wolves (Canis lupus; Linnaeus, 1758) from Alberta, Canada to investigate the importance of the following: (1) territorial surveillance likely related to renewal of scent marks along territorial edges, to reduce intraspecific risk among packs, and (2) delay in return to recently hunted areas, which may be related to anti‐predator responses of prey under varying prey densities. The movement models incorporated the spatiotemporal variable “time since last visit,” which acts as a wolf's memory index of its travel history and is integrated into the movement decision along with its position in relation to territory boundaries and information on local prey densities. We used a model selection framework to test hypotheses about the combined importance of these variables in wolf movement strategies. Time‐dependent movement for territory surveillance was supported by all wolf movement tracks. Wolves generally avoided territory edges, but this avoidance was reduced as time since last visit increased. Time‐dependent prey management was weak except in one wolf. This wolf selected locations with longer time since last visit and lower prey density, which led to a longer delay in revisiting high prey density sites. Our study shows that we can use spatially explicit random walks to identify behavioral strategies that merge environmental information and explicit spatiotemporal information on past movements (i.e., “when” and “where”) to make movement decisions. The approach allows us to better understand cognition‐based movement in relation to dynamic environments and resources.
When sampling animal movement paths, the frequency at which location measurements are attempted is a critical feature for data analysis. Important quantities derived from raw data, e.g. travel distance or sinuosity, can differ largely based on the temporal resolution of the data. Likewise, when movement models are fitted to data, parameter estimates have been demonstrated to vary with sampling rate. Thus, biological statements derived from such analyses can only be made with respect to the resolution of the underlying data, limiting extrapolation of results and comparison between studies. To address this problem, we investigate whether there are models that are robust against changes in temporal resolution. First, we propose a mathematically rigorous framework, in which we formally define robustness as a model property. We then use the framework for a thorough assessment of a range of basic random walk models, in which we also show how robustness relates to other probabilistic concepts. While we found robustness to be a strong condition met by few models only, we suggest a new method to extend models so as to make them robust. Our framework provides a new systematic, mathematically founded approach to the question if, and how, sampling rate of movement paths affects statistical inference.
Patterns of space use are often subject to large temporal and individual-level variation, due to seasonality in behaviour and environmental conditions as well as age- or sex-specific needs. Especially in temperate regions, seasonality likely influences space use even in non-migratory birds. In waterfowl of the family Anatidae, however, few studies have analyzed space use of the same individuals across the full annual cycle. We used a resident population of Mandarin Ducks (Aix galericulata) in northeast Germany to study their year-round space use in relation to season, sex, and age. We marked 172 birds with colour rings and surveyed relevant water bodies for re-encounters for several years. As space-use patterns we derived home ranges from minimum convex polygons and the number of water bodies used by individual birds. Our analysis revealed that individuals shifted their space use between seasons, in particular extending their home ranges during the non-breeding season. Between years, in contrast, birds tended to show season-specific site fidelity. Sex differences were apparent during both breeding and non-breeding season, males consistently having larger home ranges and using slightly more water bodies. No difference was found between first-year and adult birds. Our study demonstrates that mark-resighting can provide valuable information about space use in species with suitable behaviour and readily accessible habitat. In such cases, it may be a valid alternative to more expensive GPS-tracking or short-term manual radio telemetry, particularly within citizen-science projects.
Predation is a strong species interaction causing severe harm or death to prey. Thus, prey species have evolved various defence strategies to minimize predation risk, which may be immediate (e.g., a change in behaviour) or transgenerational (morphological defence structures). We studied the behaviour of two strains of a rotiferan prey (Brachionus calyciflorus) that differ in their ability to develop morphological defences in response to their predator Asplanchna brightwellii. Using video analysis, we tested: (a) if two strains differ in their response to predator presence and predator cues when both are undefended; (b) whether defended individuals respond to live predators or their cues; and (c) if the morphological defence (large spines) per se has an effect on the swimming behaviour. We found a clear increase in swimming speed for both undefended strains in predator presence. However, the defended specimens responded neither to the predator presence nor to their cues, showing that they behave indifferently to their predator when they are defended. We did not detect an effect of the spines on the swimming behaviour. Our study demonstrates a complex plastic behaviour of the prey, not only in the presence of their predator, but also with respect to their defence status.
Predation is a strong species interaction causing severe harm or death to prey. Thus, prey species have evolved various defence strategies to minimize predation risk, which may be immediate (e.g., a change in behaviour) or transgenerational (morphological defence structures). We studied the behaviour of two strains of a rotiferan prey (Brachionus calyciflorus) that differ in their ability to develop morphological defences in response to their predator Asplanchna brightwellii. Using video analysis, we tested: (a) if two strains differ in their response to predator presence and predator cues when both are undefended; (b) whether defended individuals respond to live predators or their cues; and (c) if the morphological defence (large spines) per se has an effect on the swimming behaviour. We found a clear increase in swimming speed for both undefended strains in predator presence. However, the defended specimens responded neither to the predator presence nor to their cues, showing that they behave indifferently to their predator when they are defended. We did not detect an effect of the spines on the swimming behaviour. Our study demonstrates a complex plastic behaviour of the prey, not only in the presence of their predator, but also with respect to their defence status.
