MoveTraits-A Database for Integrating Animal Behaviour Into Trait-Based Ecology.

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.

Movement ecology is a rapidly emerging new research field that explores the underlying mechanisms of organism movement, from animals, humans, to plant and microorganisms, across a range of environmental contexts and spatiotemporal scales, from individual behavior to population-level processes. Research in this area draws on a diverse range of disciplines including animal behavior, ecology, biology, geography, physics, mathematics, statistics, and computer science, and utilizes a range of techniques and technologies, including telemetry, remote sensing, and bioacoustics. Animal movements are determined by a combination of both innate individual characteristics and environmental factors, such as food availability, predation risk, and habitat quality. Movements can range from small-scale foraging excursions within a home-range to seasonal migrations over thousands of kilometers, with characteristic patterns at different spatial scales. Animal movement may affect reproductive success, genetic diversity, and disease transmission within populations, and can play a role in wider ecosystem functioning via nutrient cycling or dispersal of seeds and pollen. The rapid growth of movement ecology since the early 2000s has arguably been driven by the increased functionality, availability, and durability of modern biologgers and sensors enabling detailed data sets to be collected from a wide range of species. In turn, this has necessitated the development of new mathematical, statistical, and computational methods for modeling and analyzing these rich, and increasingly very large, data sets. In its broadest sense, movement ecology encompasses a wide-ranging set of concepts, theories, and practical approaches that relate to animal movement. Rather than attempting to cover the entire field, this bibliography specifically focuses on recent developments in modeling and analysis of the most common type of animal movement data, namely (x,y) or (x,y,z) positional data, as collected directly or indirectly over time by spatial tracking systems such as GPS or radio telemetry. Data collected in other forms such as via mark-recapture studies is beyond our scope (although still an important approach within the field). The bibliography highlights the key Movement Ecology Textbooks in the field, explores key Movement Ecology Concepts, and explains methods for Movement Data Collection and Processing and dealing with Circular Statistics. The article gives an overview of Random Walk Models of Movement and how they relate to Movement Path Analysis and Metrics and Statistical Model Fitting of Movement Data in this context. It focuses on several key applications: Foraging and Search, Home-Range, Habitat Space-Use and Resource Selection, and Collective Movement, and finishes with a summary of recent R Packages for Movement Ecology.

The recent debate on both the existence and the cause of fractal (Lévy) patterns in animal movement resonates with much deeper and richer problems in movement ecology: (1) establishing mechanistic links between animal behavior and statistical patterns of movement, and (2) understanding what is the role of randomness (stochasticity) in animal motion. Here, the idea of behavioral intermittence is shown to be crucial to establish mechanistic connections between the behavior of organisms and the statistical properties they generate when moving. Attention is drawn to the fact that some random walk modeling procedures can impair the identification of intermittent biological mechanisms which could govern major statistical properties of movement. This fact, together with some misconceptions and prejudices regarding the role of randomness in animal motion may explain why stochastic processes have been disregarded as a potential source of adaptation in animal movement. In the near future, the advances in biotelemetry together with a more explicit consideration of behavioral intermittence, and the development of novel random walk approaches, could help us to set up the bases for a landscape-level behavioral ecology.

Intraspecific trait variability has a fundamental contribution to the overall trait variability. However, little is known concerning the relative role of local (e.g. disturbances and species interaction) and regional (biogeographical) processes in generating this intraspecific trait variability. While biogeographical processes enhance plant trait variability between distant populations, in fire-prone ecosystems, recurrent fires may have a preponderant role in generating variability at a local scale. We hypothesize that plants respond to the local spatio-temporal heterogeneity generated by fire by having a relatively large local variability in regeneration traits in such a way that overrides the variability at a broader biogeographical scale. We test this hypothesis by assessing the intraspecific variability in fire-related regeneration traits of two species (Cistus salviifolius and Lavandula stoechas) growing in fire-prone ecosystems of the Mediterranean Basin. For each species, we selected six populations in two distant regions, three in the east (Anatolian Peninsula) and three in the west (Iberian Peninsula). For each species and population, we analysed the following regeneration traits: seed size, seed dormancy and stimulated germination by fire-related cues (heat and smoke). To evaluate the distribution of the variability in these traits, we decomposed the variability of trait values at each level, between regions (regional) and between population within region (local), using linear mixed-effect models. Despite the biogeographical and climatic differences between regions, for the two species, intraspecific variability in regeneration traits was higher at a local (within regions) than at a regional scale (between regions). Our results suggest that, in Mediterranean ecosystems, fire is an important source of intraspecific variability in regeneration traits. This supports the prominent role of fire as an ecological and evolutionary process, producing trait variability and shaping biodiversity in fire-prone ecosystems.

