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1. Weather radar networks provide wide-ranging opportunities for ecologists to quantify and predict movements of airborne organisms over unprecedented geographical expanses. The typically sparse spatial distribution of radar measurements poses, however, a major challenge to spatiotemporal predictive modelling.2. We propose FluxRGNN, a recurrent graph neural network that is based on a generic mechanistic description of population-level movements across the Voronoi tessellation of radar sites. The resulting hybrid model capitalises on local associations between environmental conditions and animal density as well as on spatiotemporal dependencies inherent to the movement process. We applied FluxRGNN to make 72-h forecasts of nocturnal bird migration over Western Europe using simulated trajectories and measurements from the European weather radar network.3. For both datasets, FluxRGNN achieves higher predictive performance than baseline models based on environmental conditions alone. It effectively disentangles local take-off and landing dynamics from aerial movements and correctly predicts migration directions with an accuracy of 87%.4. Continental-scale forecasts of animal density and biomass fluxes have the potential to improve the impact and cost-effectiveness of wildlife management and conservation efforts. With FluxRGNN this becomes feasible for nocturnal bird migration. In the future, other migration systems could benefit from applying the proposed method to similar static sensor networks.

1. Accurate predictions of the abundance of migrating birds are important to avoid aerial conflicts of birds, for example, with aviation or wind power installations.2. Here we develop a predictive model, using bird migration intensity extracted from operational weather data. We compare baseline phenological models to models incorporating both local and remote weather conditions using an ensemble approach. Single models are compared to ensemble models (average prediction of top 10 models). The models were evaluated by omitting single years from our 10-year dataset.3. In general, we find that wind conditions, in addition to seasonal and diurnal dynamics, are key for accurate predictions. The spring and fall migratory seasons differ, both with respect to the selected environmental variables and the contribution of the environmental model compared to the phenological model. In fall, the accumulation of migrants due to strong headwinds is an important predictor of migration.4. Because of the lower daily variation in migration intensity in spring, the phenological model performs better compared to fall. In fall, weather conditions contribute more to accurate predictions of migration intensity than in spring.5. Overall, the ensemble approach produces more accurate predictions outperforming specific environmental models. We therefore recommend that ensemble models be used in operational settings such as flight planning to reduce bird aircraft collisions during intense bird migration.

Biodiversity is changing at an unprecedented rate, and long-term monitoring is key to quantifying these changes and identifying their drivers (1, 2). Weather radars are an essential tool for meeting these goals. However, recent policy changes make vital data unavailable. Data policy should be adjusted to take into account the broad role that weather radars play beyond meteorology. In addition to providing essential meteorological data for weather forecasts, flood risk planning, storm warnings, and atmospheric and climatological research (3, 4), weather radars detect trillions of insects, bats, and birds in the air (5, 6). By collecting such data, they could provide an unrecognized service to society: long-term standardized monitoring of aerial biomass flows (7). In the United States, weather radar data have already been used at a continental scale for these purposes (6, 8). However, similar efforts in Europe (9, 10) are now fundamentally threatened. The Operational Programme for the Exchange of Weather Radar Information (OPERA) coordinates the exchange of radar data among European national meteorological services (11). It serves as a central hub for accessing weather radar data in Europe, allowing those in search of data to make one request instead of contacting each meteorological service separately. However, because of budget cuts and resulting prioritization of meteorological products, OPERA now requests that national meteorological services submit cleaned rather than uncleaned polar volume radar data (12). Uncleaned radar data include both meteorological and biological signals, whereas cleaned data exclude biological signals.

Weather radars provide detailed information on aerial movements of organisms. However, interpreting fine-scale radar imagery remains challenging because of changes in aerial sampling altitude with distance from the radar. Fine-scale radar imagery has primarily been used to assess mass exodus at sunset to study stopover habitat associations. Here, we present a method that enables a more intuitive integration of information across elevation scans projected in a two-dimensional spatial image of fine-scale radar reflectivity. We applied this method on nights of intense bird migration to demonstrate how the spatial distribution of migrants can be explored at finer spatial scales and across multiple radars during the higher flying en-route phase of migration. The resulting reflectivity maps enable explorative analysis of factors influencing their regional and fine-scale distribution. We illustrate the method&rsquo, s application by generating time-series of composites of up to 20 radars, achieving a nearly complete spatial coverage of a large part of Northwest Europe. These visualizations are highly useful in interpreting regional-scale migration patterns and provide detailed information on bird movements in the landscape and aerial environment.

