Orr Spiegel

Mechanisms of long-distance seed dispersal

Growing recognition of the importance of long-distance dispersal (LDD) of plant seeds for various ecological and evolutionary processes has led to an upsurge of research into the mechanisms underlying LDD. We summarize these findings by formulating six generalizations stating that LDD is generally more common in open terrestrial landscapes, and is typically driven by large and migratory animals, extreme meteorological phenomena, ocean currents and human transportation, each transporting a variety of seed morphologies. LDD is often associated with unusual behavior of the standard vector inferred from plant dispersal morphology, or mediated by nonstandard vectors. To advance our understanding of LDD, we advocate a vector-based research approach that identifies the significant LDD vectors and quantifies how environmental conditions modify their actions.

Trends and missing parts in the study of movement ecology

Movement is important to all organisms, and accordingly it is addressed in a huge number of papers in the literature. Of nearly 26,000 papers referring to movement, an estimated 34% focused on movement by measuring it or testing hypotheses about it. This enormous amount of information is difficult to review and highlights the need to assess the collective completeness of movement studies and identify gaps. We surveyed 1,000 randomly selected papers from 496 journals and compared the facets of movement studied with a suggested framework for movement ecology, consisting of internal state (motivation, physiology), motion and navigation capacities, and external factors (both the physical environment and living organisms), and links among these components. Most studies simply measured and described the movement of organisms without reference to ecological or internal factors, and the most frequently studied part of the framework was the link between external factors and motion capacity. Few studies looked at the effects on movement of navigation capacity, or internal state, and those were mainly from vertebrates. For invertebrates and plants most studies were at the population level, whereas more vertebrate studies were conducted at the individual level. Consideration of only population-level averages promulgates neglect of between-individual variation in movement, potentially hindering the study of factors controlling movement. Terminology was found to be inconsistent among taxa and subdisciplines. The gaps identified in coverage of movement studies highlight research areas that should be addressed to fully understand the ecology of movement.

Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures.

Summary Integrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector machines, classification and regression trees, random forest methods and artificial neural networks and compare them with linear discriminant analysis (LDA) as a baseline for classifying behavioral modes. Using a dataset of 1035 ground-truthed ACC segments, we found that all methods can accurately classify behavior (80–90%) and, as expected, all nonlinear methods outperformed LDA. We also illustrate how ACC-identified behavioral modes provide the means to examine how vulture flight is affected by environmental factors, hence facilitating the integration of behavioral, biomechanical and ecological data. Our analysis of just over three-quarters of a million GPS and ACC measurements obtained from 43 free-ranging vultures across 9783 vulture-days suggests that their annual breeding schedule might be selected primarily in response to seasonal conditions favoring rising-air columns (thermals) and that rare long-range forays of up to 1750 km from the home range are performed despite potentially heavy energetic costs and a low rate of food intake, presumably to explore new breeding, social and long-term resource location opportunities.

From Individuals to Groups and Back: The Evolutionary Implications of Group Phenotypic Composition

There is increasing interest in understanding the processes that maintain phenotypic variation in groups, populations, or communities. Recent studies have investigated how the phenotypic composition of groups or aggregations (e.g., its average phenotype or phenotypic variance) affects ecological and social processes, and how multi-level selection can drive phenotypic covariance among interacting individuals. However, we argue that these questions are rarely studied together. We present a unified framework to address this gap, and discuss how group phenotypic composition (GPC) can impact on processes ranging from individual fitness to population demography. By emphasising the breadth of topics affected, we hope to motivate more integrated empirical studies of the ecological and evolutionary implications of GPC.