Fungi play key roles in ecosystems and human societies as decomposers, nutrient cyclers, mutualists, and pathogens. Estimates suggest that roughly 3–13 million fungal species exist worldwide, yet considerable knowledge gaps exist regarding the mechanisms and consequences, both ecological and social, of fungal dispersal from local to global scales. In this review, we summarize concepts underlying fungal dispersal, review recent research, and explore how fungi possess unique characteristics that can broaden our understanding of general dispersal ecology. We highlight emerging frontiers in fungal dispersal research that integrate technological advances with trait-based ecology, movement ecology, social–ecological systems, and work in unexplored environments. Outstanding research questions across these themes are presented to stimulate theoretical and empirical research in fungal dispersal ecology. Advances in fungal dispersal will improve our understanding of fungal community assembly and biogeography across a range of spatial scales, with implications for ecosystem functioning, global food security, and human health.

The influence of intraspecific trait variation on species interactions makes trait-based approaches critical to understanding eco-evolutionary processes. Because species occupy habitats that are patchily distributed in space, species interactions are influenced not just by the degree of intraspecific trait variation but also the relative proportion of trait variation that occurs within- versus between-patches. Advancement in trait-based ecology hinges on understanding how trait variation is distributed within and between habitat patches across the landscape. We sampled larval spotted salamanders (Ambystoma maculatum) across six spatially discrete ponds to quantify within- and between-pond variation in mass, length, and various metrics associated with their relationship (scaling, body condition, shape). Across all traits, within-pond variation contributed more to total observed morphological variation than between-pond variation. Between-pond variation was not negligible, however, and explained 20-41% of total observed variation in measured traits. Between-pond variation was more pronounced in salamander tail morphology compared to head or body morphology, suggesting that pond-level factors more strongly influence tails than other body parts. We also observed differences in mass-length relationships across ponds, both in terms of scaling slopes and intercepts, though differences in the intercepts were much stronger. Preliminary evidence hinted that newly constructed ponds were a driver of the observed differences in mass-length relationships and morphometrics. General pond-level difference in salamander trait covariation suggest that allometric scaling of morphological traits is context dependent in patchy landscapes. Effects of pond age offer the hypothesis that habitat restoration through pond construction is a driver of variation in trait scaling, which managers may leverage to bolster trait diversity.

In trait-based ecology, phenotypic variation (PVar) is often quantified with measures expressing average differences between populations standardized in the range 0-1. However, these measures disregard the within-population trait variability. In addition, some of them cannot be partitioned between populations. These aspects can either alter their interpretation or limit their applicability. To overcome these problems, we propose a new measure, the phenotypic dissimilarity (PhD) index, to quantify PVar between populations in scenarios of varying within-population interindividual trait variability. PhD can also quantify within-population PVar while accounting for intraindividual trait variability. Using simulated and real data, we show that using the PhD index becomes important when the within-population trait variability is not negligible, as in all ecological studies. By accounting for within-population trait variability, the PhD index does not overestimate PVar across an environmental gradient compared to other estimators. Traits inherently vary within species. Accounting for such variability is essential to understanding species' phenotypic responses to environmental cues. The proposed PhD index will provide ecologists with a tool for quantifying PVar within species and compare it between species at different levels of biological organization. We provide an R function to calculate the PhD index.