The majority of carnivore species are described as solitary, but little is known about their social organization and interactions with conspecifics. We investigated the spatial organization and social interactions as well as relatedness of slender mongooses ( Galerella sanguinea ) living in the southern Kalahari. This is a little studied small carnivore previously described as solitary with anecdotal evidence for male associations. In our study population, mongooses arranged in spatial groups consisting of one to three males and up to four females. Male ranges, based on sleeping sites, were large and overlapping, encompassing the smaller and more exclusive female ranges. Spatial groups could be distinguished by their behaviour, communal denning and home range. Within spatial groups animals communally denned in up to 33% of nights, mainly during winter months, presumably to gain thermoregulatory benefits. Associations of related males gained reproductive benefits likely through increased territorial and female defence. Our study supports slender mongooses to be better described as solitary foragers living in a complex system of spatial groups with amicable social interactions between specific individuals. We suggest that the recognition of underlying ‘hidden' complexities in these apparently ‘solitary' organizations needs to be accounted for when investigating group living and social behaviour.

Aerial habitats present a challenge to find food across a large potential search volume, particularly for insectivorous bats that rely on echolocation calls with limited detection range and may forage at heights over 1000 m. To understand how bats use vertical space, we tracked one to five foraging flights of eight common noctules (Nyctalus noctula). Bats were tracked for their full foraging session (87.27 ± 24 min) using high-resolution atmospheric pressure radio transmitters that allowed us to calculate height and wingbeat frequency. Bats used diverse flight strategies, but generally flew lower than 40 m, with scouting flights to 100 m and a maximum of 300 m. We found no influence of weather on height, and high-altitude ascents were not preceded by an increase in foraging effort. Wingbeat frequency was independent from climbing or descending flight, and bats skipped wingbeats or glided in 10% of all observations. Wingbeat frequency was positively related to capture mass, and wingbeat frequency was positively related to time of night, indicating an effect of load increase over a foraging bout. Overall, individuals used a wide range of airspace including altitudes that put them at increased risk from human-made structures. Further work is needed to test the context of these flight decisions, particularly as individuals migrate throughout Europe.

Estimating the similarity in space use (spatio-temporal home range overlap) of animals is important for many questions regarding behavioural ecology, wildlife management and conservation. The current methods that calculate joint space use generally do not account for proximity in space use, as all of them rely on the differences between the exact spatial overlay of utilization distributions, while spatial distances between distributions should be considered to truly quantify similarity. We implemented the earth mover's distance (EMD), a spatially explicit method, that quantifies similarity between utilization distributions by calculating the effort it takes to shape one utilization distribution landscape into another, hence EMD. The EMD is a method commonly used in image retrieval applications, and we propose its use to calculate similarity in space use in the framework of movement ecology. We show that the EMD is a consistent and useful as well as versatile measure of overlap and provide an easy to use implementation in the r package move.

Animal migration has fascinated humans at least since Aristotle's time, but we only started to understand its details thanks to the famous "arrow storks" in the 19th century that returned to Europe with an arrow in their body, providing the first clues of African wintering sites. Bird migration has received a large amount of attention since then, but knowledge about migration of other organisms, even small passerine birds, remains rudimentary (Bowlin et al. 2010). published

