| Literature DB >> 31581679 |
Anouschka R Hof1,2,3, Andrew M Allen4, Paul W Bright5.
Abstract
Biodiversity is declining globally, which calls for effective conservation measures. It is, therefore, important to investigate the drivers behind species presence at large spatial scales. The Western European hedgehog (Erinaceus europaeus) is one of the species facing declines in parts of its range. Yet, drivers of Western European hedgehog distribution at large spatial scales remain largely unknown. At local scales, the Eurasian badger (Meles meles), an intraguild predator of the Western European hedgehog, can affect both the abundance and the distribution of the latter. However, the Western European hedgehog and the Eurasian badger have shown to be able to co-exist at a landscape scale. We investigated whether the Eurasian badger may play a role in the likelihood of the presence of the Western European hedgehog throughout England by using two nationwide citizen science surveys. Although habitat-related factors explained more variation in the likelihood of Western European hedgehog presence, our results suggest that Eurasian badger presence negatively impacts the likelihood of Western European hedgehog presence. Intraguild predation may, therefore, be influencing the nationwide distribution of hedgehogs in England, and further research is needed about how changes in badger densities and intensifying agricultural practices that remove shelters like hedgerows may influence hedgehog presence.Entities:
Keywords: citizen science; conservation; displacement; predator-prey interaction; spatial use
Year: 2019 PMID: 31581679 PMCID: PMC6826801 DOI: 10.3390/ani9100759
Source DB: PubMed Journal: Animals (Basel) ISSN: 2076-2615 Impact factor: 2.752
Figure 1Proportion of positive hedgehog sightings according to the ‘HogWatch’ survey of 2005–2006.
Figure 2Proportion of positive badger sightings according to the ‘Living with Mammals’ survey of 2003–2006.
Figure 3The exponential semivariogram model with 8 lags used for kriging the hedgehog data.
Figure 4The Gaussian semivariogram model with 5 lags used for kriging the badger data.
Explanation of the response variables used (LCM: Land Cover Map 2000 and 2007, NSRI: National Soil Resource Institute, LWM: Living with Mammals survey, SEDAC: Socioeconomic Data and Applications Center [see text for explanation]) to study their impact on the relative hedgehog abundance throughout England in 2005–2006. Some land classes from the LCM were not included because they were extremely rare (absent from >90% of grid cells) or were not suitable, such as “Mountain, heath, bog”, “Saltwater”, “Freshwater”, and “Coastal”.
| Variable | Explanation | Source |
|---|---|---|
| Arable land | Proportion of arable and horticultural area | LCM |
| Badger presence | Index of relative badger abundance 2003–2006 | LWM |
| Broadleaf woodland | Proportion of broadleaved woodland | LCM |
| Built-up | Proportion of built-up area (includes target classes of urban and sub-urban) | LCM |
| Coniferous woodland | Proportion of coniferous woodland | LCM |
| Human footprint | Human Influence Index normalized by biome and realm | SEDAC |
| Improved grassland | Proportion of improved grassland | LCM |
| Semi-natural grassland | Proportion of semi-natural grassland (includes target classes of rough, neutral, calcareous, acid grassland, and fen, marsh and swamp) | LCM |
| Soil type | The soil types of England 1: soils with a clay texture, 2: soils with a peaty texture, 3: soils with a sandy texture, 4: soils with a loamy texture and rich in lime, 5: soils with a loamy texture and a low fertility, 6: soils with a loamy texture and a moderate to high fertility | NSRI |
Figure 5Maps showing an index (low: 0, high: 1) of the likelihood of the presence of (a) hedgehogs and (b) badgers throughout England.
Model results of generalised linear modelling (GLM) explaining the likelihood of the presence of hedgehogs. Significant variables were determined using backwards stepwise selection. SE = standard error and p = p-value. The independent contribution of each variable towards the explained variation (R2; total = 0.242) was measured using hierarchical partitioning and VIF is the variance inflation factor.
| Variable | Coefficient | SE |
| R2 | VIF |
|---|---|---|---|---|---|
| Intercept | 0.834 | 0.029 | <0.001 | - | - |
| Badger presence | −0.078 | 0.030 | 0.010 | 0.021 | 1.047 |
| Arable land | 0.090 | 0.036 | 0.012 | 0.073 | 1.671 |
| Built-up | −0.238 | 0.056 | <0.001 | 0.045 | 1.367 |
| Improved grassland | −0.245 | 0.053 | <0.001 | 0.068 | 1.422 |
| Broadleaved woodland | −0.419 | 0.141 | 0.003 | 0.035 | 1.151 |
Top-performing models with ∆BIC < 4 from a multi-model selection consisting of all possible explanatory variables. Shaded areas indicate that the variable was included in the model. All model variables had VIFs < 2. LogL is the log-likelihood, Arable is Arable Horticulture, Badgers is the likelihood of badger presence, BLwood is broadleaved woodland, ImprG is improved grasslands, HFI is human footprint index, and Peat is soils with a peaty texture.
| ∆BIC | LogL | Arable | Badgers | BLwood | Built-Up | ImprGr | HFI | Peat |
|---|---|---|---|---|---|---|---|---|
| 0.00 1 | 223.05 | |||||||
| 0.46 | 219.85 | |||||||
| 0.76 | 219.70 | |||||||
| 0.87 | 216.68 | |||||||
| 1.20 | 225.42 | |||||||
| 1.81 | 222.15 | |||||||
| 2.46 | 224.79 | |||||||
| 2.96 | 218.60 | |||||||
| 2.99 | 221.56 | |||||||
| 3.22 | 221.44 | |||||||
| 3.40 | 218.39 | |||||||
| 3.84 | 221.13 | |||||||
| 3.98 | 227.00 |
1 BIC = −404.54.
Figure 6Results of hierarchical partitioning showing the individual contribution of each variable towards the total explained variation (R2) of the model (R2 = 0.258).