BackgroundOn-going global climate change poses a serious threat for natural populations unless they are able to evolutionarily adapt to changing environmental conditions (e.g. increasing average temperatures, occurrence of extreme weather events). A prerequisite for evolutionary change is within-population heritable genetic variation in traits subject to selection. In relation to climate change, mainly phenological traits as well as heat and desiccation resistance have been examined for such variation. Therefore, it is important to investigate adaptive potential under climate change conditions across a broader range of traits. This is especially true for life-history traits and defences against natural enemies (e.g. parasites) since they influence organisms’ fitness both directly and through species interactions. We examined the adaptive potential of fitness-related traits and their responses to heat waves in a population of a freshwater snail, Lymnaea stagnalis. We estimated family-level variation and covariation in life history (size, reproduction) and constitutive immune defence traits [haemocyte concentration, phenoloxidase (PO)-like activity, antibacterial activity of haemolymph] in snails experimentally exposed to typical (15 °C) and heat wave (25 °C) temperatures. We also assessed variation in the reaction norms of these traits between the treatments.ResultsWe found that at the heat wave temperature, snails were larger and reproduced more, while their immune defence was reduced. Snails showed high family-level variation in all examined traits within both temperature treatments. The only negative genetic correlation (between reproduction and antibacterial activity) appeared at the high temperature. However, we found no family-level variation in the responses of most examined traits to the experimental heat wave (i.e. largely parallel reaction norms between the treatments). Only the reduction of PO-like activity when exposed to the high temperature showed family-level variation, suggesting that the cost of heat waves may be lower for some families and could evolve under selection.ConclusionOur results suggest that there is genetic potential for adaptation within both thermal environments and that trait evolution may not be strongly affected by trade-offs between them. However, rare differences in thermal reaction norms across families indicate limited evolutionary potential in the responses of snails to changing temperatures during extreme weather events.

Heritable variation in traits can have wide-ranging impacts on species interactions, but the effects that ongoing evolution has on the temporal ecological dynamics of communities are not well understood. Here, we identify three conditions that, if experimentally satisfied, support the hypothesis that evolution by natural selection can drive ecological changes in communities. These conditions are: (i) a focal population exhibits genetic variation in a trait(s), (ii) there is measurable directional selection on the trait(s), and (iii) the trait(s) under selection affects variation in a community variable(s). When these conditions are met, we expect evolution by natural selection to cause ecological changes in the community. We tested these conditions in a field experiment examining the interactions between a native plant (Oenothera biennis) and its associated arthropod community (more than 90 spp.). Oenothera biennis exhibited genetic variation in several plant traits and there was directional selection on plant biomass, life-history strategy (annual versus biennial reproduction) and herbivore resistance. Genetically based variation in biomass and life-history strategy consistently affected the abundance of common arthropod species, total arthropod abundance and arthropod species richness. Using two modelling approaches, we show that evolution by natural selection in large O. biennis populations is predicted to cause changes in the abundance of individual arthropod species, increases in the total abundance of arthropods and a decline in the number of arthropod species. In small O. biennis populations, genetic drift is predicted to swamp out the effects of selection, making the evolution of plant populations unpredictable. In short, evolution by natural selection can play an important role in affecting the dynamics of communities, but these effects depend on several ecological factors. The framework presented here is general and can be applied to other systems to examine the community-level effects of ongoing evolution.

Previous work (Reynolds and Gross 1992) has demonstrated that mate choice enables female guppies, Poecilia reticulata , to obtain heritable benefits (‘good genes’) for their offspring. It is not yet known whether males also signal their functional fertility, that is, their viability as fathers, to female guppies. Our study tested the hypothesis that a male9s behaviour reveals characteristics of his ejaculate. We uncovered a strong correlation between display rate and sperm number in male guppies derived from two wild Trinidadian populations. This result indicates that information on the functional fertility of potential mates is available to females. However, our investigation also found a significant relationship between the frequency of sneaky mating and sperm number. Female guppies in wild populations, particularly those experiencing a high predation regime, are constantly subjected to sneaky mating attempts. As the males responsible for these sneaky matings have highest reserves of sperm they may have the capacity to undermine female choice. The implications of these findings for sperm competition are discussed.