Made available in DSpace on 2018-11-26T17:44:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-01-26 Robert Bosch Foundation Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission. Senckenberg Gesell Naturforsch, Senckenberg Biodivers & Climate Res Ctr, D-60325 Frankfurt, Main, Germany Goethe Univ, Dept Biol Sci, D-60438 Frankfurt, Main, Germany Univ Maryland, Dept Biol, College Pk, MD 20742 USA Univ Maryland, SESYNC, Annapolis, MD 21401 USA Univ Guelph, Dept Integrat Biol, Guelph, ON N1G 2W1, Canada Norwegian Inst Nat Res, POB 5685 Torgard, NO-7485 Trondheim, Norway Duke Univ, Dept Biol, Durham, NC 27708 USA Duke Univ, Dept Evolut Anthropol, Durham, NC 27708 USA Hirola Conservat Programme, Garissa, Kenya Swedish Univ Agr Sci, Dept Wildlife Fish & Environm Studies, S-90183 Umea, Sweden Radboud Univ Nijmegen, Dept Anim Ecol & Physiol, Inst Water & Wetland Res, NL-6500 GL Nijmegen, Netherlands Univ Fed Mato Grosso do Sul, Ecol & Conservat Grad Program, Campo Grande, MS, Brazil Univ Alberta, Dept Biol Sci, Edmonton, AB, Canada Univ London, Royal Vet Coll, Struct & Motion Lab, London NW1 0TU, England Wildlife Conservat Soc, Mongolia Program, Ulaanbaatar, Mongol Peo Rep Mississippi State Univ, Forest & Wildlife Res Ctr, Carnivore Ecol Lab, Box 9690, Mississippi State, MS USA Sao Paulo State Univ, Anim Biol Postgra Program, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil Michigan Dept Nat Resources, 1990 US 41 South, Marquette, MI 49855 USA Univ Calif Davis, Dept Anthropol, Davis, CA 95616 USA Aarhus Univ, Dept Biosci, DK-4000 Roskilde, Denmark Max Planck Inst Ornithol, Vogelwarte Radolfzell, D-78315 Radolfzell am Bodensee, Germany Wildlife Conservat Soc, Bronx, NY 10460 USA Univ Potsdam, Plant Ecol & Nat Conservat, D-14476 Potsdam, Germany Middle Tennessee State Univ, Dept Biol, Murfreesboro, TN 37132 USA Univ Pretoria, Dept Zool & Entomol, Mammal Res Inst, ZA-0028 Gauteng, South Africa Fdn Edmund Mach, Res & Innovat Ctr, Dept Biodivers & Mol Ecol, I-38010 San Michele All Adige, TN, Italy Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA Natl Zool Pk, Smithsonian Conservat Biol Inst, Front Royal, VA USA Univ Evora, Depat Fitotecn, P-7002554 Evora, Portugal Univ Evora, ICAAM, Inst Mediterranean Agr & Environm Sci, Evora, Portugal Univ Montpellier, Univ Paul Valery Montpellier, CNRS,EPHE, Ctr Ecol Fonctionnelle & Evolut,UMR 5175, F-34293 Montpellier 5, France Phillip Isl Nat Parks, Cowes, Vic, Australia Monash Univ, Sch Biol Sci, Melbourne, Vic, Australia Ohio State Univ, Dept Civil Environm & Geodet Engn, Columbus, OH 43210 USA Fiji Natl Univ, Dept Biol, POB 5529, Natabua, Lautoka, Fiji Univ Massachusetts, US Geol Survey, Massachusetts Cooperat Fish & Wildlife Res Unit, Amherst, MA 01003 USA Penn State Univ, US Geol Survey, Penn Cooperat Fish & Wildlife Res Unit, University Pk, PA 16802 USA Univ Oxford, Dept Zool, Oxford OX1 3PS, England Giraffe Conservat Fdn, POB 86099, Eros, Namibia German Primate Ctr, Behav Ecol & Sociobiol Unit, D-37077 Gottingen, Germany Tech Univ Munich, Dept Ecol & Ecosyst Management, Restorat Ecol, D-85354 Freising Weihenstephan, Germany Cary Inst Ecosyst Studies, Millbrook, NY 12545 USA Univ British Columbia, Irving K Barber Sch Arts & Sci, Unit 2, Biol, Okanagan Campus, Kelowna, BC V1V 1V7, Canada Univ Zurich, Dept Evolutionary Biol & Environm Studies, CH-8057 Zurich, Switzerland Univ Wyoming, Dept Zool & Physiol, Laramie, WY 82071 USA Univ Washington, Sch Environm & Forest Sci, Seattle, WA 98195 USA Univ Montana, Coll Forestry & Conservat, Dept Ecosyst & Conservat Sci, Wildlife Biol Program, Missoula, MT 59812 USA Bavarian Forest Natl Pk, Dept Conservat & Res, D-94481 Grafenau, Germany Albert Ludwigs Univ Freiburg, Wildlife Ecol & Management, D-79106 