# Movement behaviour of the common frog (Rana temporaria) in an anthropized mountain landscape: integrated step selection analysis of GPS tracking data

Etienne Boncourt, Björn Reineking, Gabriel Chambonnet, Stéphanie Gaucherand

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Etienne Boncourt, Björn Reineking, Gabriel Chambonnet, Stéphanie Gaucherand. Movement behaviour of the common frog (Rana temporaria) in an anthropized mountain landscape: integrated step selection analysis of GPS tracking data. Amphibia-Reptilia, 2024, 45 (1), pp.113-122. 10.1163/15685381-bja10164. hal-04380630

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Movement behaviour of the common frog (*Rana temporaria*) in an anthropized

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mountain landscape: integrated step selection analysis of GPS tracking data

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Type of manuscript:

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Short note

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Authors (names, first names):

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Boncourt, Etienne

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Reineking, Björn

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Chambonnet, Gabriel

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Gaucherand, Stéphanie

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Affiliation (of all authors):

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Univ. Grenoble Alpes, INRAE, LESSEM, F-38402 St-Martin-d'Hères, FR

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Corresponding author:

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Etienne Boncourt etienne.boncourt@gmail.com

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Total number of words (tables, reference list and captions included):

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5227

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Total number of words (captions included, but not reference list):

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3061

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Total number of words (abstract):

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150
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**Abstract**

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Movement is a key aspect of amphibian ecology as the life cycle of many species involves seasonal dispersal and migration. However, there is still a lack of knowledge about how amphibians move across landscapes in relation to landscape structure and resource availability. Modern lightweight GPS loggers and associated statistical tools offer new perspectives to fill this gap. We tracked the movements of 20 adult European common frogs (*Rana temporaria*) in a human-transformed mountain landscape during summer using GPS tags programmed with a constant 4-hour time interval between fixes. Using integrated step-selection analysis, we demonstrate firstly that frogs strongly selected aquatic habitats over open grassland. Second, ski runs have a negative effect on frogs, indicated by longer movement steps. Weather (air temperature and rain) had no effect on frog movements. These results constitute a promising first step towards studying anuran movement in relation to landscape structure at fine spatial and temporal scales.

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**Keywords**

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ski resort, movement ecology, Step Selection Function SSF, mountain ecology, ski run

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Amphibian populations are declining worldwide, mainly due to anthropogenic impacts (Stuart et al., 2004; McCallum, 2007; Wake & Vredenburg, 2008; Womack et al., 2022). This decline has multiple causes, one of which is the low dispersal rate of amphibians, particularly in fragmented landscapes (Allentoft and O'Brien, 2010). Indeed, the ability of amphibians to move across the landscape is crucial to their survival. They move both for their daily activities and for their seasonal migrations. Daily activities involve movements over short spatial scales to forage or to find a daytime refuge (Nathan et al., 2008; Pittman, Osbourn and Semlitsch, 2014). Migration phases involve seasonal
movements between breeding, foraging and overwintering habitats. Understanding
how amphibians move across a landscape is a key component to improving their
conservation (Pittman, Osbourn and Semlitsch, 2014).

Frog movement has been studied for decades, initially by tagging animals (e.g.
Breder, Breder and Redmond, 1923; Martof, 1953), then by VHF tracking since the
1960s (Kays et al., 2015). These techniques allowed the study of animal movement
patterns such as homing (Breder, Breder and Redmond, 1923; Matthews, 2003;
Mazerolle and Desrochers, 2005), home range extent (Martof, 1953) and habitat
selection (Groff et al., 2015; Groff, Calhoun and Loftin, 2016).

Habitat selection by animals is traditionally assessed by comparing point
observation data either to the total proportion of the considered habitat in the study
area or to randomly chosen locations in the area (e.g. Groff, Calhoun and Loftin, 2016).
In recent years, the advent of GPS technology has provided new opportunities for
animal tracking. In particular, the ability to obtain fixes with a constant time interval and
high temporal and spatial resolution (Cagnacci et al., 2010; Hebblewhite and Haydon,
2010; Christensen and Chow-Fraser, 2014; Kays et al., 2015; Latham et al., 2015)
allows the use of new statistical tools such as step selection functions (SSF, Fortin et
al. 2005, Thurfjell, Ciuti and Boyce, 2014). SSF allow the study of habitat selection
considering steps instead of point observations (Thurfjell, Ciuti and Boyce, 2014).
Habitat use and availability are considered at a local scale (Fortin et al. 2005).

