What influences changes in the home
range, resource use and
behaviour for the Capra ibex?

Author

Owen King

1 Introduction:

Many habitats and organisms are suffering from the impacts of global warming, posing an increased risk of extinction for some species, whilst the effects remain largely unknown for others. Often the focus is towards the Polar habitats or Reefs, however little focus is ever given in the media and science to the mountainous regions of the world where animals have evolved to live harmoniously in cold and unforgiving landscapes (Brighenti et al. 2021). However, as the climate changes and the world is warming, many animals who inhabit higher elevation areas are being pushed higher which is incredibly problematic (Aublet et al. 2008). This shift is resulting in a decrease in usable resources, increase in energy expenditure and a need to navigate rougher terrain (Meza‐Joya et al. 2023). Additionally, as these mountainous habitats are already fragmented geographically, the loss of connecting habitats can result in populations becoming isolated, causing loss of gene flow, genetic diversity, and overall fitness of offspring which is especially the case for herbivorous organisms (Meza‐Joya et al. 2023).

The Alps are the most expansive and tallest mountain range in all of Europe, housing thousands of species of flora and fauna, and due to the rugged terrain, many regions are unsuitable for urbanisation, making it a relatively unmodified landscape (see Table 1) (Kellerer-Pirklbauer et al. 2022).

Table 1: Outlining the approximate number of fauna species that inhabit the alps by Order (Tasser et al. 2024).

The Alps are home to many animals that are adapted to fill the niche of high altitudes and low temperatures one such animal being the Alpine ibex (Capra ibex) (Figure 1) (Acevedo and CASSINELLO 2008). This animal almost fell victim to extinction from over hunting however because of one of the most successful recorded reintroduction programmes, this species of ibex is now widespread across the Alps (Anon. 2023). This species holds high importance culturally and ecologically (Brambilla et al. 2020); being a food source for numerous predators and helping maintain the health of alpine meadows and grasslands through grazing and the prevention of shrub encroachment (Veldhuis et al. 2020).

Figure 1: Alpine Ibex in the Alpine mountains, Image sourced from Creative Commons

Due to the remote location of these species and the rugged terrain in which they inhabit it is incredibly hard to track them and obtain meaningful information from these animals which is where predictive, and descriptive modelling can be an extremely beneficial tool for creating and implementing evidence-based conservation actions (Berger-Tal et al. 2022). Spatial models such as Resource Selection Functions (RSFs) and Step Selection Functions (SSFs) help identify key habitat features and movement corridors by calculating preferences for environmental variables under different conditions (Bautista et al. 2021). Home range estimators like Auto Kernal Density Estimation (AKDE) and dynamic Brownian Bridge Movement Model (dBBMM) provide insight into spatial usage (Silva et al. 2021). Meanwhile, Hidden Markov Models (HMMs) offer a way to infer behavioural states from basic movement data such as GPS data, revealing how animals’ activity and behavioural patterns are influenced by environmental stressors like elevation or human disturbance (Klappstein et al. 2023). All of which can help to develop a detailed account of the ibex’s behaviour and how they interact with their environment.

This study aims to develop an understanding of the spatial and behavioural ecology of a population of Alpine ibex in the western region of the Alps using GPS data sourced from MoveBank (Cavailhès 2020). Three main questions will be answered to inform this understanding of the Alpine ibex:

1. What is the ibex’s home range, and does the home range of this animal change seasonally?

2. How are resource use and habitat selection influenced by environmental variables such as elevation, slope, season and land cover?

3. Can movement-related behaviours be identified, and how are these behaviours influenced by environmental variables such as habitat and season?

2 Methods:

The study area spanned over four national parks (Gran Paradiso, Vanoise, Ecrins and Mercantour) which are situated within the eastern region of the Alps. The GPS data spanned over two years, 2018-2019 and tracked 121 individuals with 244,540 overall GPS locations; these figures are after cleaning and refining the data which was sourced from MoveBank (Cavailhès 2020). Due to the majority of individuals having a large number of GPS locations, any individual with less than 250 was cut from the dataset to ensure there is at least a full month of data for each individual when accounting for the 3 - 6 hour GPS fix period. The habitat data was sourced from the European Space Agency (ESA) which has 10 metre resolution and 11 habitat levels, with only one being marine/aquatic, this habitat data has a global classification accuracy of 76.7% (Anon. 2021).

Figure 2: Map of the study area in Europe

Methodology for each analysis:

What is the ibex’s home range, and does the home range of this animal change seasonally?

AKDE was used to establish the home range of the ibex, this method was used due to the large intervals between GPS locations and its ability to adjust for autocorrelation (each location is correlated with the next temporally), meaning it was best fit for the ibex data. In addition to this, literature suggests, this is the most accurate method when estimating home range as it does not assume each GPS point is independant and takes into account the time stamp and location in reference to the previous GPS points (Noonan et al. 2019).

To test for the significant difference between home ranges across seasons, a Kruskal Wallis test and a series of Dunn Bonferoni tests to establish specific differences between seasons.

How are resource use and habitat selection influenced by environmental variables such as elevation, slope, season and land cover?

A Resource Selection Function was used due to large sampling period of 3-6 hours meaning it was the most suitable model for the data set. When random samples were produced for the analysis only 122,330 random points were produced, which was half of the size of the original data set 244,661. This is due to hardware limitations, however 10-100 iterations of the data set would be recommended in future for a more reliable analysis (Boyce et al. 2002). Variables included in this analysis were season, habitat, elevation (metres) and slope (degrees). For this analysis a binomial general linear regression was used to calculate the used against the unused habitat.

Can movement-related behaviours be identified, and how are these behaviours influenced by environmental variables such as habitat and season?

A Hidden Markov Model was produced to identify two states of behaviours, resting/foraging and traveling, this type of analysis was used as it allows the use of abiotic variables such as habitat and season (Patterson et al. 2009). Season and habitat were used to inform the model and to identify which enviromental variables influence behaviour. Elevation and slope were excluded from this model due to drastically decreasing the viability of the test. The model was a two-state model using step length and turning angle (parameters for each were calculated using two analysies). For step length, a Weilbull distribution was used as the ibex step length is skewed left, and a wrapped Cauchy was used which accounts for the three peaks seen in the turning angle of the animals (McClintock and Michelot 2018).

Variables Used in Each Analysis
AKDE RSF HMM
Season ✓ ✓ ✓
Habitat ✓ ✓
Elevation (m) ✓
Slope (degrees) ✓

The table above details each covariate that is included in each analysis. Due to lack of model integrity, not all covariates could be used for HMM or the AKDE.

3 Results:

3.1 Home Range analysis

The AKDE analysis was conducted to establish the ibex’s home range and whether it significantly changed across seasons. The Kruskal Wallis test revealed a significant result (chi-squared = 23.987, df = 3, p-value =< 0.000) indicating a strong significant difference between home ranges across all four seasons.

Crosswalk table outlining the significant difference of home ranges during different seasons

Table 2: The table below shows the significant difference between each season in terms of home range size from the AKDE analysis. This was found using a Kruskal wallis and a series of Dunn tests. Interacting with the arrows below each season will order the significance by least significant or most significant.

The cross walk table (see Table 2) outlines the statistical difference in home range size when comparing between seasons, with Autumn showing a larger home range size, averaging 21.6 metres squared with a large error margin of up to 68 metres squared. It shows that every season is statistically different form Autumn, however Summer is the least statistically different which is expected when considering how large some of the home ranges where in the summer months (see interactive map (#interactive-map) below for more details).

These findings conform with most literature on the home range of the ibex, as they often can be found moving around significantly more in the summer months to find new breeding territory this is also applicable to autumn venturing to find new territory before the higher elevations become too inhospitable due to Winter (Veldhuis et al. 2020) these differences are further highlighted in Figure 3.

3.1.1 Significant difference in home range between seasons

Figure 3: Box plots outlining the difference between home ranges in terms of season in metres squared. Blue representing winter, green representing spring, pink representing summer and orange representing autumn. The black dots are outliers from the normal distribution. Exact values can be seen by hovering cursor over individual box plots.

The larger territory could also be attributed to mating or hyperphagia, by roaming larger areas to find females and better resources to increase their biological fitness. Females would need to have excess energy for breeding, and males for competing for the right to breed; this would occur from December to February (Coloma et al. 2011), which likely contributes to the smaller home ranges seen in the winter season where individuals would likely either occupy or defend a breeding territory which are often small due to competition (Willisch and Neuhaus 2009).

Seasonal Home Range Map for Capra ibex

This interactive map shows the top 4 individuals which showed the largest variance in their home range between seasons.

The majority of home ranges are fairly small throughout every season. This is highlighted in Figure 4.

3.1.2 Distribution of home range values for each season

3.2 Habitat and resource selection (Resource Selection Function)

3.2.1 Habitat selection and influence of choice by enviromental covariates

After conducting the RSF, a mROC was conducted to check the validity of the model; for this model the ROC value was 0.9925 which states it has a predictive power/accuracy of 99 percent, which is unlikely. This is likely due to collinearity seen between slope and elevation; as seen in Figure 7, both variables influence the prediction in an almost identical pattern, however because of how important these variables are to habitat selection in the ibex, they where retained due to their ecological importance.

The ibex showed strong selection behaviour, actively avoided certain habitat features and actively selecting for others; in Figure 5 shows the likelihood of the ibex selecting specific habitats, the further right the error margins are, the more likely they are to select these specific habitats.

Figure 5: This point graph displays the likelihood of an ibex to choose a habitat, the likelihood of an ibex choosing between different elevations, the likelihood of an ibex choosing different areas depending on the slope of the terrain, and whether season has an impact on habitat use. Covariates situated to the left of dashed line indicate that they actively avoid areas and covariates to the right indicate individuals actively seek areas.

The model indicates that they actively select higher elevations, woodlands, grasslands and bare ground/rock. Additionally, it shows that season influences habitat choice which conforms with our previous findings in the AKDE and current literature on what influences the choice of habitat and which habitat types are actively chosen (Viana et al. 2018).

The model also reveals that the ibex actively avoid water, urban areas, snow and ice, and steep slopes, indicating a negative relationship with them all. Being near water is likely very exposing and highly correlated with ice and snow, which would be avoided due to difficulty to limited maneuverability (García-González 2008).

3.2.2 How season influences habitat selection

Season does not have a significant effect on how the habitat is utilised, the only difference being a slight increase in the probability of the ibex utilising bare rock/ground, woodland and grassland habitats (see Figure 6); this finding was expected due to current literature (Viana et al. 2018) and the previous result in figure 5. Although bare rock is not as suitable a habitat, the predation risk is much lower due to it being rougher terrain, which is cohesive with what is known about how the ibex select their habitats (Grignolio et al. 2007).

Figure 7 looks at the probability of using different levels of slope and elevation within the landscape and it can be seen that season does not have a significant effect, however they do show a higher probability of inhabiting areas at higher elevation and a lower probability of inhabiting areas with steeper slopes which indicates a conflicting set of interests for the ibex (Cavailhès 2020), As typically the higher the elevation the steeper the landscape. This is recognised to be the ibex’s biggest threat that climate change presents, as warming temperatures force the ibex to higher elevations where gentler inclines are less common (Aublet et al. 2008).

3.3 Activity analysis (Hidden Markov Model)

To create the Hidden Markov Model, data are needed on both the step length and the turning angle of the ibex, and how this data is distributed. From Figure 8, it can be concluded that the ibex spend much of their time either resting or foraging, which is typical for many herbivorous animals. Same can be said for the turning angle in Figure 9, within three peaks the animal is either moving in circles in either direction or going straight; both the short step length and large turning angles are very typical behaviour for herbivorous animals, known as area-restricted search (Owen-Smith et al. 2010).

3.3.1 Step length distribution

3.3.2 Turning angle distribution

3.3.3 Average step length and turning angle between states

Two states were identified (State 1 = resting/foraging and State 2 = Moving) and significant differences in the behaviour of the animals when in these states were found. The step length was significantly shorter during State 1; however, the turning angle was significantly larger, further supporting the idea that the ibex exhibit the behavioural patterns which align with area restricted search, moving slowly and looping around areas of suitable foraging (Dorfman et al. 2022). During State 2, the longer step length and small turning angle suggests that when these animals were traversing across the landscape, they typically do not go very fast and keep on a central bearing of nearly 0 degrees (North) (see Figures 10 and 11).

3.3.4 What influences the shift from resting/foraging to moving

When ibex are in grassland, woodland and shrublands they are less likely to transition from resting/foraging to moving around, this follows what was found in the resource selection function in Figure 5, which explains why these animals would not be likely to move onto a different habitat if they are actively selecting these habitats (Aublet et al. 2008). Furthermore, there is a high probability of animals transitioning from resting to moving when found in snow, ice and water, which reinforces again what was found in the resource selection function, indicating that these animals are traversing through these habitats to find a habitat that they actively select like grassland, woodlands and shrublands (Acevedo and CASSINELLO 2008) (see Figure 12).

Summer appears to have the expected effect on the transition between resting to traveling, given that this season, along with Autumn, showed the larger home range size indicating that they are travelling to suitable habitat during this time (Aublet et al. 2008). In addition to this, they are less likely to stay in one place for too long because the risk of predation is significantly higher in the summer, especially for wild ungulates such as the ibex (Gazzola et al. 2005). As winter is the breeding period, it is unlikely that they would travel as frequently as they would need to preserve their energy to rear young (females) and compete for the opportunity to mate (males) (Willisch and Neuhaus 2009) (See Figure 13). Additionally they are unlikely to travel in the winter because they need to preserve energy during this time due to the lack of resources available and need to partake in basking behaviours to stay warm (Signer et al. 2011).

3.3.5 What influences the shift from moving to resting/foraging

Similarly to what was seen when the ibex were transitioning from resting to moving in Figure 12, they are less likely to stop moving when they are in snow/ice, bare ground/rock and water which indicates these habitats are unfavourable (Figure 14) which is further supported by the resource selection function previously conducted (Figure 5). Additionally, the animals are more likely to rest and forage in places where resources are more abundant like grassland and woodland which is why they are more likely to transition from State 2 to 1 when in these habitats (Acevedo and CASSINELLO 2008).

The results are almost entirely reflective to the transitioning of States between 1 and 2, apart from an increased likelihood of transitioning between state 2 to state 1 (Figure 15) during the spring periods, indicating that they are searching for food sources during this time where new vegetation growth is likely to occur (Aublet et al. 2008). However, it can be inferred that they likely find these food sources even if its not enough to keep them in one place and prevent them from transitioning from State 1 to State 2, which is why the probability of shifting between states is almost neutral (Signer et al. 2011).

4 Discussion and Conservation recommendations

As key resources and environmental variables have been identified to be favoured by the ibex, it is important to now understand why this is important and how it can inform conservation efforts for all populations of ibex within the Alps.

It is clear from both the HMM (Figures 12 & 14) and the RSF (Figures 6 & 7) that the ibex show a prefernce towards woodlands, grasslands, and areas of higher altitude, which aligns with similar literature exploring resource use of the ibex and similar mountain-dwelling ungulates (Bhattacharya and Sathyakumar 2011). Although the Alpine ibex is no longer endangered and is doing very well, there is evidence to suggest that with climate change they could be facing significant habitat loss and connectivity which could lead to significant genetic degradation, particularly due to the fact that they come from a small population of less than 100 individuals from which the reintroduction programme was sourced in 1911 (Stüwe and Nievergelt 1991). In addition to this, although they are doing well currently, if their numbers begin to reduce due to habitat loss, the loss of other key fauna (such as wolves, lynx and bears) could follow suit due to a heavy reliance on species such as the ibex for food (Gazzola et al. 2005) which could lead to further human-wildlife conflicts as these predators would need to shift their diets to domestic ungulates [Gazzola et al. (2005)](Breitenmoser 1998).

Understanding which habitats are favoured can also aid in developing conservation schemes for developing protected habitats and enhancing or expanding current habitats (Bricca et al. 2024). Developing suitable habitats at higher elevations and refining legislative protections would help to prepare the ibex as climate change takes a hold of the planet forcing many mountainous animals higher (Pepin et al. 2022).

The home ranges of these animals are extensive across the alps and shift seasonally which is expected of animals who breed in a particular season. There is a significant avoidance behaviour for urban areas however other threats outlined by the IUCN consist of humans partaking in recreational activities within protected zones which could cause significant disturbance during periods when ibex may be significantly more vulnerable, December to June (Winter & Spring) (Brambilla and Brivio 2018) (Figure 3). This vulnerability comes from the fact that the home ranges are significantly smaller during these periods due to lack of resources and mating, but disturbance and hunting could cause these animals to exert themselves during a time that they do not have the excess energy to exert (Signer et al. 2011) which highlights the flaws in the legislation that is built to protect animals that rely on the ibex (England 2015). Serious revision should take place and seasonal protection should be put into place at least to prevent disturbance or exploitation of these animals during vulnerable periods, especially as their home range shifts to the extent it likely falls outside of designated protected areas (Brambilla et al. 2020). Lack of protection for a primary food source such as the ibex could result in significant declines in predator species that rely on the ibex which are already facing similar threats from climate change (Breitenmoser 1998).

5 Limitations

Although valuable information was drawn from this study, these analyses possess flaws.

AKDE:

The analysis provided a good understanding of these animals home range, however during certain periods their home range was small especially during the winter and spring. This can pose an issue as AKDE can overestimate the individuals’ actual home ranges Noonan et al. (2019). In addition to this, the AKDE is produced from each individuals’ activity range which could misrepresent their actual home ranges and is likely why some individuals had very large home ranges during summer and autumn periods where the animals roam across the landscape more (Fleming et al. 2015).

RSF:

Although the analysis provided insight into the resources selected by the ibex, some results may be misleading. This is because RSF compares used locations to available ones, yet not all available areas may actually be accessible to the ibex, particularly in rugged or seasonally variable terrain which is often seen in the Alps (Boyce et al. 2002). If an ibex cannot physically reach certain areas due to environmental barriers, treating those areas as available could distort the model’s output. Additionally, the assumption that available points represent unused habitat may not be true, especially considering that not every individual in the Western Alps was GPS-tracked. Areas considered “unused” might in fact be used by unmonitored ibex, leading to an oversimplified or incomplete understanding of habitat selection patterns (Brennan et al. 2012).

HMM:

There was a distinct difference in the behaviours identified during the HMM analysis however it cannot be known truly what these behaviours were. This is reinforced by the fact that the data used had a 3-6 hour sampling period and each state relies on the difference from the previous GPS fix meaning that it only assumes it either does or does not shift between states where there could have been multiple times during that period where they exhibited both states. In addition to this, the model relies on turning angle and step length but if the ibex travel back to the starting location before the next GPS fix this could be mis-categorised as resting whereas the ibex was not (Patterson et al. 2009). There are a lot of assumptions with this model but this type of analysis has proven to be effective in developing a rough understanding of the activity levels of different animals and was used effectively to establish two states of behaviours for the ibex in this study (McClintock et al. 2020).

6 Conclusions

All three of these analyses provided useful insights into this animal’s spatial ecology and recommendations should be taken into consideration when developing further conservation strategies for the ibex. In conclusion, the extent of protection for the ibex is limited and its suitable habitats are shrinking; expanding and increasing the quality of these habitats, as well as futhering legal protections, could drastically improve the chances of this animal surviving the changing climate and aid in the persistence of large predators within the Alpine region.

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