Many animals that have to cope with predation have evolved mechanisms to reduce their predation risk. One of these mechanisms is change in morphology, for example, the development of spines. These spines are induced, when mothers receive chemical signals of a predator (kairomones) and their daughters are then equipped with defensive spines. We studied the behaviour of a prey and its predator when the prey is either defended or undefended. We used common aquatic micro-invertebrates, the rotifers Brachionus calyciflorus (prey) and Asplanchna brightwellii (predator) as experimental animals. We found that undefended prey increased its swimming speed in the presence of the predator. The striking result was that the defended prey did not respond to the predator's presence. This suggests that defended prey has a different response behaviour to a predator than undefended conspecifics. Our study provides further insights into complex zooplankton predator-prey interactions. Predation is a strong species interaction causing severe harm or death to prey. Thus, prey species have evolved various defence strategies to minimize predation risk, which may be immediate (e.g., a change in behaviour) or transgenerational (morphological defence structures). We studied the behaviour of two strains of a rotiferan prey (Brachionus calyciflorus) that differ in their ability to develop morphological defences in response to their predator Asplanchna brightwellii. Using video analysis, we tested: (a) if two strains differ in their response to predator presence and predator cues when both are undefended; (b) whether defended individuals respond to live predators or their cues; and (c) if the morphological defence (large spines) per se has an effect on the swimming behaviour. We found a clear increase in swimming speed for both undefended strains in predator presence. However, the defended specimens responded neither to the predator presence nor to their cues, showing that they behave indifferently to their predator when they are defended. We did not detect an effect of the spines on the swimming behaviour. Our study demonstrates a complex plastic behaviour of the prey, not only in the presence of their predator, but also with respect to their defence status.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.
Intraspecific trait variation (ITV) is thought to play a significant role in community assembly, but the magnitude and direction of its influence are not well understood. Although it may be critical to better explain population persistence, species interactions, and therefore biodiversity patterns, manipulating ITV in experiments is challenging. We therefore incorporated ITV into a trait‐ and individual‐based model of grassland community assembly by adding variation to the plants’ functional traits, which then drive life‐history tradeoffs. Varying the amount of ITV in the simulation, we examine its influence on pairwise‐coexistence and then on the species diversity in communities of different initial sizes. We find that ITV increases the ability of the weakest species to invade most, but that this effect does not scale to the community level, where the primary effect of ITV is to increase the persistence and abundance of the competitively‐average species. Diversity of the initial community is also of critical importance in determining ITV's efficacy; above a threshold of interspecific diversity, ITV does not increase diversity further. For communities below this threshold, ITV mainly helps to increase diversity in those communities that would otherwise be low‐diversity. These findings suggest that ITV actively maintains diversity by helping the species on the margins of persistence, but mostly in habitats of relatively low alpha and beta diversity.
Animal movements arise from complex interactions of individuals with their environment, including both conspecific and heterospecific individuals. Animals may be attracted to each other for mating, social foraging, or information gain, or may keep at a distance from others to avoid aggressive encounters related to, e.g., interference competition, territoriality, or predation. With modern tracking technology, more datasets are emerging that allow to investigate fine‐scale interactions between free‐ranging individuals from movement data, however, few methods exist to disentangle fine‐scale behavioural responses of interacting individuals when these are highly individual‐specific.
In a framework of step‐selection functions, we related movements decisions of individuals to dynamic occurrence distributions of other individuals obtained through kriging of their movement paths. Using simulated data, we tested the method's ability to identify various combinations of attraction, avoidance, and neutrality between individuals, including asymmetric (i.e. non‐mutual) behaviours. Additionally, we analysed radio‐telemetry data from concurrently tracked small rodents (bank vole, Myodes glareolus) to test whether our method could detect biologically plausible behaviours.
We found that our method was able to successfully detect and distinguish between fine‐scale interactions (attraction, avoidance, neutrality), even when these were asymmetric between individuals. The method worked best when confounding factors were taken into account in the step‐selection function. However, even when failing to do so (e.g. due to missing information), interactions could be reasonably identified. In bank voles, responses depended strongly on the sexes of the involved individuals and matched expectations.
Our approach can be combined with conventional uses of step‐selection functions to tease apart the various drivers of movement, e.g. the influence of the physical and the social environment. In addition, the method is particularly useful in studying interactions when responses are highly individual‐specific, i.e. vary between and towards different individuals, making our method suitable for both single‐species and multi‐species analyses (e.g. in the context of predation or competition).
Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.
The pace-of-life syndrome (POLS) hypothesis posits that suites of traits are correlated along a slow-fast continuum owing to life history trade-offs. Despite widespread adoption, environmental conditions driving the emergence of POLS remain unclear. A recently proposed conceptual framework of POLS suggests that a slow-fast continuum should align to fluctuations in density-dependent selection. We tested three key predictions made by this framework with an ecoevolutionary agent-based population model. Selection acted on responsiveness (behavioral trait) to interpatch resource differences and the reproductive investment threshold (life history trait). Across environments with density fluctuations of different magnitudes, we observed the emergence of a common axis of trait covariation between and within populations (i.e., the evolution of a POLS). Slow-type (fast-type) populations with high (low) responsiveness and low (high) reproductive investment threshold were selected at high (low) population densities and less (more) intense and frequent density fluctuations. In support of the predictions, fast-type populations contained a higher degree of variation in traits and were associated with higher intrinsic reproductive rate (r(0)) and higher sensitivity to intraspecific competition (gamma), pointing to a universal trade-off. While our findings support that POLS aligns with density-dependent selection, we discuss possible mechanisms that may lead to alternative evolutionary pathways.