Alpine ecosystems are important for biodiversity but are sensitive to human
development, in particular to the development of tourism infrastructure (Strong et al.,
2002; Lassen and Savoia, 2005; Sato, Wood and Lindenmayer, 2013), but also to
global warming, pollution, overgrazing, water abstraction or the introduction of alien
species (Schmeller et al., 2022). Ski resort management alters vegetation, particularly
on ski runs (Kubota and Shimano, 2010), and thus alters animal habitat (Sato, Wood and Lindenmayer, 2013). Despite this, little is known about the potential effects of human winter recreation infrastructure on amphibians (Sato, Wood and Lindenmayer, 2013).

In this study, we used GPS technology to track the movements of amphibians in a ski resort. Our biological model was the European common frog *Rana temporaria* Linnaeus 1758. This species is widespread in Europe, extending beyond the Arctic Circle, and displays several adaptive traits to the mountain environment (Beattie, 1987; Ryser, 1996, Miaud, Guyetant and Elmberg, 1999). It is also known to be sensitive to changes in the landscape matrix (Hitchings and Beebee, 1997; Johansson et al., 2005, Vos et al., 2007, Decout et al., 2012). It is therefore an excellent model species for studying how frogs move through different land covers in a human-modified mountain landscape.

The study area was located on the snow front of the Les Arcs 2000 ski resort in the department of Savoie, France (GPS coordinates: 45.569 N 6.833 E, see Supplementary Figure S1). The elevation ranged from 2050 m to 2300 m a.s.l. The landscape consisted of several features, with lowland wetlands fed by small streams and roads that traversed the landscape. The wetland was a peatland characterised by hygrophilous herbaceous vegetation (e.g. *Carex* sp.). The soil was waterlogged, but there was no pond or other open water. All these elements were embedded in a matrix of grassland. Shrubs and trees were absent due to human intervention.

The fieldwork took place between July and August 2017. We captured 30 frogs in the wetlands at the lowest parts of the study area using landing nets during the day and at night. We fitted them with Gipsy-5 GPS tags from TechnoSmart. As these tags do not have a remote download system, we also fitted them with radio tags (Advanced Telemetry Systems, R1605 series glue-on transmitters) in order to recover the GPS tags (Supplementary Figure S2). The GPS tags were marinized by the manufacturer with heat-shrink tubes. The radio tags were already marinized. We glued the radio tag to the GPS tag and used the technique developed by Groff et al. (2015) to attach them to the frogs with a waistband, as this technique was described as safe for the animals based on tests with tags weighing up to 3.8 g or 9.3% of
body mass. In our study, the mean weight of the frogs was 41.0 ± 7.7 g (median = 39.9 g) so the 4.4 g tags (GPS + VHF tags) represented 10.7% of the mean body mass of the tagged animals, slightly higher than the target of 10%. The tags were programmed to record a fix every 4 hours.

Fieldwork was conducted in summer to monitor daily movements rather than migration events, as this species is known to migrate in spring and autumn. Frogs do not move much during this period (Pittman et al., 2014). To increase the likelihood of observing movement and associated habitat selection, we released the animals at different distances from their capture points, expecting them to return to their initial position, as homing is a well-documented behaviour in amphibians (Breder, Breder and Redmond, 1923; Matthews, 2003; Mazerolle and Desrochers, 2005). Capture-release distances ranged from 21 m to 521 m (supplementary Figure S1, supplementary material S3). We deliberately chose short distances in order not to exceed the movement capacity of this species (for example, the maximum migration distance is approximately 3,000 m, Decout et al., 2012).

