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

doi:10.21203/rs.3.rs-2096604/v1

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

https://doi.org/10.21203/rs.3.rs-2096604/v1

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Background: Animals are very important for plant seed dispersal in light of global change. The study of seeds transport on the exterior of animals’ bodies (epizoochory) is particularly challenging due to the difficulty to describe and quantify the process of seed release. Shaking movement of fur has been shown to cause seed release and can hence determine seed retention times, necessary to assess dispersal distances. Such information on shaking movements is, however, only available on the neck of animals thanks to wildlife collars containing accelerometers.

Methods: In order to quantify shaking forces on the main body of mammals where most plant seeds attach, and to predict this body acceleration from (known) neck acceleration, we simultaneously measured acceleration at the neck, the breast and the upper hind leg of mammals spanning a large range of body masses. We quantify shaking strength as the 95%-quantile of the resultant acceleration (of all measured values in data subsections of five seconds).

Results: While, compared to the neck, acceleration had a similar range of values at the breast and was considerably higher at the leg, neck acceleration in combination with animal body mass proved to be a very valuable predictor: 81 and 63% of variation in breast and leg acceleration could be explained, respectively.

Conclusions: These results enable the use of available acceleration data from animals’ necks to predict body acceleration for mammals with known body mass. In combination with i) further lab experiments to determine seed release in dependence of fur acceleration for specific seed-fur combinations, and ii) animal movement data, this information can be used to predict probability and spatial distributions of seed dispersal. Besides seed dispersal, we believe that the ability to predict body acceleration on mammals should also benefit other ecological fields like parasitology.

body acceleration

contact separation force

epizoochorous seed dispersal

wildlife collar

mammals

Animals are among the most important vectors for long distance dispersal of plant seeds (e.g. Howe & Smallwood, 1982; Nathan et al., 2008, Fricke et al., 2022). Besides contributing to seed dispersal via endozoochory (i.e. passage of seeds through their digestive system), animals (notably mammals) also transport a large number of seeds of many plant species via attachment to the exterior of the body. This mechanism is called epizoochorous seed dispersal (Fischer et al., 1996; Benthien et al., 2016). While our mechanistic understanding of endozoochorous dispersal has been increased thanks to studies of animal feeding behaviour, animal movement and gut retention times (e.g. Picard et al., 2016, González-Varo et al., 2017, Lepková et al., 2018, Fricke et al., 2022), we still have comparatively little knowledge about the process of epizoochory. The major challenge in this respect is the description and quantification of the process of seed detachment. As a consequence, we lack information on the retention time of seeds in animal fur, a crucial parameter for quantifying seed dispersal and dispersal distances. This study aims at quantifying and explaining shaking movements on different parts of mammal bodies, relevant for seed detachment, and thus, identifies the link between the process of seed detachment to available collar data, frequently recorded on wild animals.

The three phases of seed dispersal are initiation (for epizoochory seed attachment to an animal body), transport (movement of the animal at a certain velocity) and termination (seed detachment) (Nathan et al., 2008). Seed attachment can be studied by moving animal furs through/along vegetation (Fischer et al., 1996) and by combing furs of wild animals (e.g. Heinken et al., 2002). Thanks to recent advances in tracking technologies and modelling, our understanding of animal movement, and hence, potential seed transport, has increased (e.g. Jonson et al., 2008, Gurarie et al., 2017, MacCurdy et al., 2009, Wright et al., 2020) and plenty of movement data are available, e.g. on Movebank (movebank.org). The process of seed detachment poses, however, a greater challenge since it cannot be easily observed and studied in the wild.

Due to the difficulty to investigate seed detachment in the wild, previous studies have chosen an experimental approach. They found that for this process the interplay of the animal surface (e.g. fur properties) and seed morphology (e.g. appendages like hooks) are crucial factors, determining the so-called contact separation force that is needed to detach the seed from the fur (Gorb & Gorb, 2002, Couvreur et al., 2004; Tackenberg et al., 2006; de Pablos & Peco, 2007). On an animal body forces to release seeds can be created via shaking of the body, causing acceleration of the fur (F = m x a), for example during animal movement. It has been shown that seed release can be induced by fur acceleration (Römermann et al., 2005) and that the strength of fur shaking determines the proportion of seeds released (see Figure S1).

