The expansion of oil palm plantations has led to land-use change and deforestation in the tropics, which has affected biodiversity. Although the impacts of the crop on terrestrial biodiversity have been extensively reviewed, its effects on freshwater biodiversity remain relatively unexplored. We reviewed the research assessing the impacts of forest-to-oil palm conversion on freshwater biota and the mitigating effect of riparian buffers on these impacts. We searched for studies comparing taxa richness, species abundance, and community composition of macroinvertebrates, amphibians, and fish in streams in forests (primary and disturbed) and oil palm plantations with and without riparian buffers. Then, we conducted a meta-analysis to quantify the overall effect of the land-use change on the 3 taxonomic groups. Twenty-nine studies fulfilled the inclusion criteria. On average, plantations lacking buffers hosted 44% and 19% fewer stream taxa than primary and disturbed forests, respectively. Stream taxa on plantations with buffers were 24% lower than in primary forest and did not differ significantly from disturbed forest. In contrast, stream community composition differed between forests and plantations regardless of the presence of riparian buffers. These differences were attributed to agrochemical use and altered environmental conditions in the plantations, including temperature changes, worsened water conditions, microhabitat loss, and food and shelter depletion. On aggregate, abundance did not differ significantly among land uses because increases in generalist species offset the population decline of vulnerable forest specialists in the plantation. Our results reveal significant impacts of forest-to-oil palm conversion on freshwater biota, particularly taxa richness and composition (but not aggregate abundance). Although preserving riparian buffers in the plantations can mitigate the loss of various aquatic species, it cannot conserve primary forest communities. Therefore, safeguarding primary forests from the oil palm expansion is crucial, and further research is needed to address riparian buffers as a promising mitigation strategy in agricultural areas.
The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low.