We obtained movement data for 20 out of 30 frogs (13 males, 5 females, 2 undetermined). Ten others could not be used (frogs lost tags too quickly, tags malfunctioned, not enough fixes, etc.). We recorded a total of 456 fixes (264 for males, i.e. 20.3 per male; 177 for females, i.e. 35.4 per female; 15 undetermined, i.e. 7.5 fixes per unsexed frog), which corresponds to 22.8 ± 25.4 fixes per individual (median = 12.5) over a period of 11.2 ± 6.3 days (median = 12.2). We assumed that the difference in the number of fixes recorded between males and females was due to chance (the moment the tag detached) and not to any biological difference. We recovered 100% of the tags.

We created a land use raster combining aerial photo interpretation from the 2013 BD ORTHO® database with 0.5 m resolution provided by the Institut Géographique National (IGN – downloadable at http://geoservices.ign.fr/bdortho#telechargementanc) and field observations. We used five land use classes: grassland, wetlands, watercourses, ski runs and roads. We created a watercourse class by applying a 3 m buffer around the watercourses we observed in the field. This class thus included both the watercourses and their banks. We also used a 2016 digital elevation model with 5 m resolution from the BD TOPO® of the IGN (downloadable at https://geoservices.ign.fr/bdtopo#telechargement2016) from which we calculated slope. We used hourly rainfall and temperature data from the Tignes meteorological station (GPS coordinates 45.467 N 6.907 E, altitude 2084 m, distance from the study area ~13 km) provided by MeteoFrance (https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=110&id_rubrique=37).
We used integrated step selection functions (iSSF, Avgar et al., 2016) to analyse how landscape and weather covariates influence common frog movement. iSSF allow for a detailed analysis of the effect of habitat on animal movements. They are an extension of step selection functions (SSF, Fortin et al., 2005) and compare each observed step (straight line between two successive locations with a constant time interval) with randomly generated steps considered as available to the animal. We considered as available all locations reachable in a step according to the distribution of all step lengths in any direction. iSSF then typically use conditional logistic regression approach to estimate the relative probability of selecting a given step compared to another (Manly et al., 2002; Signer, Fieberg and Avgar, 2018). However, conditional regression models that include a random effect on individuals are challenging to fit (Craiu et al., 2011). As conditional logistic regression models are likelihood-equivalent to a Poisson model with stratum-specific fixed intercepts, Muff, Signer and Fieberg (2020) proposed a new frequentist approach to fit iSSF using the glmmTMB package (Brooks et al. 2017). glmmTMB provides faster results than other packages commonly used for fitting mixed-effects SSF like coxme (Therneau, 2015) or mclogit (Elff, 2016) (Muff, Signer and Fieberg, 2020).

We performed the analysis using the amt R package (Signer, Fieberg and Avgar, 2018). We first converted our tracking data from points to steps, considering two consecutive locations of an individual as a step. We resampled with a sampling rate of 4 h with a tolerance of 1 h to remove the irregular fixes. Each step was defined by a geo-referenced start point, a geo-referenced end point and a start date. For each step, we extracted the land use class at the start, the land use class at the end, the air temperature and the slope of the step using the step length and the elevation difference (Fig. 1A.). We used rainfall as a categorical variable (rain / no rain) and temperature as a quantitative variable in degrees Celsius. Using conditional logistic regression, we compared these covariates with those of 200 randomly generated steps for each step (Fig. 1B), as well as their effect on step length. We define ‘habitat selection’ as whether animals preferentially choose (or remain in) one land use class over others (Avgar et al., 2016; Fieberg et al., 2021). We define ‘habitat avoidance’ as an animal tending to move at high speed, i.e. long step length in a land use class it is located in, following Aiello et al. (2023). To account for homing behaviour, we introduced a ‘homing’ variable, i.e. the square distance from the capture location, expressed as $(x - x\_capture)^2 + (y - y\_capture)^2$, where x\_capture and y\_capture are the coordinates of the capture location, and x and y the coordinates of the location under consideration. We included random effects of individuals to account for multiple observations on the same individual using the package glmmTMB (Brooks et al. 2017).
We started the analysis with a full model including all 8 covariates (effect of sex on step length, habitat selection, habitat avoidance, slope, effect of slope on step length, effect of rainfall on step length, effect of air temperature on step length and homing). We performed model selection by removing covariates one by one on the basis of the best Akaike's Information Criterion (AIC) gain and keeping the variables whose effect was significant on the basis of 95% confidence intervals. If a variable showed a significant interaction effect (e.g. step length x slope), we kept the base variable in the model in addition to the interaction following Brooks et al. (2017). We treated the homing variable differently, keeping it in the first models as part of the experimental design. We tested the effect of removing it at the end of the selection process, though, for illustrative purposes. Following Burnham and Anderson (2004) and Mazerolle (2006), we considered the model with the lowest AIC as the best supporting model. Finally, for easier interpretation we also calculated the Akaike weights to represent the relative likelihood of each model.