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal's home range, and many methods of estimating home ranges have arisen over the past half a century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as kernel density estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian bridge movement models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of appropriately using these two categories of distributions and their estimators. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home ranges estimated using estimators of occurrence distributions are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use estimators of range distributions to quantify home ranges and estimators of occurrence distributions to answer other questions in movement ecology, such as when and where an animal crossed a linear feature, visited a location of interest, or interacted with other animals.

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

Animal movement and behaviour is fundamental for ecosystem functioning. The process of seed dispersal by frugivorous animals is a showcase for this paradigm since their behaviour shapes the spatial patterns of the earliest stage of plant regeneration. However, we still lack a general understanding of how intrinsic (frugivore and plant species traits) and extrinsic (landscape features) factors interact to determine how seeds of a given species are more likely to be deposited in some places more than in others. We develop a multi-species mechanistic model of seed dispersal based on frugivore behavioural responses to landscape heterogeneity. The model was fitted to data from three-years of spatially-explicit field observations on the behaviour of six frugivorous thrushes and the fruiting patterns of three fleshy-fruited trees in a secondary forest of the Cantabrian range (N Spain). With such model we explore how seed rain patterns arise from the interaction between animal behaviour and landscape heterogeneity. We show that different species of thrushes respond differently to landscape heterogeneity even though they belong to the same genus, and that provide complementary seed dispersal functions. Simulated seed rain patterns are only realistic when at least some landscape heterogeneity (forest cover and fruit abundance) is taken into account. The common and simple approach of re-sampling movement data to quantify seed dispersal produces biases in both the distance and the habitat at which seeds arrive. Movement behaviour not only affects dispersal distance and seed rain patterns but also can affect frugivore diet composition even if there is no built-in preference for fruiting species. In summary, the fate of seeds produced by a given plant species is strongly affected by both the composition of the frugivore assemblage and the landscape-scale context of the plant location, including the presence of fruits from other plants (from the same or different species).

Video object tracking has been used with great success in numerous applications ranging from autonomous vehicle navigation to medical image analysis. A broad and emerging domain for exploration is in the field of automatic video-based animal behavior understanding. Interesting, yet difficult-to-test hypotheses, can be evaluated by high-throughput processing of animal movements and interactions collected from both laboratory and field experiments. In this paper we describe BioSense, a new standalone software platform and user interface that provides researchers with an open-source framework for collecting and quantitatively analyzing video data characterizing animal movement and behavior (e.g. spatial location, velocity, region preference, etc.). BioSense is capable of tracking multiple objects in real-time, using various object detection methods suitable for a range of environments and animals. Real-time operation also provides a tactical approach to object tracking by allowing users the ability to manipulate the control of the software while seeing visual feedback immediately. We evaluate the capabilities of BioSense in a series of video tracking benchmarks representative of the challenges present in animal behavior research.

Summary1. Understanding human resource extraction is crucial for conservation science, allowing accurate assessments of system sustainability and testing key assumptions about human resource users.2. We apply ecological methods and principles to Global Positioning System (GPS) data on human movement to investigate the ecological and behavioural differences between human hunters and non‐hunters, a method which can be reproduced with any species which routinely return to a central place. The integration of movement ecology and habitat selection can greatly augment the applicability and scope of both disciplines, and we explore the issues that arise from integration, because of the differing data types and methods used by each approach.3. We propose an adaptable methodological framework which can be used to combine movement ecology and habitat selection when using data from GPS tracking devices, whether from human or animal foragers. The methodology is based on three steps and can be implemented in the free downloadable statistical program R, using the full code in the Data S1.4. First, we show that net squared displacement, coupled with nonlinear mixed‐effects models, is suitable for quantifying characteristics of small‐scale movement, such as daily travel patterns, and extracting parts of these journeys for analysis.5. Secondly, having extracted part of a journey, biased random bridges use the auto‐correlated GIS tracking data to create a utilization distribution (UD) for each individual. This method includes movement between known locations to estimate use intensity in an area.6. Finally, the UD can be analysed by a resource utilization function which relates the intensity of use to landscape features of an area to identify habitats selected by an individual. This can be used to predict use of the landscape at larger scales for both individuals and an entire population.7. This methodological framework is a flexible method to accurately assess human and animal resource use and movement through the natural environment.