Accelerometers, that measure acceleration at high frequency can quantify the strength of acceleration which directly translates into the force that seeds experience in animal furs. To transfer the above-mentioned insights on seed detachment in dependence of force/acceleration into natural systems it is therefore essential to measure acceleration over time on wild animals. Thanks to rapid advances in GPS telemetry in the last decade (e.g. Cagnacci et al., 2010, Hallworth & Marra, 2015) more and more studies are conducted on animal movement, often including measures of acceleration. These data can be used to infer animal activity, energy budgets or even specific behavioural patterns or syndromes (e.g. Brown et al., 2013, Gleiss et al., 2011, Kröschel et al., 2017, Rast et al., 2020). Nowadays many commercially available GPS tracking collars (e.g. Eobs, Vectronic aerospace, Biotrack) are equipped with accelerometers and the recording of acceleration data does not cause much additional effort and costs (apart from battery life and storage space). Still, analysing acceleration data is challenging (e.g. training algorithms to detect behaviour patterns e.g. Giovanetti et al., 2017, Valletta et al., 2017, Chakravarty et al., 2019), which might be one reason for large differences in the number of publications using only GPS data and those using acceleration data in movement ecology. These reasons, together with own experiences in previous projects leads us to believe that considerable amounts of acceleration data are recorded and therefore theoretically available without actually being used and published (yet).

In principle, these acceleration data bear the potential to inform epizoochorous seed dispersal. However, these data are, without exception, recorded at the neck of animals, since the 3D-accelerometers are included in the GPS neck collars used in these studies (e.g. Weegman et al., 2017, Chakravarty et al., 2019). Seeds are to some extent attached at the head and neck but especially at lower parts of the animal torso and the legs (compare Albert et al., 2015, Liehrmann et al., 2018, Petersen & Bruun, 2019). To be able to use available acceleration data from animal necks to infer acceleration at the torso and leg, there is a clear need to identify relationships between acceleration between these different body parts. Besides acceleration at the neck body size can be expected to be an important determinant for acceleration at lower parts of the body, and hence, relevant for seed detachment. Larger animals have a higher muscle mass with larger cross-sectional area, thus more strength, and faster (limb) movements which should result in higher acceleration at their body (compare Calder, 1984, Kilbourne & Hoffman, 2013, Cloyed et al., 2021).

In this study we measured acceleration simultaneously at different body parts of mammals of various body size in order to quantify 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.

For measuring acceleration (three dimensions X, Y, Z) on different sections of the animal body, accelerometers were attached with nylon straps to the neck, the breast and to the hind leg (shank) between knee and heel of the animals, wherever possible (40 individuals of 13 breeds of 8 species, see Table 1, Figure 1). Where necessary, sensors were fixated with adhesive elastic bandages to the upper legs of the animals. They were then left to move for at least five minutes in their enclosures (indoors and outdoors) in the stalls of the agricultural faculty of University of Hohenheim and in Wilhelma Zoological Garden, Stuttgart, in November and December 2018, with the aim of recording a minimum of three time segments of at least five seconds with i) walking-like movement and ii) running movement. If necessary and possible animals were tempted to move by leading them on a leash (camels, horses) or gently chasing them (sheep, goats). 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.

