Spatiotemporal and Individual Patterns of Domestic Cat (Felis catus) Hunting Behaviour in France
Abstract
:Simple Summary
Abstract
1. Introduction
- Following Thomas et al. [28] in the UK, we predict that the number of prey brought home by domestic cats will be higher during spring and summer coinciding with prey breeding seasons in temperate areas than during the rest of the year.
- Weather conditions strongly influence small mammals [29,30,31], birds [32,33], and lacertid [34] activity in Europe; rainfall positively influences small mammal activity but negatively influences the activity of birds and lacertids. Thus, we predict a higher number of small mammals brought home by cats in regions with a higher relative rainfall, while the number of birds and lacertids brought home by cats in such localities will be lower.
- Biogeographic factors determine prey species’ ecological range [35,36]. Accordingly, we predict that the number of individuals of endothermic prey (i.e., mammals and birds) brought home by cats will increase from the southeast to the northwest while the opposite pattern will be true for ectothermic prey (i.e., lacertids) brought home by cats.
2. Materials and Methods
2.1. Preys Brought Home
2.2. Spatial Variables
2.3. Statistical Analysis
3. Results
3.1. Prey Species Brought Home by Cats
3.2. Seasonality of Prey Brought Home by Cats
3.3. Climatic and Geographic Effects on Prey Brought Home by Cats
3.4. Anthropogenic Impacts on Prey Brought Home by Cats
3.5. Individual Factors Influencing Prey Brought Home by Cats
4. Discussion
4.1. Prey Species Brought Home by Cats
4.2. Seasonality of Prey Brought Home by Cats
4.3. Climatic and Geographic Effects on Prey Brought Home by Cats
4.4. Anthropogenic Pressure Influence on Prey Returned Home
4.5. Individual Factors Influencing Prey Brought Home by Cats
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Predictor | Estimate (SE) | Statistic | p-Value * |
---|---|---|---|---|
Soricids R2adj = 0.48 Pearson estimate = 0.66 Deviance explained = 53.6% | Intercept | −2.78 (0.24) | z = −11.52 | 0.0001 |
Month | Edf = 5.33 | X2 = 492.31 | 0.01 | |
Rainfall | Edf = 1.05 | X2 = 3.97 | 0.05 | |
Latitude × Longitude | Edf = 5.37 | X2 = 45.65 | 0.0001 | |
HFI | Edf = 3.12 | X2 = 4.94 | 0.14 | |
Age (Female) | Edf = 4.21 | X2 = 172.49 | 0.0001 | |
Age (Male) | Edf = 3.45 | X2 = 86.96 | 0.0001 | |
CatID | Edf = 1154.40 | X2 = 8888.47 | 0.001 | |
Town | Edf = 0.28 | X2 = 2.53 | 0.74 | |
Year | Edf = 6.67 | X2 = 827.85 | 0.01 | |
Cricetids R2adj = 0.41 Pearson estimate = 0.76 Deviance explained = 57.1% | Intercept | −2.16 (0.13) | z = −17.19 | 0.0001 |
Month | Edf = 4.56 | X2 = 571.14 | 0.05 | |
Rainfall | Edf = 3.12 | X2 = 13.15 | 0.01 | |
Latitude × Longitude | Edf = 12.41 | X2 = 82.82 | 0.0001 | |
HFI | Edf = 3.40 | X2 = 163.78 | 0.0001 | |
Age (Female) | Edf = 7.50 | X2 = 72.13 | 0.0001 | |