Freiburg, Germany Univ Toulouse, INRA, CEFS, Castanet Tolosan, France Univ Calif Davis, Anim Behav Grad Grp, Davis, CA 95616 USA Bionet Natr Onderzoek, NL-6171 EL Stein, Netherlands Univ Vet Med Vienna, Res Inst Wildlife Ecol, A-1160 Vienna, Austria Univ Coll Cork, Sch Biol Earth & Environm Sci, Cork, Ireland Univ Wyoming, US Geol Survey, Dept Zool & Physiol, Wyoming Cooperat Fish & Wildlife Res Unit, Laramie, WY USA North Carolina Museum Nat Sci, Raleigh, NC 27601 USA North Carolina State Univ, Dept Forestry & Environm Resources, Raleigh, NC 27695 USA Karatina Univ, Dept Nat Resource Management, POB 195710101, Karatina, Kenya Univ Lethbridge, Dept Psychol, Lethbridge, AB T1K 3M4, Canada Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA Univ Valencia, Terr Vertebrates Grp, Cavanilles Inst Biodivers & Evolutionary Biol, E-46980 Valencia, Spain SUNY Stony Brook, Dept Anthropol, Stony Brook, NY 11794 USA TSG, SSC, IUCN, Rua Licuala 622,Damha 1,Campo Grande, BR-79046150 Mato Grosso Sul, Brazil IPE Inst Pesquisas Ecol Inst Ecol Res, Caixa Postal 47, BR-12960000 Sao Paulo, Brazil Univ Alicante, Dept Ciencias Ambient & Recursos Natur, Vertebrates Zool Res Grp, Alicante, Spain Embrapa Pantanal, BR-79320900 Corumba, MS, Brazil Chico Mendes Inst Conservat Biodivers, Natl Res Ctr Carnivores Conservat, BR-12952011 Atiba, SP, Brazil Univ Glasgow, Inst Biodivers Anim Hlth & Comparat Med, Glasgow, Lanark, Scotland NYU, Dept Biol, Ctr Genom & Syst Biol, New York, NY 10003 USA Univ Calif Davis, Dept Microbiol & Mol Genet, Davis, CA 95616 USA Univ Oslo, Dept Biosci, Ctr Ecol & Evolutionary Synth, NO-0316 Oslo, Norway Hebrew Univ Jerusalem, Alexander Silberman Inst Life Sci, Dept Ecol Evolut & Behav, Movement Ecol Lab, IL-91904 Jerusalem, Israel Norwegian Inst Nat Res, NO-0349 Oslo, Norway Norwegian Univ Sci & Technol NTNU, Dept Math Sci & Ctr Biodivers Dynam, N-7491 Trondheim, Norway Univ Fed Mato Grosso do Sul, Dept Ecol, BR-79070900 Campo Grande, MS, Brazil Field Museum Nat Hist, Integrat Res Ctr, Chicago, IL 60605 USA Consorzio Parco Nazl Stelvio, Bormio, Sondrio, Italy Univ Grenoble Alpes, Irstea, UR LESSEM, BP 76, F-38402 St Martin Dheres, France Univ Bayreuth, BayCEER, D-95447 Bayreuth, Germany Natl Park Schwarzwald, D-77889 Seebach, Germany Univ New South Wales, Evolut & Ecol Res Ctr, Sydney, NSW 2052, Australia Univ New South Wales, Sch Biol Earth & Environm Sci, Sydney, NSW 2052, Australia Penn Game Commiss, Harrisburg, PA 17110 USA Princeton Univ, Dept Ecol & Evolutionary Biol, Princeton, NJ 08544 USA Univ Konstanz, Dept Biol, D-78467 Constance, Germany Off Natl Chasse & Faune Sauvage, Studies & Expertise DRE, F-01330 Birieux, France Univ Haifa, Dept Evolutionary & Environm Biol, IL-3498838 Haifa, Israel Western Ecosyst Technol Inc, Laramie, WY 82070 USA Aarhus Univ, Arctic Res Ctr, DK-8000 Aarhus C, Denmark Polish Acad Sci, Inst Nat Conservat, PL-31120 Krakow, Poland Univ Porto, CIBIO InBIO Associate Lab, P-4485661 Vairao, Portugal Univ Lisbon, Inst Super Agron, InBIO Associate Lab, Ctr Appl Ecol Prof Baeta Neves, P-1349017 Lisbon, Portugal Univ Lisbon, Fac Ciencias, Ctr Ecol Evolut & Environm Changes, P-1749016 Lisbon, Portugal Univ Calif Davis, Dept Environm Sci & Policy, Davis, CA 95616 USA Jackson Hole Conservat, Jackson, WY 83001 USA Univ Vet Med Hannover, Inst Terr & Aquat Wildlife Res, D-30173 Hannover, Germany Univ Calif Santa Cruz, Environm Studies Dept, Ctr Integrated Spatial Res, Santa Cruz, CA 95060 USA Univ Pretoria, Dept Zool & Entomol, ZA-0028 Hatfield, South Africa Colorado State Univ, Dept Fish Wildlife & Conservat Biol, Ft Collins, CO 80523 USA Tatra Natl Pk, PL-34500 Zakopane, Poland Sao Paulo State Univ, Anim Biol Postgra Program, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil

High-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.ObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.MethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.ResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corroborating previous findings for this species.ConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting migratory connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology. published

ContextHigh-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.ObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.MethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.ResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species.ConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

The growing interest in the effects of light pollution on daily and seasonal cycles of animals has led to a boost of research in recent years. In birds, it has been hypothesized that artificial light at night can affect daily aspects of behaviour, but one caveat is the lack of knowledge about the light intensity that wild animals, such as birds, are exposed to during the night. Organisms have naturally evolved daily rhythms to adapt to the 24-h cycle of day and night, thus, it is important to investigate the potential shifts in daily cycles due to global anthropogenic processes such as urbanization. We captured adult male European blackbirds (Turdus merula) in one rural forest and two urban sites differing in the degree of anthropogenic disturbance. We tagged these birds with light loggers and simultaneously recorded changes in activity status (active/non-active) through an automated telemetry system. We first analysed the relationship between light at night, weather conditions and date with daily activity onset and end. We then compared activity, light at night exposure and noise levels between weekdays and weekends. Onset of daily activity was significantly advanced in both urban sites compared to the rural population, while end of daily activity did not vary either among sites. Birds exposed to higher amounts of light in the late night showed earlier onset of activity in the morning, but light at night did not influence end of daily activity. Light exposure at night and onset/end of daily activity timing was not different between weekdays and weekends, but all noise variables were. A strong seasonal effect was detected in both urban and rural populations, such as birds tended to be active earlier in the morning and later in the evening (relative to civil twilight) in the early breeding season than at later stages. Our results point at artificial light at night as a major driver of change in timing of daily activity. Future research should focus on the costs and benefits of altered daily rhythmicity in birds thriving in urban areas.

Summary 1. The distance travelled by animals is an important ecological variable that links behaviour, energetics and demography. It is usually measured by summing straight-line distances between intermittently sampled locations along continuous animal movement paths. The extent to which this approach underestimates travel distance remains a rarely addressed and unsolved problem, largely because true movement paths are rarely, if ever, available for comparison. Here, we use simulated movement paths parameterized with empirical movement data to study how estimates of distance travelled are affected by sampling frequency. 2. We used a novel method to obtain fine-scale characteristics of animal movement from camera trap videos for a set of tropical forest mammals and used these characteristics to generate detailed movement paths. We then sampled these paths at different frequencies, simulating telemetry studies, and quantified the accuracy of sampled travel distance estimation. 3. For our focal species, typical telemetry studies would underestimate distances travelled by 67– 93%, and extremely high sampling frequencies (several fixes per minute) would be required to get tolerably accurate estimates. The form of the relationship between tortuosity, sample frequency, and distance travelled was such that absolute distance cannot accurately be estimated by the infrequent samples used in typical tracking studies. 4. We conclude that the underestimation of distance travelled is a serious but underappreciated problem. Currently, there is no reliable, widely applicable method to obtain approximately unbiased estimates of distance travelled by animals. Further research on this problem is needed.

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal's movement path. 4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks. 5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.

