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Animal pollinators are globally threatened by anthropogenic land use change and agricultural intensification. The yield of many food crops is therefore negatively impacted because they benefit from biotic pollination. This is especially the case in the tropics. For instance, fruit set of Coffea arabica has been shown to increase by 10–30% in plantations with a high richness of bee species, possibly influenced by the availability of surrounding forest habitat. Here, we performed a global literature review to (1) assess how much animal pollination enhances coffee fruit set, and to (2) examine the importance of the amount of forest cover, distance to nearby forest and forest canopy density for bee species richness and coffee fruit set. Using a systematic literature review, we identified eleven case studies with a total of 182 samples where fruit set of C. arabica was assessed. We subsequently gathered forest data for all study sites from satellite imagery. We modelled the effects of open (all forest with a canopy density of ≥25%), closed (≥50%) and dense (≥75%) forests on pollinator richness and fruit set of coffee. Overall, we found that animal pollination increases coffee fruit set by ~18% on average. In only one of the case studies, regression results indicate a positive effect of dense forest on coffee fruit set, which increased with higher forest cover and shorter distance to the forest. Against expectations, forest cover and distance to open forest were not related to bee species richness and fruit set. In summary, we provide strong empirical support for the notion that animal pollinators increase coffee fruit set. Forest proximity had little overall influence on bee richness and coffee fruit set, except when farms were surrounded by dense tropical forests, potentially because these may provide high-quality habitats for bees pollinating coffee. We, therefore, advocate that more research is done to understand the biodiversity value of dense forest for pollinators, notably assessing the mechanisms underlying the importance of forest for pollinators and their pollination services.
Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria
(2022)
The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.
To highlight human impact on biodiversity in the Lamto region, termites were studied with regard to their use as bio-indicators of habitat change in the tropics. Using a standardized method, termites were sampled in the three most common habitat types, i.e., in semi-deciduous forest, savanna woodland, and annually burned savanna, all inside Lamto Reserve and its surrounding rural domain. Termite species richness fell from 25 species in the Lamto forest to 13 species in the rural area, involving strong modification in the species composition (species turnover = 59 %). In contrast, no significant change in diversity was found between the Lamto savannas and the rural ones. In addition, the relative abundance of termites showed a significantly greater decline in the rural domain, even in the species Ancistrotermes cavithorax (Sjostedt) (Isoptera: Termitidae), which is known to be ecologically especially versatile. Overall, the findings of this study suggest further investigation around Lamto Reserve on the impact of human activities on biodiversity, focusing on forest conversion to land uses (e.g. agricultural and silvicultural systems).
Forests are increasingly affected by natural disturbances. Subsequent salvage logging, a widespread management practice conducted predominantly to recover economic capital, produces further disturbance and impacts biodiversity worldwide. Hence, naturally disturbed forests are among the most threatened habitats in the world, with consequences for their associated biodiversity. However, there are no evidence-based benchmarks for the proportion of area of naturally disturbed forests to be excluded from salvage logging to conserve biodiversity. We apply a mixed rarefaction/extrapolation approach to a global multi-taxa dataset from disturbed forests, including birds, plants, insects and fungi, to close this gap. We find that 757% (mean +/- SD) of a naturally disturbed area of a forest needs to be left unlogged to maintain 90% richness of its unique species, whereas retaining 50% of a naturally disturbed forest unlogged maintains 73 +/- 12% of its unique species richness. These values do not change with the time elapsed since disturbance but vary considerably among taxonomic groups. Salvage logging has become a common practice to gain economic returns from naturally disturbed forests, but it could have considerable negative effects on biodiversity. Here the authors use a recently developed statistical method to estimate that ca. 75% of the naturally disturbed forest should be left unlogged to maintain 90% of the species unique to the area.