Species	Breed	Indi- viduals	Individuals with leg acceleration measure-ment	Body mass range (kg)	Range of the length of hind leg (cm)	Location	Ground surface
Camel (Camelus ferus)	Bactrian Camel	3	3	580 - 800	94 - 103	Wilhelma Zoological Garden	Soil (outdoors)
Cow (Bos Taurus)	Holstein-Friesian Cattle	3	3	715 -867	97 – 100	Hohenheim (“Meiereihof”)	Concrete, plastic (indoors)
Cow (Bos taurus)	Jersey Cattle	3	1	169 - 465	76 – 83	Hohenheim (“Meiereihof”)	Concrete (outdoors)
Donkey (Equus asinus)	Poitou	3	3	470 - 530	83 - 90	Wilhelma Zoological Garden	Soil (outdoors)
Goat (Capra aegagrus)	Bunte Deutsche Edelziege	5	4	39 - 86	43 – 53	Hohenheim (“Meiereihof”)	Concrete with straw cover (indoors)
Goat (Capra aegagrus)	West African Dwarf	2	1	26 - 28	30 – 34	Wilhelma Zoological Garden	Concrete (outdoors)
Horse (Equus ferus caballus)	Dülmener Horse	2	2	246 - 293	74 – 77	Wilhelma Zoological Garden	Soil (outdoors)
Horse (Equus ferus caballus)	Shetland Pony	2	2	157 - 178	57 - 64	Wilhelma Zoological Garden	Soil (outdoors)
Mule (Equus mulus)	-	1	1	292	72	Wilhelma Zoological Garden	Soil (outdoors)
Sheep (Ovis aries)	Cameroon Sheep	4	4	27.5 - 50	38 – 43	Wilhelma Zoological Garden	Concrete (outdoors)
Sheep (Ovis aries)	Merino	5	3	76 – 122	48 – 60	Hohenheim (“Meiereihof”)	Concrete with straw cover (indoors), concrete and soil (outdoors)
Pig (Sus scrofa domesticus)	Deutsche Landrasse x Pietrain	5	5	32 – 275	28 – 52	Hohenheim („Unterer Lindehof“)	Concrete (indoors)
Pig (Sus scrofa domesticus)	Kunekune	2	0	90 - 120	26 - 28	Wilhelma Zoological Garden	Soil (outdoors)

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 sec.) followed by a gap of approx. 1.4 sec. Positions of the different sensors were alternated, while a large bulky e-obs sensor (tag 1653) was only used with four individuals, and a smaller and lighter 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 compared to the MSR sensors. Before use all four sensors were tested for comparability by simultaneously measuring the movement of a laboratory shaker, which demonstrated negligible variation. All measurements were done at 100Hz (each of the three axes 33.3 Hz). Temporal synchronization of all collars/sensors was achieved by starting a 0.1 second resolution stop watch at the same time as manually shaking all three sensors for approx. 15 sec. This “extreme acceleration event” could later easily be recognized in the beginning of the data series of all sensors and defined time point 0 for the specific measurement session. The time of the stop watch was used as reference for any observation during the animal trials that should 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 s ^-²) according to manufacturer instructions and visually checked for synchronism between neck breast and leg. For each animal the sequences were cut into subsections of five seconds on which analyses were performed, i.e. for each dimension X, Y, Z approx. 5 x 33.3 = 167 values for the MSR loggers; and only approx. 3.6 x 33 =119 values of the e-obs sensors due to gaps in the data (1.4 sec. every 4.7 sec. see above). Testing subsections of ten seconds instead of five did not change results notably.

As a measure of maximum strength of shaking we calculated the “resulting acceleration” as the resultant of acceleration in the three individual dimensions. After subtracting 9.81 m/s² to eliminate omnipresent gravitational acceleration, we calculated the 95%-quantile of this resulting acceleration for each five second interval as a measure of maximum acceleration (Figure 2).

To investigate how this shaking strength on the breast and the leg of the animal can be explained by shaking at the neck of the animal and its body mass we used linear mixed-effects models (R, packages lme4 and for model evaluation MuMIn, R Development Core Team, 2020, Bates et al., 2015, Barton, 2019) including the interaction term of neck shaking and body mass. All variables were log-transformed and scaled, to yield scaling relationships (power-law form) with the predictor variables. Data with values of < 0.1 m/s² at the neck were excluded to not regress values close to 0 (during phases of no movement) at the different body parts with each other, but rather only analyse periods of animal activity. The models included random effects of individual nested within species on the slope for neck acceleration, and on the intercept.

Variation of maximum acceleration (measure of shaking strenght, quantified as 95% quantile of resulting acceleration) is considerable between species but also between individuals (Fig. 3, for within-individual variation see Figure S2). Acceleration values as well as variability (within an between species) are much larger at the hind leg than at the neck or breast,. Overall no very clear and consistent effect of body size on body accleration can be observed.