Males moved significantly greater distances between steps than females (mean step length: 7.5 m versus 3.8 m, Wilcoxon test with *P* < 1e-9).

The best performing model including homing (as part of the experimental design) is the model including habitat avoidance, habitat selection, slope and the interaction of step length and slope (model 'no sex, no weather', Table 1). The effects of sex, air temperature and rain were not included. The homing effect was also not significant and did not appear in the best model ('No sex, no weather, no homing', Table 1). Three models had an Akaike weight >0.10. The model estimates and confidence intervals were very similar from one model to another, making interpretation easier.

## Slope

A significantly negative regression coefficient (-0.152 [-0.274, -0.031] in the best performing model) for the interaction of slope and step length ('slope x step length' covariate) was included in all models (Table 1). This means that in our experiment the frogs moved more slowly in the steep areas than in the low slope areas. Note that we
kept the 'slope' covariate as a main effect in all models that included the 'slope x step length' interaction, as it is recommended to keep all covariates as main effects that are included as interactions (Brooks et al., 2017).

### 189 Habitat selection

The regression coefficient for each habitat represents selection relative to grassland (which is considered the default habitat). In each model, watercourses were more attractive than any other land cover (Table 1). The effects of wetlands and ski runs were not significant, and roads did not appear in the models due to lack of data (we did not have registered locations on roads).

### 195 Habitat avoidance

Habitat avoidance was included in each model (Table 1). Ski runs were significantly (estimate = 0.686 [0.168, 1.204] in the best performing model) more avoided than grassland (i.e. frogs located on ski runs tended to move faster). Wetlands and watercourses did not differ from grassland in terms of frog avoidance.

To our knowledge, no such study using high-resolution tracking at fixed time intervals has been conducted on frogs yet. Nevertheless, the study of amphibian movements at small temporal and spatial scales could be valuable for better understanding their behaviour and scaling from the individual to the population scale (Pittman, Osbourn and Semlitsch, 2014). A possible reason for the lack of such studies may be the relatively large weight of GPS tags compared to the average weight of an amphibian (Thomas, Holland and Minot, 2011). Only recently has the miniaturization of GPS technology reached a point where it is available for amphibian studies.

Our results confirmed some preferences in the way Common frogs move through different land covers. For example, we have demonstrated that the frogs tend
210 to be attracted to watercourses in summer. This is in line with studies showing a
211 preference for aquatic summer habitats in the mountains (Vences et al., 2000;
212 Cogălniceanu et al., 2012). The integrated step selection function allowed us to explore
213 the effect of various covariates on movement speed as well as resource selection,
214 providing a more complete picture than a classical resource selection model. In
215 particular, we found that frogs were able to move faster when placed on a presumably
216 unsuitable land use (ski runs) than when placed on a more suitable habitat such as a
217 wetland. We interpret this as a repulsive effect of unsuitable habitats.

218 The choice of method for attaching transmitters to amphibians is always
219 challenging because of the high risk of injury due to the softness of their skin (Bull,
220 2000; Muths, 2003; Groff et al., 2015). With the type of harness we chose based on the
221 literature, we experienced a 100% injury rate (in particular, we observed traces of skin
222 lacerations), which is much higher than that obtained with lighter radio transmitters
223 (Groff et al., 2015). Therefore, while the use of GPS transmitters for frog movement
224 studies is promising, improved techniques for attaching external transmitters to animals
225 are needed to prevent animal injury. Further research is needed to improve the
226 attachment system, for example by testing harnesses higher on the body, until even
227 lighter GPS tags become available.