Age (Male) | Edf = 1.00 | X2 = 2.38 | 0.12 | |
CatID | Edf = 1309.41 | X2 = 16,494.29 | 0.01 | |
Town | Edf = 0.60 | X2 = 90.78 | 0.85 | |
Year | Edf = 5.79 | X2 = 544.12 | 0.82 | |
Murids R2adj = 0.37 Pearson estimate = 1.16 Deviance explained = 47.2% | Intercept | −0.65 (0.05) | z = −14.14 | 0.0001 |
Month | Edf = 6.63 | X2 = 209.70 | 0.0001 | |
Rainfall | Edf = 1.83 | X2 = 1.63 | 0.52 | |
Latitude × Longitude | Edf = 1.30 | X2 = 49.32 | 0.0001 | |
HFI | Edf = 2.84 | X2 = 39.99 | 0.0001 | |
Age (Female) | Edf = 3.72 | X2 = 10.44 | 0.1 | |
Age (Male) | Edf = 4.73 | X2 = 30.14 | 0.0001 | |
CatID | Edf = 1828 | X2 = 51,789.30 | 0.79 | |
Town | Edf = 0.00 | X2 = 0.00 | 0.99 | |
Year | Edf = 4.97 | X2 = 41.94 | 0.05 | |
Passeriformes R2adj = 0.18 Pearson estimate = 0.88 Deviance explained = 24.7% | Intercept | −1.16 (0.04) | z = −31.89 | 0.0001 |
Month | Edf = 6.91 | X2 = 251.62 | 0.0001 | |
Rainfall | Edf = 1.00 | X2 = 1.69 | 0.19 | |
Latitude × Longitude | Edf = 5.62 | X2 = 11.61 | 0.1 | |
HFI | Edf = 4.52 | X2 = 18.14 | 0.01 | |
Age (Female) | Edf = 4.90 | X2 = 46.01 | 0.0001 | |
Age (Male) | Edf = 2.71 | X2 = 56.38 | 0.0001 | |
CatID | Edf = 1045.30 | X2 = 6135.86 | 0.96 | |
Town | Edf = 0.20 | X2 = 0.57 | 0.77 | |
Year | Edf = 0.80 | X2 = 1.10 | 0.91 | |
Lacertilians R2adj = 0.46 Pearson estimate = 0.58 Deviance explained = 55.7% | Intercept | −3.06 (0.08) | z = −37.57 | 0.0001 |
Month | Edf = 7.61 | X2 = 785.08 | 0.0001 | |
Rainfall | Edf = 2.79 | X2 = 3.59 | 0.31 | |
Latitude × Longitude | Edf = 1.86 | X2 = 297.10 | 0.0001 | |
HFI | Edf = 1.00 | X2 = 11.18 | 0.0001 | |
Age (Female) | Edf = 3.14 | X2 = 29.88 | 0.0001 | |
Age (Male) | Edf = 1.00 | X2 = 23.71 | 0.0001 | |
CatID | Edf = 949.90 | X2 = 4953.17 | 0.001 | |
Town | Edf = 0.00 | X2 = 0.00 | 0.95 | |
Year | Edf = 3.56 | X2 = 8.90 | 0.06 |
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Castañeda, I.; Forin-Wiart, M.-A.; Pisanu, B.; de Bouillane de Lacoste, N. Spatiotemporal and Individual Patterns of Domestic Cat (Felis catus) Hunting Behaviour in France. Animals 2023, 13, 3507. https://doi.org/10.3390/ani13223507
Castañeda I, Forin-Wiart M-A, Pisanu B, de Bouillane de Lacoste N. Spatiotemporal and Individual Patterns of Domestic Cat (Felis catus) Hunting Behaviour in France. Animals. 2023; 13(22):3507. https://doi.org/10.3390/ani13223507
Chicago/Turabian StyleCastañeda, Irene, Marie-Amélie Forin-Wiart, Benoît Pisanu, and Nathalie de Bouillane de Lacoste. 2023. "Spatiotemporal and Individual Patterns of Domestic Cat (Felis catus) Hunting Behaviour in France" Animals 13, no. 22: 3507. https://doi.org/10.3390/ani13223507
APA StyleCastañeda, I., Forin-Wiart, M. -A., Pisanu, B., & de Bouillane de Lacoste, N. (2023). Spatiotemporal and Individual Patterns of Domestic Cat (Felis catus) Hunting Behaviour in France. Animals, 13(22), 3507. https://doi.org/10.3390/ani13223507