Acceleration at the breast of animals is well explained by acceleration at the neck of animals (marginal R² = 0.81 for fixed effects, Chisq (1) = 31.858, p = 1.658e-08). Body mass negatively affects this relationship (interaction term with neck acceleration: Chisq (1) = 5.7463, p = 0.01652, Fig. 4, left panel). Also, the acceleration at the hind leg is well explained by neck acceleration (marginal R² = 0.63, Chisq (1) = 27.157, p = 1.876e-07), but body mass has only a weak positive effect that was not found to be significant (Chisq (1) = 0.9188, p = 0.3378, Fig. 3, right panel). 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 and leg:

\({A}_{breast}=0.534*{{A}_{neck}}^{1.547}*{mass}^{0.094}*{{A}_{neck}}^{-0.107 * mass}\) (Eq. 1)

\({A}_{leg} =4.222 *{{A}_{neck}}^{0.922}*{mass}^{-0.170}*{{A}_{neck}}^{0.044 * mass}\) (Eq. 2)

Model equations (Equations 1 and 2) and prediction plots (Fig. 4) show that, even though body mass has no significant effect on leg acceleration, it still affects the effect of neck acceleration on body shaking differently, namely decreasing the effect of neck acceleration on the breast but slightly increasing the effect of neck shaking on the leg.

This study examined how to predict acceleration of mammalian body parts at which many plant seeds are attached to the fur. We found that acceleration at an animal’s neck, which is frequently measured and hence, available for many wildlife species, as well as animal body mass are very valuable predictors of acceleration at the torso and legs of mammals. Thus, these two variables can inform epizoochorous seed dispersal, specifically seed retention time on animal bodies, by determining the force acting on seeds during animal movements and eventually causing their release from the fur.

We found that the maximum of acceleration, quantifying the strength of shaking, measured at the neck of moving mammals together with body mass explains 81% of the maximum acceleration at the breast/torso of the animals, and that the additional effect of individual and species (random factors) was less than 10%. Variability of acceleration at the upper hind leg is larger and is somewhat less well predicted by neck acceleration and body mass (only 63%). Notably body mass affected the neck effect, so that the effect of neck acceleration on breast acceleration is weaker for heavier animals. The neck effect on leg acceleration is slightly stronger for heavier animals (however, not significantly). The fact that neck acceleration cannot explain as much variance in leg acceleration as in breast acceleration could be explained by the larger spatial separation – these two body parts are possibly more decoupled from each other. However, different behaviours, walking modes, gaits or movement speeds in the moment of measurement should also more directly affect the limbs of an animal and hence, more strongly cause variation in leg acceleration than in torso or neck acceleration. The fact that individual and species did not explain more variance of leg acceleration than of breast acceleration (both less than 10%) moreover indicates that such behavioural aspects play an important role, especially compared to other species-specific characteristics which are to some degree independent of boy mass, like body composition, geometry, leg length etc.. The weaker effect of neck acceleration on breast acceleration for larger animals could be a result of inertia of the large bulky torso of e.g. camels and cows, and to some degree by body composition – especially camels have a long neck, where head movements can be expected to directly affect torso acceleration less strongly than e.g. in a goat, a sheep or a pig. Even if this effect was not found to be significant, body mass seems to influence the effect of neck acceleration on leg acceleration positively. Even though at first sight this seems to contradict findings of Cloyed et al. (2021), that acceleration of the entire animal should be independent of body size, this appears reasonable: higher movement speeds (Calder, 1984, Cloyed et al., 2021) combined with larger stride lengths (Heglund et al., 1974) and resulting higher maximum limb movement speeds in large animals could be expected to result in turn in higher acceleration of the limbs (in times of high activity, as depicted by neck acceleration).

These results demonstrate that the strength of body acceleration can be well predicted from two rather simple and frequently available variables for mammals: animal body mass, the most frequently used trait in animal ecology (e.g. Calder, 1984, Brown et al., 2004, White et al., 2007), and acceleration measured at the neck of the animals. Acceleration data stemming from animals’ necks can be interpreted to describe the general level of activity of the animals, but they also encode behavioural states (e.g. Gerencsér et al., 2013, Graf et al., 2015, Giovanetti et al., 2017), and modes of movement (Williams et al., 2015). While information on animal body mass is easily available, also plenty of acceleration data, recorded at the neck, have become available to the scientific community, and a certain unknown share of them are still not exhaustively used, presumably because the use of these data for ecological inference is still in the process of being explored (e.g. Giovanetti et al., 2017 Valletta et al., 2017, Chakravarty et al., 2019).