Summary 1. Abundance estimation is a pervasive goal in ecology. The rate of detection by motion-sensitive camera traps can, in principle, provide information on the abundance of many species of terrestrial vertebrates that are otherwise difficult to survey. The random encounter model (REM, Rowcliffe et al. 2008) provides a means estimating abundance from camera trap rate but requires camera sensitivity to be quantified. 2. Here, we develop a method to estimate the area effectively monitored by cameras, which is one of the most important codeterminants of detection rate. Our method borrows from distance sampling theory, applying detection function models to data on the position (distance and angle relative to the camera) where the animals are first detected. Testing the reliability of this approach through simulation, we find that bias depends on the effective detection angle assumed but was generally low at less than 5% for realistic angles typical of camera traps. 3. We adapted standard detection functions to allow for the possibility of smaller animals passing beneath the field of view close to the camera, resulting in reduced detection probability within that zone. Using a further simulation to test this approach, we find that detection distance can be estimated with little or no bias if detection probability is certain for at least some distance from the camera. 4. Applying this method to a 1-year camera trapping data set from Barro Colorado Island, Panama, we show that effective detection distance is related strongly positively to species body mass and weakly negatively to species average speed of movement. There was also a strong seasonal effect, with shorter detection distance during the wet season. Effective detection angle is related more weakly to species body mass, and again strongly to season, with a wider angle in the wet season. 5. This method represents an important step towards practical application of the REM, including abundance estimation for relatively small (

Optimal cache spacing theory predicts that scatter-hoarding animals store food at a density that balances the gains of reducing cache robbery against the costs of spacing out caches further. We tested the key prediction that cache robbery and cache spacing increase with the economic value of food: the ratio of food to consumer abundance. We quantified cache pilferage and cache spacing by the Central American agouti, Dasyprocta punctata, in the tropical forest of Barro Colorado Island, Panama, across 10 1 ha plots that encompassed a more than 100-fold range in the availability of Astrocaryum palm seeds, the agouti's principal food. We found that caches were pilfered at higher rates in plots with lower seed availability, and that agoutis cached seeds further away and into lower densities where seed availability was lower. Food scarcity apparently increased the pressure of food competitors on caches, stimulating agoutis to put more effort into caching seeds to create lower cache densities, fully consistent with theory. We conclude that the optimal cache density depends not only on the nutritional value of food but also on the economic value, which may vary in space as well as time. (C) 2009 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

GIScience 2016 Short Paper Proceedings An Algorithm for Empirically Informed Random Trajectory Generation Between Two Endpoints G. Technitis 1 , R. Weibel 1 , B. Kranstauber 2,3 , K. Safi 2,3 ! Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland Email: georgios.technitis@geo.uzh.ch ! Department of Migration and Immuno-ecology, Max Planck Institute for Ornithology, Am Obstberg 1, 78315 Radolfzell, Germany ! Department of Biology, University of Konstanz, Konstanz, Germany Abstract We present a method for enabling the researcher to create empirically informed, and thus realistic, random trajectories between two endpoints. The method used relies on empirical distribution functions, which define a dynamic drift expressed in a stepwise joint probability surface. We create random discrete time-step trajectories that connect spatiotemporal points while maintaining a predefined geometry, often based on real observed trajectory. The resulting trajectories can be used a)to generate null models for hypotheses testing, b)as a basis for resource selection models, through the integration of spatial context and c)to quantify space use intensity. 1.! Introduction Random trajectories have been increasingly used in movement ecology since their introduction in the early 1980s (Kareiva and Shigesada 1983), gaining significant popularity in the last two decades (Turchin 1998). A wide range of case studies have used the concept, addressing multiple questions related to movement and space use. The majority of the examples found in the literature, however, share one characteristic: the movement has only one restrictive point, the start. Consequently, the simulation is forced to start at a specific location, but can then move according to the set conditions in the given space. In the real world however, this is not always useful: when studying migration patterns (Codling et al. 2010), nest borrowing (Waldeck et al. 2008), or fusion of high and low frequency GPS points, etc. the ability to specify the ending point is crucial. Technitis et al. (2015) introduced RTG, an algorithm that enables the user to create randomly varying, possible trajectories between endpoints, based on principles of Time Geography. In this paper we substantially extend this algorithm. We present a methodology to connect two endpoints by generating empirically informed random trajectories, respecting characteristics of the moving object. Our approach is based on core theoretical concepts of Time Geography in combination with the Random Walk movement model, and most importantly, we use empirical data to inform our modelling process. 2.! Background Space-time prisms (STP) assist us in calculating the points accessible in space, given the time budget and the maximum speed of an agent (Kuijpers, et al. 2010). The calculated path space (in three dimensions defined by x,y and t), and more specifically its 2-D spatial projection, also known as potential path area (PPA), is a homogenous area within which the trajectory lies. The concept of the STP is very intuitive, although it accounts only for the maximum speed of the mover, gives no information regarding the preference of the mover within the given boundaries, and the result is an area, not an individual trajectory.

