Prioritizing landscapes for mitigating the impacts of onshore wind farms on multidimensional waterbird diversity in the Yellow Sea

Abstract

Ongoing wind energy developments play a key role in mitigating the global effects of climate change and the energy crisis; however, they have complex ecological consequences for many flying animals. The Yellow Sea coast is considered as an ecological bottleneck for migratory waterbirds along the East Asian-Australasian flyway (EAAF), and is also an important wind farm base in China. However, the effects of large-scale onshore wind farms along the EAAF on multidimensional waterbird diversity, and how to mitigate these effects, remain unclear. Here we examined how wind farms and their surrounding landscapes affected multidimensional waterbird diversity along the Yellow Sea coast. Taxonomic, functional, and phylogenetic diversity of the waterbird assemblages, and mean pairwise distances and nearest taxon distances with null models were quantified in relation to 4 different wind turbine densities. We also measured 6 landscape variables. Multi-dimensional waterbird diversity (taxonomic, functional, and phylogenetic diversity) significantly decreased with increasing wind turbine density. Functional and phylogenetic structures tended to be clustered in waterbird communities, and environmental filtering drove waterbird community assemblages. Furthermore, waterbird diversity was regulated by a combination of wind turbine density and landscape variables, with edge density of aquaculture ponds, in addition to wind turbine density, having the greatest independent contribution to waterbird diversity. These results suggest that attempts to mitigate the impact of wind farms on waterbird diversity could involve the landscape transformation of wind farm regions, for example, by including high-edge-density aquaculture ponds (i.e., industrial ponds) around wind farms, instead of traditional low-edge-density aquaculture ponds.

Keywords: East China coast; environmental filtering; mitigation measure; renewable energy; sustainable development; waterbird conservation.

Figure 1

Distribution of wind farms and transects along the Yellow Sea coast. (A) whole study area; (B–D), survey regions corresponding to detailed images in (B–D).

Figure 2

Map of example landscape interpretation with radius of 1 km (Jiangsu Qidong on Yellow Sea coast).

Figure 3

Waterbird diversity around onshore wind farms along the Yellow Sea coast. Different small letters above bars indicate P < 0.05. Mean ± standard error (SE). TD, taxonomic diversity; FD, functional diversity; PD, phylogenetic diversity; MFD, mean pairwise functional distance; MPD, mean pairwise phylogenetic distance; SES.FD, standardized effect size of FD; SES.PD, standardized effect size of PD; SES.MFD, standardized effect size of MFD; SES.MPD, standardized effect size of MPD.

Figure 4

Landscape variables at onshore wind farms along the Yellow Sea coast. Different small letters above bars indicate P < 0.05. Mean ± standard error (SE). LCFH, landscape configurational heterogeneity; LCPH, landscape compositional heterogeneity; TA, area of tidal flats; FA, area of farmland; AA, area of aquaculture ponds; APED, aquaculture pond-edge density.

Figure 5

Independent contributions of each variable derived by hierarchical partitioning to each dimension of biodiversity of waterbirds along the Yellow Sea coast. WF: wind farm; APED: aquaculture pond-edge density; TA: area of tidal flats; LCFH: landscape configurational heterogeneity; LCPH: landscape compositional heterogeneity; FA: area of farmland; TD: taxonomic diversity; FD: functional diversity; PD: phylogenetic diversity; MFD: mean pairwise functional distance; MPD: mean pairwise phylogenetic distance; SES.FD: standardized effect size of FD; SES.PD: standardized effect size of PD; SES.MFD: standardized effect size of MFD; SES.MPD: standardized effect size of MPD.