Quantification of acceleration (strength) at the body of mammals is of crucial importance for epizoochorous seed dispersal (compare Römermann et al., 2005). Epizoochorous seed dispersal in animal furs plays a major role for the large-scale dynamics of many plant species. This clearly is the case for plant species with seeds that are adapted to epizoochory (e.g. Römermann et al., 2005, Petersen & Bruun, 2019). However, also numerous vascular plant species without obvious morphological adaptations to epizoochory, such as hooked appendages, are transported in animal furs (Fischer et al., 1996). In fact, ‘non-standard’ dispersal vectors for which a given plant species does not seem to show morphological adaptations, may be of key importance for long-distance seed dispersal (Nathan et al., 2008). For the removal of seeds with strongly attaching appendages (e.g. hooks) intentional shaking, grooming behaviour or rubbing against objects (Liehrmann et al., 2018) are obviously very relevant. Among these at least intentional shaking can still be recorded with acceleration measurements. However, particularly for seeds without appendages body acceleration while walking and running can be expected to be the a very important factor causing seed release. This is also shown by a supplemental experiment in which we placed seeds of three different herbs on a rabbit fur. We found that a considerable share of these seeds (up to 10%) could be released from the fur when it was accelerated to approx. 6 ms^− 2 (maximum acceleration, comparable to the values measured at animal bodies; Figure S1 and Fig. 2).

To explicitly inform epizoochorous seed dispersal, more experiments are needed in which seed release of various seed fur combinations is quantified in dependence of shaking strength of furs (Römermann et al., 2005). Such experiments also need to inform about the time span acceleration of certain intensity needs to be applied to release certain proportion of seeds from the fur. Merging this information with acceleration data of animal bodies, which we show can be predicted from neck measurements, can yield distributions of seed retention times. Linking these to movement speed (distances travelled per time) of these animals would enable us to develop a full model of epizoochorous seed dispersal, analogous to models of endozoochory (Schurr et al., 2009) and simulate seed dispersal kernels.

Besides epizoochorous seed dispersal, we believe that knowing acceleration characteristics across animal bodies can also be relevant for other ecological fields. Not only seeds but also animals such as grasshoppers can be dispersed in fur (Fischer et al., 1996). 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. Acceleration here should become even more relevant causing the need for a larger force to hold onto the animal body, when parasites get heavier while feeding (F = m x a). Such variation in energy expenditure surely will involve effects on individual fitness of the parasites and hosts.

Acceleration data at animal necks’ have to our knowledge not yet been collected to inform seed dispersal of plants. Still, they obviously contain valuable information on seed dispersal and other important ecological processes. With this study we therefore like to motivate the consideration of possibilities to recycle available ecological data in order (compare Hampton et al., 2013) to keep the impact on the environment through ecological field studies and experiments as low as possible. We hope to stimulate creative research by demonstrating the utility of available data for other fields of ecology, and are looking forward to hopefully reading of other, not yet considered applications these results might be relevant for.

F: force (N)

m: mass (kg)

a: acceleration (m s^-2)

A_neck, A_breast, A_leg: acceleration at animals’ necks, breasts and hind legs (m s^-2)

Ethics approval and consent to participate

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

Consent for publication

Not applicable.

Availability of data and materials

All data, specifically measured acceleration of all animals, are published at figshare.com:
https://doi.org/10.6084/m9.figshare.20182100.v1

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable.

Authors' contributions

C.M.B and F.M.S. conceived the idea. C.M.B. organized and lead data collection, analysed the data and wrote the manuscript. L.D and M.C. assisted data collection. F.M.S., L.D. and M.C. assisted analysis. All authors contributed critically to the manuscript drafts and gave final approval for publication.

Acknowledgements

The authors thank many scientists and technicians that helped in data collection: the Biomove team at the Universtiy of Hohenheim, 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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What acceleration data from wildlife collars and animal body mass tell us about seed dispersal

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