228 The behaviour of the Common frog changes considerably during the year
229 (Miaud, Guyetant and Elmberg, 1999). This study was only conducted during the
230 summer when adults are mainly foraging and resting. The sedentary nature of the
231 species in summer, coupled with the short battery life, led us to relocate the frogs for
232 the purposes of the experiment. As a result, the observed movements and space use
233 may differ from natural movements of the species at this time of year. Summer
234 movements represent only a small part of the frogs' movements: they also move during
235 juvenile dispersal and adult migrations (Pittman, Osbourn and Semlitsch, 2014). A
promising extension of this work would be to replicate it during one of these phases.

They are relatively short, and a battery life of just a few days or weeks might be
sufficient to study these events.

Our study contributes to the knowledge of the movements of the Common frog
through different land cover at very fine temporal and spatial scales. Technological
advances will provide new opportunities in the future to track smaller animals for longer
periods of time. This will allow us to better understand the movement of small animals
across the landscape and the impact of human infrastructure on these movements. Our
results are relevant from a conservation perspective in human-transformed mountain
landscapes.

## Acknowledgements

This study was funded by the Agence de l'eau Rhône-Méditerranée-Corse (French
water agency). The authors would also like to thank Claude Miaud for his valuable
advice and the Les Arcs ski resort for its support and data.

The captures and, more generally, the entire experiment were carried out in
compliance with the French laws on protected species. The Prefecture (administrative
centre) of the Savoie department authorized the experiment in May 2017.

## Data availability

The tracking data are available via MoveBank ("Common frog - Rana temporaria - Les
Arcs, Savoie, France", ID 2630735956, managed by EB).
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Figure1. Frog movement modelling. Considering a constant time interval (4 hours), a step was defined by a start point, an end point and a start date T. For each step, we recorded the land use class at the start, the land use class at the end, slope and weather covariates (air temperature, rainfall) (A). Using conditional logistic regression, we compared each observed steps to 50 randomly generated steps starting at the same georeferenced point (B), with an exponential step length and uniform turning angle distribution.

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Table 1. Results of the models analysed. For each covariate, the coefficient estimate, standard error and 95 % confidence intervals (95 % CI). The differences in AIC with the best performing model are shown as ΔAIC, i.e. as the Akaike weight. Significant results at 95% are shown in bold.