What's your move? Movement as a link between personality and spatial dynamics in animal populations

Recent studies have established the ecological and evolutionary importance of animal personalities. Individual differences in movement and space-use, fundamental to many personality traits (e.g. activity, boldness and exploratory behaviour) have been documented across many species and contexts, for instance personality-dependent dispersal syndromes. Yet, insights from the concurrently developing movement ecology paradigm are rarely considered and recent evidence for other personality-dependent movements and space-use lack a general unifying framework. We propose a conceptual framework for personality-dependent spatial ecology. We link expectations derived from the movement ecology paradigm with behavioural reaction-norms to offer specific predictions on the interactions between environmental factors, such as resource distribution or landscape structure, and intrinsic behavioural variation. We consider how environmental heterogeneity and individual consistency in movements that carry-over across spatial scales can lead to personality-dependent: (1) foraging search performance; (2) habitat preference; (3) home range utilization patterns; (4) social network structure and (5) emergence of assortative population structure with spatial clusters of personalities. We support our conceptual model with spatially explicit simulations of behavioural variation in space-use, demonstrating the emergence of complex population-level patterns from differences in simple individual-level behaviours. Consideration of consistent individual variation in space-use will facilitate mechanistic understanding of processes that drive social, spatial, ecological and evolutionary dynamics in heterogeneous environments.

Incorporating density dependence into the directed-dispersal hypothesis

The directed-dispersal (DrD) hypothesis, one of the main explanations for the adaptive value of seed dispersal, asserts that enhanced (nonrandom) arrival to favorable establishment sites is advantageous for plant fitness. However, as anticipated by the ideal free distribution theory, enhanced seed deposition may impair site suitability by increasing density-dependent mortality, thus negating the advantage postulated by the DrD hypothesis. Although the role of density effects is thoroughly discussed in the seed-dispersal literature, this DrD paradox remains largely overlooked. The paradox, however, may be particularly pronounced in animal-mediated dispersal systems, in which DrD is relatively common, because animals tend to generate local seed aggregations due to their nonrandom movements. To investigate possible solutions to the DrD paradox, we first introduce a simple analytical model that calculates the optimal DrD level at which seed arrival to favorable establishment sites yields maximal fitness gain in comparison to a null model of random arrival. This model predicts intermediate optimal DrD levels that correspond to various attributes of the plants, the dispersers, and the habitat. We then use a simulation model to explore the temporal dynamics of the invasion process of the DrD strategy in a randomly dispersed population, and the resistance of a DrD population against invasion of other dispersal strategies. This model demonstrates that some properties of the invasion process (e.g., mutant persistence ratio in the population and generations until initial establishment) are facilitated by high DrD levels, and not by intermediate levels as expected from the analytical model. These results highlight the need to revise the DrD hypothesis to include the countering effects of density-dependent mortality inherently imposed by enhanced arrival of seeds to specific sites. We illustrate how the revised hypothesis can elucidate previous results from empirical studies reporting little or no support for the DrD hypothesis, and we suggest its incorporation in designing empirical studies of plant recruitment and in management practices.

AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements

BackgroundThe study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data.DescriptionHere we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models.ConclusionsAcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.

Context‐dependent flight speed: evidence for energetically optimal flight speed in the bat Pipistrellus kuhlii?

1. Understanding the causes and consequences of animal flight speed has long been a challenge in biology. Aerodynamic theory is used to predict the most economical flight speeds, minimizing energy expenditure either per distance (maximal range speed, Vmr) or per time (minimal power speed, Vmp). When foraging in flight, flight speed also affects prey encounter and energy intake rates. According to optimal flight speed theory, such effects may shift the energetically optimal foraging speed to above Vmp. 2. Therefore, we predicted that if energetic considerations indeed have a substantial effect on flight speed of aerial-hawking bats, they will use high speed (close to Vmr) to commute from their daily roost to the foraging sites, while a slower speed (but still above Vmp) will be preferred during foraging. To test these predictions, echolocation calls of commuting and foraging Pipistrellus kuhlii were recorded and their flight tracks were reconstructed using an acoustic flight path tracking system. 3. Confirming our qualitative prediction, commuting flight was found to be significantly faster than foraging flight (9.3 vs. 6.7 m s(-1)), even when controlling for its lower tortuosity. 4. In order to examine our quantitative prediction, we compared observed flight speeds with Vmp and Vmr values generated for the study population using two alternative aerodynamic models, based on mass and wing morphology variables measured from bats we captured while commuting. The Vmp and Vmr values generated by one of the models were much lower than our measured flight speed. According to the other model used, however, measured foraging flight was faster than Vmp and commuting flight slightly slower than Vmr, which is in agreement with the predictions of optimal flight speed theory. 5. Thus, the second aerodynamic model we used seems to be a reasonable predictor of the different flight speeds used by the bats while foraging and while commuting. This supports the hypothesis that bats fly at a context-dependent, energetically optimal flight speed.