Travel speed (average speed of travel while active) and day range (average speed over the daily activity cycle) are behavioural metrics that influence processes including energy use, foraging success, disease transmission and human-wildlife interactions, and which can therefore be applied to a range of questions in ecology and conservation. These metrics are usually derived from telemetry or direct observations. Here, we describe and validate an entirely new alternative approach, using camera traps recording passing animals to measure movement paths at very fine scale. Dividing the length of a passage by its duration gives a speed observation, and average travel speed is estimated by fitting size-biased probability distributions to a sample of speed observations. Day range is then estimated as the product of travel speed and activity level (proportion of time spent active), which can also be estimated from camera-trap data. We field tested the procedure with data from a survey of terrestrial mammals on Barro Colorado Island, Panama. Travel speeds and day ranges estimated for 12 species scaled positively with body mass, and were higher in faunivores than in herbivores, patterns that are consistent with those obtained using independent estimates derived from tracked individuals. Comparisons of our day range estimates with independent telemetry-based estimates for three species also showed very similar values in absolute terms. We conclude that these methods are accurate and ready to use for estimating travel speed and day range in wildlife. Key advantages of the methods are that they are non-invasive, and that measurements are made at very high resolution in time and space, yielding estimates that are comparable across species and studies. Combined with emerging techniques in computer vision, we anticipate that these methods will help to expand the range of species for which we can estimate movement rate in the wild.

During migratory journeys, birds may become displaced from their normal migratory route. Experimental evidence has shown that adult birds can correct for such displacements and return to their goal. However, the nature of the cues used by migratory birds to perform long distance navigation is still debated. In this experiment we subjected adult lesser black-backed gulls migrating from their Finnish/Russian breeding grounds (from >60°N) to Africa (to

Birds migrate over vast distances at substantial costs. The highly dynamic nature of the air makes the selection of the best travel route difficult. We investigated to what extent migratory birds may optimise migratory route choice with respect to wind, and if route choice can be subject to natural selection. Following the optimal route, calculated using 21 years of empirical global wind data, reduced median travel time by 26.5% compared to the spatially shortest route. When we used a time-dependent survival model to quantify the adaptive benefit of choosing a fixed wind-optimised route, 84.8% of pairs of locations yielded a route with a higher survival than the shortest route. This suggests that birds, even if incapable of predicting wind individually, could adjust their migratory routes at a population level. As a consequence, this may result in the emergence of low-cost flyways representing a global network of aerial migratory pathways. published

[Background] In recent years high resolution animal tracking data has become the standard in movement ecology. The Brownian Bridge Movement Model (BBMM) is a widely adopted approach to describe animal space use from such high resolution tracks. One of the underlying assumptions of the BBMM is isotropic diffusive motion between consecutive locations, i.e. invariant with respect to the direction.Here we propose to relax this often unrealistic assumption by separating the Brownian motion variance into two directional components, one parallel and one orthogonal to the direction of the motion., [Results] Our new model, the Bivariate Gaussian bridge (BGB), tracks movement heterogeneity across time. Using the BGB and identifying directed and non-directed movement within a trajectory resulted in more accurate utilisation distributions compared to dynamic Brownian bridges, especially for trajectories with a non-isotropic diffusion, such as directed movement or Lévy like movements. We evaluated our model with simulated trajectories and observed tracks, demonstrating that the improvement of our model scales with the directional correlation of a correlated random walk., [Conclusion] We find that many of the animal trajectories do not adhere to the assumptions of the BBMM. The proposed model improves accuracy when describing the space use both in simulated correlated random walks as well as observed animal tracks. Our novel approach is implemented and available within the “move” package for R., The International Max Planck Research School for Organismal Biology for support of BK, The International Max Planck Research School for Organismal Biology for support of BK; F.B. acknowledges Grants RyC-2009-04133, and BFU2010-22337 from the Ministry of Science and Innovation (Spain), and the Human Frontier Science Program (Ref. RGY0084/2011); Rechenzentrum Garching of the Max Planck Society for support and maintenance of the computational cluster; Track owners for providing sample data: Fisher data, Scott LaPoint, supported by NSF (Grant 0756920 to Roland Kays) and National Geographic Society Waitt Grant Program (Grant W157-11 to SL), Eagle owl data, commissioned by Paul Voskamp (province of Limburg, the Netherlands) collected by René Janssen (Bionet natuuronderzoek), Stork Data, Martin Wikelski, HUJ-MPIO (Hebrew University Jerusalem, PI Ran Nathan) and MPIO joint Deutsch-Israelisches Project Grant and the Storchenhof Lohburg, Turkey Vulture data, Keith Bildstein and the Hawk Mountain Sanctuary, Waved Albatross data, Sebatian Cruz and the Galapagos National Park Service, Straw-coloured fruit bat, Dina Dechmann, Jakob Fahr and Martin Wikelski.

