Green treasures: Investigating the biodiversity potential of equine yards through the presence and quality of landscape features in the Netherlands

private owners. However, there are five times as many private yard owners, indicating these types of owners, too, have access to a considerable amount of extensively used land. Moreover, there is great diversity in yard design, which is not surprising considering the wide variety of equestrian properties After all, the way in which horses are kept is likely to vary depending on the primary purpose of the equine yard, ranging from sport and recreational activities, to coaching and therapy, breeding and trade and, on occasion, for the production of meat or milk or as working animals [20, 36, 37]. Such differences in yard design may be one of the key drivers to promoting biodiversity as the number of LFs explains signification variation in PPC. In particular, smaller yards with a greater number of LFs have greater PPC and land- scape complexity. At the same time, design parameters to enhance biodiversity likely require complex considerations such as taking the purpose of the property into account and balancing yard size and the number of LFs. It could be argued, therefore, that much of the diversity observed is likely the result of the constraints and priorities of a yard’s primary purpose [38]. In order to integrate ecosystem services and stimulate biodiversity in equine yards, particular attention should be paid to the interaction between land utility and ecological processes [13], as well as how management and husbandry techniques may impact the ecological development of the landscape [39–42]. Table 5. Model summary of multivariable hierarchical linear regression results.

Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change 1 0.459 0.21 0.205 13.0664 0.21 39.709 3 447 <0.001 2 0.687 0.472 0.455 10.82127 0.261 19.611 11 436 <0.001 https://doi.org/10.1371/journal.pone.0301168.t005 Table 6. Multivariable hierarchical linear regression results. Model Unstandardized Coefficients Beta regression coefficients t Sig. Part Collinearity Statistics B Std. Error Tolerance VIF 1 Yard size (ha) -0.335 0.095 -0.160 -3.532 <0.001 -0.148 0.864 1.157 Number of horses -0.028 0.052 -0.025 -0.543 0.587 -0.023 0.862 1.160 Number of LFs 1.817 0.179 0.426 10.131 <0.001 0.426 0.997 1.003 2 Yard size -0.505 0.083 -0.241 -6.086 <0.001 -0.212 0.775 1.290 Number of horses -0.088 0.045 -0.076 -1.955 0.051 -0.068 0.794 1.260 Number of LFs 0.987 0.167 0.232 5.898 <0.001 0.205 0.785 1.274 Tree lanes total m2 0.002 0.001 0.050 1.342 0.180 0.047 0.877 1.140 Tree rows total m2 0.003 0.000 0.207 5.386 <0.001 0.187 0.824 1.213 Fruit orchard total m2 0.006 0.003 0.073 1.992 0.047 0.069 0.908 1.102 Wild Hedges total m2 0.003 0.001 0.122 3.089 0.002 0.108 0.780 1.282 Flowering strips total m2 0.003 0.001 0.156 4.242 <0.001 0.148 0.901 1.110 Woody strips total m2 0.001 0.001 0.047 1.211 0.227 0.042 0.788 1.269 Herbaceous grassland total m2 0.001 0.000 0.061 1.711 0.088 0.060 0.966 1.036 Buffer strips total m2 0.004 0.001 0.276 7.831 <0.001 0.273 0.973 1.028

Embankments total m2 0.002 0.000 0.147 3.980 <0.001 0.139 0.889 1.125 Wood piles total m2 0.017 0.011 0.055 1.544 0.123 0.054 0.961 1.040 Cluttered corners total m2 0.009 0.002 0.166 4.676 <0.001 0.163 0.957 1.045 aDependent Variable: PPC https://doi.org/10.1371/journal.pone.0301168.t006 PLOS ONE Biodiversity potential of equine yards through landscape features PLOS ONE | https://doi.org/10.1371/journal.pone.0301168 April 11, 2024 10 / 16