Factors Influencing Foraging Search Efficiency: Why Do Scarce Lappet-Faced Vultures Outperform Ubiquitous White-Backed Vultures?

The search phase is a critical component of foraging behavior, affecting interspecific competition and community dynamics. Nevertheless, factors determining interspecific variation in search efficiency are still poorly understood. We studied differences in search efficiency between the lappet-faced vulture (Torgos tracheliotus; LFV) and the white-backed vulture (Gyps africanus; WBV) foraging on spatiotemporally unpredictable carcasses in Etosha National Park, Namibia. We used experimental food supply and high-resolution GPS tracking of free-ranging vultures to quantify search efficiency and elucidate the factors underlying the observed interspecific differences using a biased correlated random walk simulation model bootstrapped with the GPS tracking data. We found that LFV’s search efficiency was higher than WBV’s in both first-to-find, first-to-land, and per-individual-finding rate measures. Modifying species-specific traits in the simulation model allows us to assess the relative role of each factor in LFV’s higher efficiency. Interspecific differences in morphology (through the effect on perceptual range and motion ability) and searchers’ spatial dispersion (due to different roost arrangements) are in correspondence with the empirically observed advantage of LFV over WBV searchers, whereas differences in other aspects of the movement patterns appear to play a minor role. Our results provide mechanistic explanations for interspecific variation in search efficiency for species using similar resources and foraging modes.

Socially interacting or indifferent neighbours? Randomization of movement paths to tease apart social preference and spatial constraints

Summary Understanding how animals interact with their physical and social environment is a major question in ecology, but separating between these factors is often challenging. Observed interaction rates may reflect social behaviour – preferences or avoidance of conspecifics or certain phenotypes. Yet, environmental spatiotemporal heterogeneity also affects individual space use and interaction rates. For instance, clumped and ephemeral resources may force individuals to aggregate independently of sociality. Proximity-based social networks (PBSNs) are becoming increasingly popular for studying social structures thanks to the parallel improvement of biotracking technologies and network randomization methods. While current methods focus on swapping individual identities among network nodes or in the data streams that underlies the network (e.g. individuals movement paths), we still need better tools to distinguish between the contribution of sociality and other factors towards those interactions. We propose a novel method that randomizes path segments among different time stamps within each individual separately (Part I). Temporal randomization of whole path segments (e.g. full days) retains their original spatial structure while decoupling synchronization among individuals. This allows researchers to compare observed dyadic association rates with those expected by chance given explicit space use of the individuals in each dyad. Further, since environmental changes are commonly much slower than the duration of social interactions, we can differentiate between these two factors (Part II). First, an individuals path is divided into successive time windows (e.g. weeks), and days are randomized within each time window. Then, by exploring how the deviations between randomized and observed networks change as a function of time window length, we can refine our null model to account also for temporal changes in the activity areas. We used biased-correlated random walk models to simulate populations of socially indifferent or sociable agents for testing our method for both false-positive and negative errors. Applying the method to a data set of GPS-tracked sleepy lizards (Tiliqua rugosa) demonstrated its ability to reveal the social organization in free-ranging animals while accounting for confounding factors of environmental spatiotemporal heterogeneity. We demonstrate that this method is robust to sampling bias and argue that it is applicable for a wide range of systems and tracking techniques, and can be extended to test for preferential phenotypic assortment within PBSNs.