Background Understanding how environmental conditions, especially wind, influence birds' flight speeds is a prerequisite for understanding many important aspects of bird flight, including optimal migration strategies, navigation, and compensation for wind drift. Recent developments in tracking technology and the increased availability of data on large-scale weather patterns have made it possible to use path annotation to link the location of animals to environmental conditions such as wind speed and direction. However, there are various measures available for describing not only wind conditions but also the bird's flight direction and ground speed, and it is unclear which is best for determining the amount of wind support (the length of the wind vector in a bird’s flight direction) and the influence of cross-winds (the length of the wind vector perpendicular to a bird’s direction) throughout a bird's journey. Results We compared relationships between cross-wind, wind support and bird movements, using path annotation derived from two different global weather reanalysis datasets and three different measures of direction and speed calculation for 288 individuals of nine bird species. Wind was a strong predictor of bird ground speed, explaining 10-66% of the variance, depending on species. Models using data from different weather sources gave qualitatively similar results; however, determining flight direction and speed from successive locations, even at short (15 min intervals), was inferior to using instantaneous GPS-based measures of speed and direction. Use of successive location data significantly underestimated the birds' ground and airspeed, and also resulted in mistaken associations between cross-winds, wind support, and their interactive effects, in relation to the birds' onward flight. Conclusions Wind has strong effects on bird flight, and combining GPS technology with path annotation of weather variables allows us to quantify these effects for understanding flight behaviour. The potentially strong influence of scaling effects must be considered and implemented in developing sampling regimes and data analysis. Electronic supplementary material The online version of this article (doi:10.1186/2051-3933-1-4) contains supplementary material, which is available to authorized users.

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal's movement path. 4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks. 5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.

The analysis of trajectories has become an important field in geographic visualization, as cheap GPS sensors have become commonplace and, in many cases, valuable information can be derived either from the data themselves or their metadata if processed and visualized in the right way. However, showing the “right” information to highlight dependencies or correlations between different measurements remains a challenge, because the technical intricacies of applying a combination of automatic and visual analysis methods prevents the majority of domain experts from analyzing and exploring the full wealth of their movement data. This paper presents an exploration through enrichment approach, which enables iterative generation of metadata based on exploratory findings and is aimed at enabling domain experts to explore their data beyond traditional means.

SUMMARYAnimals must optimize their daily energy budgets, particularly if energy expenditures are as high as they are in flying animals. However, energy budgets of free-ranging tropical animals are poorly known. Newly miniaturized heart rate transmitters enabled this to be addressed this in the small, energetically limited, neotropical bat Molossus molossus. High-resolution 48 h energy budgets showed that this species significantly lowers its metabolism on a daily basis, even though ambient temperatures remain high. Mean roosting heart rate was 144 beats min–1, much lower than expected for a 10 g bat. Low roosting heart rates combined with short nightly foraging times (37 min night–1) resulted in an estimated energy consumption of 4.08 kJ day–1, less than one-quarter of the predicted field metabolic rate. Our results indicate that future research may reveal this as a more common pattern than currently assumed in tropical animals, which may have implications in the context of the effect of even small temperature changes on tropical species.

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a species at a location, recording their movement in the Eulerian sense. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a year-long terrestrial animal monitoring system at Barro Colorado Island, Panama. The data gathered from our camera network shows the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans.