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What acceleration data from wildlife collars and animal body mass tell us about seed dispersal



The seeds of many plant species can be dispersed over long distances in animal fur (epizoochory). Quantifying epizoochory in the wild is, however, challenging, since it is difficult to measure the retention times of seeds in fur. These retention times depend on the acceleration that seeds experience and that can detach seeds from fur. Wildlife collars containing accelerometers may thus provide crucial information on epizoochorous seed dispersal. However, this is only the case if acceleration of the animal’s neck (where collars are attached) is informative of acceleration of the animal’s main body (where most seeds are transported).


We used accelerometers to simultaneously measure acceleration at the neck, breast and the upper hind leg of 40 individuals of eight mammal species spanning a large range of body masses (26–867 kg). We then quantified maximum acceleration as the 95%-quantile of the resultant acceleration (of all measured values in data intervals of 5 s).


Maximum acceleration was comparable between the neck and breast but substantially higher at the hind leg. Maximum acceleration measured by neck collars and body mass jointly explained 81% of the variance in maximum acceleration of the breast and 62% of the variance in maximum acceleration of the leg.


Acceleration measured by neck collars is informative of the acceleration experienced by seeds attached to other body parts (breast and leg). When combined with animal movement data and lab measurements of how fur acceleration affects seed release and retention times, widely used collar accelerometers can thus be used to assess distances of epizoochorous seed dispersal.


Animals are among the most important vectors for long distance dispersal of plant seeds [14, 22, 30]. They disperse seeds via endozoochory (passage through their digestive system, e.g., [16, 26]). However, many animals (notably mammals) also transport large numbers of seeds of many plant species via attachment to the exterior of the body (epizoochory). A major challenge in the understanding and prediction of epizoochorous seed dispersal is the quantification of seed detachment from the animal. Consequently, we lack information on the retention time of seeds in animal fur, a crucial parameter for quantifying seed dispersal and dispersal distances [30]. This study aims at quantifying and explaining shaking movements, relevant for seed detachment from different parts of mammal bodies. In particular, it evaluates whether wildlife collars fitted with accelerometers, that are now widely used on wild animals, can inform us on the process of seed detachment.

Two aspects of epizoochorous seed dispersal, namely, seed attachment to the fur and animal movement are comparatively well-studied by moving animal furs through/along vegetation [13], combing furs of wild animals (e.g., [20]) and recording animal movement with modern tracking technology (e.g., [18, 40]). However, the quantification of seed detachment, which determines seed retention time and hence dispersal distance, is more challenging, since epizoochorously dispersed seeds are typically small, hidden in animal fur and cannot be observed without altering animal behaviour [29].

Since it is difficult to investigate seed detachment in the wild, previous studies have resorted to lab measurements of the forces needed to detach seeds from fur or the time needed to shake seeds out of the fur. They found that detachment is controlled by the interplay of the animal surface (e.g., fur properties) and seed morphology (e.g., appendages like hooks), which determines the ‘contact separation force’ that is needed to detach the seed from the fur [10, 11, 17, 37]. On an animal body forces to release seeds can be created via shaking of the body. According to Newton’s second law of motion, the force experienced by a seed is the product of seed mass and fur acceleration. It has been shown that seed release can be induced by fur acceleration [35] and that the strength of fur shaking determines the proportion of seeds released (see Additional file 1: Figure S1).

Accelerometers, that measure acceleration at high frequency can quantify the force that seeds of a given mass experience in animal furs (according to Newton’s second law of motion, see above). Hence, accelerometers on bodies of wild animals may provide information on seed detachment from and seed retention in fur. Thanks to rapid advances in GPS telemetry in the last decade [6, 19] more and more studies measure animal acceleration in the wild, since nowadays many commercially available GPS tracking collars (e.g., Eobs, Vectronic aerospace, Biotrack) are equipped with three-dimensional accelerometers and the recording of acceleration data does not cause much additional effort and costs (apart from battery life and storage space). These data can be used to infer animal activity, energy budgets or even specific behavioural patterns or syndromes [4, 15, 25, 34].

Acceleration measurements on wild animals are mostly taken at the neck [8, 38], whereas most seeds are attached to lower parts of the animal torso and the legs (compare [1, 27, 32]). However, it is largely unclear how acceleration of animal necks is related to acceleration of other body parts. Moreover, such relationships may depend on properties of the animals, notably their body mass. In general, smaller animals show faster (limb) movements which should result in higher acceleration of their body and hence larger forces acting on seeds in their fur (compare [7, 9, 24]). In contrast, higher inertia of the torso of larger animals could possibly result in a weaker link between the acceleration of the limbs and the neck.

To assess the value of collar accelerometers for assessing epizoochorous seed dispersal by wild animals, we measured acceleration simultaneously at different body parts of mammals ranging in body mass from 26 to 867 kg. We then quantified (i) how acceleration at the breast/torso and the leg of mammals is related to acceleration at the neck of animals, and (ii) how this relationship depends on animal body mass.


We measured three-dimensional acceleration on different sections of the animal body for 40 individuals of 13 breeds of 8 mammal species kept at the Agricultural Science Faculty of the University of Hohenheim and Wilhelma Zoological Garden, Stuttgart (Table 1, Fig. 1). To this end, accelerometers were attached with nylon straps (2.3 cm wide, metal buckles) to the neck, breast and hind leg (shank, between knee and heel) of the animals. Deployment of sensors to the breast and leg was easily feasible and caused minimal distress for the animals. Where necessary (e.g., for goats with short fur and thin legs), sensors were additionally fixed to the upper leg of the animals with adhesive elastic bandages. Animals were then left to move for at least 5 min in their enclosures (indoors and outdoors) with the aim of recording a minimum of three time segments of at least 5 s with (i) walking-like movement and (ii) running movement. If necessary and possible animals were tempted to move by leading them on a leash (camel, horse) or gently chasing them (sheep, goat). This research was approved by the animal welfare officer of the University of Hohenheim (Nr. S 476/18 LÖ).

Table 1 List of species and individuals used for the study including additional information on the animals and study conditions. Locations were all in Baden-Württemberg, Germany; Wilhelma Zoological Garden and research facility Hohenheim “Meiereihof “ are in Stuttgart, the research station Hohenheim “Unterer Lindenhof “ is located in Eningen unter Achalm
Fig. 1
figure 1

Examples of animals [top left: donkey (Equus asinus), top right: camel (Camelus ferus), bottom: goat (Capra aegagrus)] carrying accelerometers (green arrows: MSR 165 and yellow arrows: e-obs sensor, tag 4462) around the neck, the breast and the upper hind leg. Photos taken in Wilhelma Zoological Garden in Stuttgart, Germany

Sensors used were two MSR 165 (MSR Electronics GmbH, Seuzach, Switzerland), recording continuously, and e-obs GPS and acceleration neck collars (e-obs GmbH, Grünwald, Germany), recording acceleration in so-called “bursts” of 330 values (3.3 s) followed by a (technically inevitable) gap of approx. 1.4 s. Positions of the different sensors were randomly alternated. Still, a large e-obs sensor (tag 1653) was only used with four individuals, a smaller e-obs sensor (tag 4462) was more often used at the leg, since it was less disturbing for the animal and could be attached more easily and stable at the leg compared to the slightly heavier MSR sensors. Before use all four sensors were tested for comparability by simultaneously measuring the movement of a laboratory shaker. This showed negligible variation between sensors (max. 3.5% variation in maximum acceleration of any single sensor from the mean of all sensors). All sensors were set to record at 100 Hz (i.e., each of the three axes would record at 33.3 Hz). Temporal synchronization of all collars/sensors was achieved by starting a 0.1 s resolution stop watch at the same time as manually shaking all three sensors for approx. 15 s. This “extreme acceleration event” could later easily be recognized at the beginning of the data series of all sensors and defined the beginning of the specific measurement session. The time of the stop watch was used as reference for any observation during the animal trials that could be linked to the data series (start and end of valid recording period for any animal). Body mass of each individual was obtained from the respective zookeepers (last weighing).

Acceleration data series were calibrated (raw measurement values transformed to m/s2) according to manufacturer instructions and visually checked for synchronism between neck breast and leg. For each animal the acceleration timeseries were cut into 5 s intervals on which analyses were performed. We chose an interval length of 5 s, since this was short enough to cover only a single type of behaviour but long enough to minimize the impact of recording gaps of the e-obs sensors (see above). We did, however, repeat all analyses with 10 s intervals and found that this did not notably change results.

From acceleration measurements in three dimensions, we calculated body acceleration by calculating the resultant acceleration vector (resultant acceleration = sqrt(accelerationX2 + accelerationY2 + accelerationZ2) and subtracting gravitational acceleration (9.81 m/s2). We then calculated the 95%-quantile of body acceleration per 5 s interval as a measure of maximum acceleration (Fig. 2). Intervals with maximum neck acceleration < 0.1 m/s2 were excluded from further analyses, since they represent phases when the animals did not move.

Fig. 2
figure 2

Timeseries (covering 400 s) of the acceleration measured on the neck of a goat in each of the three axes (upper panel), the length of the resultant acceleration vector (middle panel) and the maximum acceleration (lower panel). Maximum acceleration is calculated as the 95% quantile of resultant acceleration minus gravitational acceleration per 5 s interval

To investigate how well maximum acceleration of other body parts can be explained by maximum acceleration of the neck and by an animal’s body mass we fitted linear mixed-effects models (packages lme4, [2] in R version 4.0.2 [33]). The response variables of these models were maximum acceleration at the breast and the leg, respectively. As fixed-effect predictor variables both models included maximum acceleration at the neck and individual body mass plus the interaction of these two variables. The models also included random effects of individual nested within species on the intercept and the slope for neck acceleration (Additional file 1: Eqs. 1, 2). These random effects capture variation not accounted for by body mass (resulting from other animal traits or measurement conditions). All variables were log-transformed and scaled, to yield power-law scaling relationships.


Maximum acceleration (the 95% quantile of body acceleration per 5 s interval) varied considerably between species and individuals (Fig. 3, Additional file 1: Figure S2). Acceleration values and their variability (within an between species) were much larger at the hind leg than at the neck or breast.

Fig. 3
figure 3

Boxplots showing maximum acceleration at the neck, breast and leg of eight mammal species (ordered by increasing mean body mass). Maximum acceleration is calculated as the 95% quantile of resultant acceleration minus gravitational acceleration per 5 s interval. Outliers are omitted for clarity

Acceleration at the breast of animals is well-explained by acceleration at the neck of animals. Body mass slightly weakens the positive effect of neck acceleration on breast acceleration (negative interaction term with neck acceleration, Fig. 4, left panel). The marginal R2 (variance explained by fixed effects only, i.e., neck acceleration and body mass) is 0.81. In addition, the acceleration at the hind leg is well-explained by neck acceleration and body mass (marginal R2 = 0.62), but here body mass increases the effect of neck acceleration on leg acceleration (Fig. 4, right panel). Coefficients of fitted models, likelihood-ratio tests and AIC values are given in the Additional file materials (Additional file 1: Tables S1, S2). Besides neck acceleration and body mass, some variability in body shaking is also explained by individuals and species (conditional R2 including fixed effects and random effect of individual nested within species was 0.89 and 0.71 for breast and leg acceleration, respectively). By back-transforming the fixed-effect components of the fitted (full) models, we obtain the following equations for acceleration A at the breast (Eq. 1) and leg (Eq. 2):

Fig. 4
figure 4

Prediction plots of linear mixed-effects models for maximum acceleration at the breast (left panel) and the hind leg (right panel). Predictions are only shown for fixed effects, namely neck acceleration and body mass; line thickness and symbol size indicate mean species body mass and individual body mass, respectively, see legend. Maximum acceleration is calculated as the 95% quantile of resultant acceleration minus gravitational acceleration per 5 s interval

$${A}_{breast}=0.534*{{A}_{neck}}^{1.547}*{mass}^{0.094}*{{A}_{neck}}^{-0.107 * mass}$$
$${A}_{leg} =4.222 *{{A}_{neck}}^{0.922}*{mass}^{-0.170}*{{A}_{neck}}^{0.044 * mass}$$


This study shows that maximum acceleration of the breast and leg of mammals can be predicted well from two variables that are widely available for wild mammals: body mass (the most frequently used trait in animal ecology; [5, 7, 39] and acceleration of the neck (now routinely measured by many wildlife collars). This makes it possible to translate acceleration measurements at the neck into the forces experienced by plant seeds attached to other body parts, a crucial step for assessing epizoochorous seed dispersal by wild mammals. The predictive capacity of maximum neck acceleration and body mass was somewhat higher for maximum acceleration of the breast than for maximum acceleration of the hind leg. This could be explained by the larger spatial separation of neck and hind legs. Moreover, different behaviours, walking modes, gaits or movement speeds in the moment of measurement should more directly affect leg movement and hence, cause partial independence of leg and neck acceleration. The fact that individual and species did not explain more variance of leg acceleration than of breast acceleration (both less than 10%) supports this interpretation, namely, that such behavioural aspects play an important role, especially compared to other species-specific characteristics like body composition, geometry, leg length etc.

The weaker positive effect of neck acceleration on breast acceleration for larger animals is likely to result from greater torso inertia in large-bodied animals (compare [28]). To some extent, it may also reflect greater neck length in large animals (notably camels) which may cause weaker translation of head movements into torso movement.

Quantification of acceleration at the body of mammals is of crucial importance for epizoochorous seed dispersal (compare [35]). For the removal of seeds with strongly attaching appendages (e.g., hooks) intentional shaking, grooming behaviour or rubbing against objects [27] are obviously very relevant. Among these at least intentional shaking can still be recorded with acceleration measurements. However, numerous vascular plant species without obvious morphological adaptations to epizoochory, such as hooked appendages, are transported in animal furs [13]. Particularly for these seeds body acceleration while walking and running can be expected to be a very important factor causing seed release (compare Additional file 1: FigureS1).

To mechanistically predict distance of epizoochorous seed dispersal, acceleration measurements have to be integrated with other types of data. First, estimates of the acceleration and resulting force experienced by seeds need to be combined with either direct measures of the contact separation force of seeds in a particular fur [17] or with measurements of the distribution of seed retention times for given fur acceleration [35]. This will yield distributions of retention times for a specific seed-fur combination. Secondly, by combining these retention time distributions with measures of animal speed or spatially explicit movement trajectories, one can obtain distances of epizoochorous seed dispersal (analogous to [36, 40] for endozoochorous seed dispersal).

Knowledge of variation in acceleration across animal bodies may also be relevant for ecological fields other than the study of seed dispersal. Body acceleration determines the forces experienced not only by seeds but also by animals such as grasshoppers that are dispersed in fur [13]. Moreover, ecto-parasites have to spend more energy when experiencing strong and repeated acceleration, while they crawl through the fur until they reach their targeted feeding location [31]. Once an ecto-parasite started feeding, the acceleration it experiences should become even more relevant, since it determines how strongly attachment force has to increase as the parasite’s mass increases [23]. Such variation in energy expenditure is likely to affect the fitness of ecto-parasites and their hosts.

Outlook and conclusions

Acceleration measurements at animal necks contain valuable information on epizoochorous seed dispersal by wild mammals. Since such measurements are now widely available, there is considerable potential for ‘recycling’ them [21] to assess the dispersal services provided by wild animals [12].

Availability of data and materials

All data, specifically measured acceleration of all animals, are published at



Maximum acceleration at animals’ necks (m/s2) Quantified as the 95%-quantile of resultant acceleration


Maximum acceleration at animals’ breasts (m/s2) Quantified as the 95%-quantile of resultant acceleration


Maximum acceleration at animals’ hind legs (m/s2) Quantified as the 95%-quantile of resultant acceleration


Individual body mass (kg)


  1. Albert A, Mårell A, Picard M, Baltzinger C. Using basic plant traits to predict ungulate seed dispersal potential. Ecography. 2015.

    Article  Google Scholar 

  2. Bates D, Machler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015.

  3. Benthien O, Bober J, Castens J, Stolter C. Seed dispersal capacity of sheep and goats in a near-coastal dry grassland habitat. Basic Appl Ecol. 2016.

    Article  Google Scholar 

  4. Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP. Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelem. 2013.

    Article  Google Scholar 

  5. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. Toward a metabolic theory of ecology. Ecology. 2004.

    Article  Google Scholar 

  6. Cagnacci F, Boitani L, Powell RA, Boyce MS. Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Phil Trans Royal Soc B Biol Sci. 2010.

    Article  Google Scholar 

  7. Calder WA. Size function, and life history. Cambridge: Harvard University Press; 1984.

    Google Scholar 

  8. Chakravarty P, Cozzi G, Ozgul A, Aminian K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol Evol. 2019.

    Article  Google Scholar 

  9. Cloyed CS, Grady JM, Savage VM, Uyeda JC, Dell AI. The allometry of locomotion. Ecology. 2021.

  10. Couvreur M, Couvreur M, Vandenberghe B, Verheyen K, Hermy M. An experimental assessment of seed adhesivity on animal furs. 2004. Seed Sci Res.

  11. De Pablos I, Peco B. Diaspore morphology and the potential for attachment to animal coats in Mediterranean species: an experiment with sheep and cattle coats. Seed Sci Res. 2007.

    Article  Google Scholar 

  12. Farwig N, Berens DG. Imagine a world without seed dispersers: a review of threats, consequences and future directions. Basic Appl Ecol. 2012.

    Article  Google Scholar 

  13. Fischer SF, Poschlod P, Beinlich B. Experimental studies on the dispersal of plants and animals on sheep in calcareous grasslands. J Appl Ecol. 1996.

    Article  Google Scholar 

  14. Fricke EC, Ordonez A, Rogers HS, Svenning J-C. The effects of defaunation on plants’ capacity to track climate change. Science. 2022.

    Article  PubMed  Google Scholar 

  15. Gleiss AC, Wilson RP, Shepard ELC. Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure. Method Ecol Evol. 2011.

    Article  Google Scholar 

  16. González-Varo JP, Carvalho CS, Arroyo JM, Jordano P. Unravelling seed dispersal through fragmented landscapes: Frugivore species operate unevenly as mobile links. Mol Ecol. 2017.

    Article  PubMed  Google Scholar 

  17. Gorb E, Gorb S. Contact separation force of the fruit burrs in four plant species adapted to dispersal by mechanical interlocking. Plant Physiol Biochem. 2002.

    Article  Google Scholar 

  18. Gurarie E, Fleming CH, Fagan WF, Laidre KL, Hernández-Pliego J, Ovaskainen O. Correlated velocity models as a fundamental unit of animal movement synthesis and applications. Mov Ecol. 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hallworth MT, Marra PP. Miniaturized GPS tags identify non-breeding territories of a small breeding migratory songbird. Nature Sci Rep. 2015.

    Article  Google Scholar 

  20. Heinken T, Hanspach H, Raudnitschka D, Schaumann F. Dispersal of vascular plants by four species of wild mammals in a deciduous forest in NE Germany. Phytocoenologia. 2002.

    Article  Google Scholar 

  21. Hampton SE, Strasser CA, Tewksbury JJ, Gram WK, Budden AE, Batcheller AL, Duke CS, Porter JH. Big data and the future of ecology. Front Ecol Environ. 2013.

    Article  Google Scholar 

  22. Howe HF, Smallwood J. Ecology of seed dispersal. Ann Rev Ecol Evol Syst. 1982.

    Article  Google Scholar 

  23. Kampowski T, Schuler B, Speck T, Poppinga S. The effects of substrate porosity, mechanical substrate properties and loading conditions on the attachment performance of the mediterranean medicinal leech (Hirudo verbana). J Royal Soc Interface. 2022.

    Article  Google Scholar 

  24. Kilbourne BM, Hoffman LC. Scale Effects between body size and limb design in quadrupedal mammals. PLoS ONE. 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kröschel M, Reineking B, Werwie F, Wildi F, Storch I. Remote monitoring of vigilance behavior in large herbivores using acceleration data. Animal Biotelemetry. 2017.

    Article  Google Scholar 

  26. Lepková B, Horčičková E, Vojta J. Endozoochorous seed dispersal by free-ranging herbivores in an abandoned landscape. Plant Ecol. 2018.

    Article  Google Scholar 

  27. Liehrmann O, Jégoux F, Guilbert MA, Isselin-Nondedeu F, Saïd S, Locatelli Y, Baltzinger C. Epizoochorous dispersal by ungulates depends on fur, grooming and social interactions. Ecol Evol. 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mohamed Thangal SN, Donelan JM. Scaling of inertial delays in terrestrial mammals. PLoS ONE. 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mouissie AM, Lengkeek W, Van Diggelen R. Estimating adhesive seed-dispersal distances: field experiments and correlated random walks. Funct Ecol. 2005.

    Article  Google Scholar 

  30. Nathan R, Schurr FM, Spiegel O, Steinitz O, Trakhtenbrot A, Tsoar A. Mechanisms of long-distance seed dispersal. Trends Ecol Evol. 2008.

    Article  PubMed  Google Scholar 

  31. Nilsson A, Lundqvist L. Host selection and movements of Ixodes Ricinus (Acari) larvae on small mammals. Oikos. 1978.

    Article  Google Scholar 

  32. Petersen TK, Bruun HH. Can plant traits predict seed dispersal probability via red deer guts, fur, and hooves. Ecol Evol. 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  33. R Development Core Team. R. A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2008.

    Google Scholar 

  34. Rast W, Kimmig SE, Giese L, Berger A. Machine learning goes wild: using data from captive individuals to infer wildlife behaviours. PLoS ONE. 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Römermann C, Tackenberg O, Poschlod P. How to predict attachment of seeds to sheep and cattle potential from simple morphological seed traits. Oikos. 2005.

    Article  Google Scholar 

  36. Schurr FM, Spiegel O, Steinitz O, Trakhtenbrot A, Tsoar A, Nathan R. Long-distance seed dispersal. In Ann Plant Rev. 2009.

    Article  Google Scholar 

  37. Tackenberg O, Römermann C, Thompson K, Poschlod P. What does diaspore morphology tell us about external animal dispersal evidence from standardized experiments measuring seed retention on animal-coats. Basic Appl Ecol. 2006.

    Article  Google Scholar 

  38. Weegman MD, Bearhop S, Hilton GM, Walsh AJ, Griffin L, Resheff YS, Nathan R, Fox AD. Using accelerometry to compare costs of extended migration in an arctic herbivore. Curr Zool. 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  39. White EP, Ernest SKM, Kerkhoff AJ, Enquist BJ. Relationships between body size and abundance in ecology. Trends Ecol Evol. 2007.

    Article  PubMed  Google Scholar 

  40. Wright SJ, Heurich M, Buchmann CM, Böcker R, Schurr FM. The importance of individual movement and feeding behaviour for long-distance seed dispersal by red deer a data-driven mode. Mov Ecol. 2020.

    Article  PubMed  PubMed Central  Google Scholar 

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The authors thank many scientists and technicians that helped in data collection: the Biomove team at the University of Potsdam, specifically F. Jeltsch and W. Ullmann, the working groups Behavioral Physiology of Livestock and of Animal Nutrition at the University of Hohenheim and the teams at Meiereihof and Unterer Lindenhof, specifically M. Rodehutscord, V. Stefanski, B. Pfaffinger, J. Krieg, H. Trapp, W. Dunne and M. Ganser, and the team of Wilhelma Zoological Garden, specifically B. Schäfer and G. Schleussner.


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CMB and FMS conceived the idea. CMB organized and led data collection, analysed the data and wrote the manuscript. LD and MC assisted data collection. FMS, LD and MC assisted analysis. All authors contributed critically to the manuscript drafts and gave final approval for publication. All authors read and approved the final manuscript.

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Correspondence to Carsten M. Buchmann.

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This research was approved by the animal welfare officer of the University of Hohenheim (Nr. S 476/18 LÖ).

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Supplementary Information

Additional file 1:

Figure S1. Accelerationmeasured on a laboratory shaker running at three different intensitiesfor 25 s, and maximum acceleration, quantified as the 95% quantile of the resultant acceleration in subsections of 5 s. The acceleration created by this laboratory shaker is comparable to the acceleration measured on the animal bodies. Symbols show the proportion of three herb seeds that were separated from a rabbit furafter running in each intensity for 450 s (mean +/ − S.E.of three runs, each with 15 seeds of any species placed on the fur). Note: some noise is added to the x-coordinates of the symbols to improve readability. Figure S2. Boxplots showing maximum accelerationfile as well.acceleration) determined for 5 s subsections of acceleration data measured at the neck, breast and leg of 40 individuals of 13 breeds of 8 mammal species; ordered after individual body mass. Outliers are omitted for clarity. Table S1. Summary of fitted linear mixed-effects models for breast and leg acceleration. Model coefficients are for models with log-transformed and scaled variables. Likelihood-ratio tests were performed between full modeland additive modelfor the interaction term, and for Aneck and mass they were performed between the additive model and the model containing only mass and Aneck, respectively. Table S2. AIC values of the full linear mixed-effects models for breast and leg accelerationand reduced simplified model versions.

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Buchmann, C.M., Dreyling, L., Constantin, M. et al. What acceleration data from wildlife collars and animal body mass tell us about seed dispersal. Anim Biotelemetry 11, 22 (2023).

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