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<table><caption>441 Table 1.</caption><thead><tr><th>Model</th><th>ΔAIC</th><th>Akaike weight</th><th>Coefficients</th><th>step length x female</th><th>step length x male</th><th>habitat selection: ski run</th><th>habitat selection: wetland</th><th>habitat selection: watercourse</th><th>habitat avoidance: ski run</th><th>habitat avoidance: wetland</th><th>habitat avoidance: watercourse</th><th>step length x slope</th><th>slope</th><th>step length x rain</th><th>step length x temperature</th><th>homing</th></tr></thead><tbody><tr><td rowspan="3">Full</td><td rowspan="3">13.56</td><td rowspan="3">0.00</td><td>Estimate</td><td>-0.247</td><td>0.083</td><td>0.004</td><td>0.512</td><td><strong>0.953</strong></td><td><strong>0.664</strong></td><td>-0.018</td><td>-0.035</td><td><strong>-0.149</strong></td><td>0.104</td><td>0.027</td><td>0.006</td><td>-1.012</td></tr><tr><td>Standard error</td><td>0.357</td><td>0.155</td><td>0.570</td><td>0.316</td><td><strong>0.271</strong></td><td><strong>0.266</strong></td><td>0.219</td><td>0.263</td><td><strong>0.062</strong></td><td>0.087</td><td>0.182</td><td>0.059</td><td>1.391</td></tr><tr><td>95 % CI</td><td>[-0.948, 0.454]</td><td>[-0.221, 0.387]</td><td>[-1.114, 1.122]</td><td>[-0.108, 1.132]</td><td><strong>[0.421, 1.485]</strong></td><td><strong>[0.142, 1.186]</strong></td><td>[-0.446, 0.411]</td><td>[-0.550, 0.479]</td><td><strong>[-0.271, -0.0263]</strong></td><td>[-0.0670, 0.274]</td><td>[-0.330, 0.384]</td><td>[-1.093, 0.122]</td><td>[-3.738, 1.715]</td></tr><tr><td rowspan="3">No sex</td><td rowspan="3">11.35</td><td rowspan="3">0.00</td><td>Estimate</td><td>-</td><td>-</td><td>0.019</td><td>0.526</td><td><strong>0.962</strong></td><td><strong>0.671</strong></td><td>-0.099</td><td>-0.153</td><td><strong>-0.151</strong></td><td>0.104</td><td>0.030</td><td>0.009</td><td>-0.987</td></tr><tr><td>Standard error</td><td>-</td><td>-</td><td>0.570</td><td>0.317</td><td><strong>0.272</strong></td><td><strong>0.266</strong></td><td>0.190</td><td>0.255</td><td><strong>0.062</strong></td><td>0.087</td><td>0.178</td><td>0.060</td><td>1.327</td></tr><tr><td>95 % CI</td><td>-</td><td>-</td><td>[-1.098, 1.136]</td><td>[-0.096, 1.148]</td><td><strong>[0.429, 1.495]</strong></td><td><strong>[0.1496, 1.193]</strong></td><td>[-0.473, 0.275]</td><td>[-0.651, 0.348]</td><td><strong>[-0.273, -0.028]</strong></td><td>[-0.066, 0.275]</td><td>[-0.318, 0.378]</td><td>[-0.107, 0.126]</td><td>[-3.587, 1.614]</td></tr><tr><td rowspan="3">No temperature</td><td rowspan="3">9.57</td><td rowspan="3">0.01</td><td>Estimate</td><td>-0.250</td><td>0.0820</td><td>0.004</td><td>0.511</td><td><strong>0.952</strong></td><td><strong>0.670</strong></td><td>-0.014</td><td>-0.035</td><td><strong>-0.149</strong></td><td>0.104</td><td>0.026</td><td>-</td><td>-1.009</td></tr><tr><td>Standard error</td><td>0.358</td><td>0.155</td><td>0.570</td><td>0.316</td><td><strong>0.271</strong></td><td><strong>0.259</strong></td><td>0.218</td><td>0.263</td><td><strong>0.062</strong></td><td>0.087</td><td>0.182</td><td>-</td><td>1.395</td></tr><tr><td>95 % CI</td><td>[-0.952, 0.452]</td><td>[-0.222, 0.386]</td><td>[-1.114, 1.122]</td><td>[-0.109, 1.131]</td><td><strong>[0.421, 1.484]</strong></td><td><strong>[0.162, 1.179]</strong></td><td>[-0.441, 0.412]</td><td>[-0.551, 0.481]</td><td><strong>[-0.271, -2.629]</strong></td><td>[-0.067, 0.274]</td><td>[-0.331, 0.383]</td><td>-</td><td>[-3.743, 1.724]</td></tr><tr><td rowspan="3">No weather</td><td rowspan="3">5.59</td><td rowspan="3">0.06</td><td>Estimate</td><td>-0.240</td><td>0.088</td><td>0.004</td><td>0.511</td><td><strong>0.952</strong></td><td><strong>0.668</strong></td><td>-0.020</td><td>-0.038</td><td><strong>-0.149</strong></td><td>0.104</td><td>-</td><td>-</td><td>-1.041</td></tr><tr><td>Standard error</td><td>0.348</td><td>0.148</td><td>0.570</td><td>0.316</td><td><strong>0.271</strong></td><td><strong>0.259</strong></td><td>0.209</td><td>0.261</td><td><strong>0.06</strong></td><td>0.087</td><td>-</td><td>-</td><td>1.382</td></tr><tr><td>95 % CI</td><td>[-0.923, 0.442]</td><td>[-0.201, 0.377]</td><td>[-1.113, 1.121]</td><td>[-0.109, 1.131]</td><td><strong>[0.420, 1.483]</strong></td><td><strong>[0.160, 1.176]</strong></td><td>[-0.429, 0.388]</td><td>[-0.549, 0.473]</td><td><strong>[-0.272, -0.027]</strong></td><td>[-0.067, 0.275]</td><td>-</td><td>-</td><td>[-3.749, 1.666]</td></tr><tr><td rowspan="3">No sex, no weather</td><td rowspan="3">3.39</td><td rowspan="3">0.11</td><td>Estimate</td><td>-</td><td>-</td><td>0.021</td><td>0.524</td><td><strong>0.960</strong></td><td><strong>0.679</strong></td><td>-0.100</td><td>-0.153</td><td><strong>-0.151</strong></td><td>0.105</td><td>-</td><td>-</td><td>-1.009</td></tr><tr><td>Standard error</td><td>-</td><td>-</td><td>0.570</td><td>0.317</td><td><strong>0.272</strong></td><td><strong>0.260</strong></td><td>0.187</td><td>0.255</td><td><strong>0.062</strong></td><td>0.087</td><td>-</td><td>-</td><td>1.319</td></tr><tr><td>95 % CI</td><td>-</td><td>-</td><td>[-1.096, 1.137]</td><td>[-0.097, 1.145]</td><td><strong>[0.428, 1.493]</strong></td><td><strong>[0.170, 1.189]</strong></td><td>[-0.466, 0.267]</td><td>[-0.653, 0.346]</td><td><strong>[-0.273, -0.028]</strong></td><td>[-0.066, 0.275]</td><td>-</td><td>-</td><td>[-3.595, 1.576]</td></tr><tr><td rowspan="3">No weather, no homing</td><td rowspan="3">2.15</td><td rowspan="3">0.21</td><td>Estimate</td><td>-0.305</td><td>0.074</td><td>-0.011</td><td>0.497</td><td><strong>0.949</strong></td><td><strong>0.689</strong></td><td>0.012</td><td>-0.001</td><td><strong>-0.147</strong></td><td>0.100</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Standard error</td><td>0.494</td><td>0.189</td><td>0.570</td><td>0.316</td><td><strong>0.271</strong></td><td><strong>0.27</strong></td><td>0.304</td><td>0.327</td><td><strong>0.063</strong></td><td>0.087</td><td>-</td><td>-</td><td>-</td></tr><tr><td>95 % CI</td><td>[-1.273, 0.663]</td><td>[-2.970, 0.446]</td><td>[-1.127, 1.106]</td><td>[-0.122, 1.116]</td><td><strong>[0.417, 1.482]</strong></td><td><strong>[0.154, 1.225]</strong></td><td>[-0.584, 0.608]</td><td>[-0.643, 0.641]</td><td><strong>[-0.270, -2.338]</strong></td><td>[-0.071, 0.271]</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">No sex, no weather, no homing</td><td rowspan="3">0</td><td rowspan="3">0.63</td><td>Estimate</td><td>-</td><td>-</td><td>-0.006</td><td>0.517</td><td><strong>0.956</strong></td><td><strong>0.686</strong></td><td>-0.108</td><td>-0.161</td><td><strong>-0.152</strong></td><td>0.103</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Standard error</td><td>-</td><td>-</td><td>0.568</td><td>0.317</td><td><strong>0.272</strong></td><td><strong>0.264</strong></td><td>0.200</td><td>0.253</td><td><strong>0.062</strong></td><td>0.087</td><td>-</td><td>-</td><td>-</td></tr><tr><td>95 % CI</td><td>-</td><td>-</td><td>[-1.119, 1.106]</td><td>[-0.104, 1.138]</td><td><strong>[0.422, 1.489]</strong></td><td><strong>[0.168, 1.204]</strong></td><td>[-0.501, 0.285]</td><td>[-0.657, 0.334]</td><td><strong>[-0.274, -0.031]</strong></td><td>[-0.067, 0.273]</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>