High bee functional diversity buffers crop pollination services against Amazon deforestation

Predicting outcomes of land use change on biodiversity and ecosystem services remains a key priority for ecologists, but may be particularly challenging in diverse tropical ecosystems. Trait-based approaches are a key tool to meet this challenge. Such approaches seek functional mechanisms underpinning species’ responses to environmental disturbance and contributions to ecosystem services. Here, we use a functional trait approach to study effects of land use change on stingless bee communities and on pollination services to açaí palm (Euterpe oleracea, Arecaceae) in the eastern Brazilian Amazon. We compared traits of stingless bees visiting açaí inflorescences across a land use intensity gradient (low to high forest cover) to determine: (1) the role of traits in bee species’ responses to deforestation; (2) how deforestation affects functional composition of bee communities; and (3) whether bee traits better explain variation in açaí fruit production than species diversity metrics. We found that bee species’ responses to deforestation were non-random and predicted by body size, with small-sized bees more susceptible to forest loss, and changes in functional diversity of bee communities were important for pollination services. However, not all changes in functional diversity were associated with forest loss. Together, these results suggest that: (1) large tracts of minimally disturbed tropical rainforest are vital for the conservation of diverse stingless bee communities; (2) efficient pollination is contingent on bee species not only having divergent trait values (functional dispersion), but also traits’ relative abundance in communities (functional evenness); and (3) high functional diversity in stingless bee communities buffers açaí pollination services to loss of sensitive species. Thus, conservation strategies must focus on protecting wider biodiversity, not just ecosystem services, to guarantee conservation of native eusocial bee taxa. Doing so will safeguard crop pollination services, the pollination of native plant communities, and the long-term resilience of Amazon forest ecosystems

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Data from: Anthropogenic disturbance of tropical forests threatens pollination services to açaí palm in the Amazon river delta

The açaí palm Euterpe oleracea Mart. in the Amazon river delta has seen rapid expansion to meet increased demand for its fruit. This has been achieved by transforming lowland forest habitats (floodplains) into simplified agroforests and intensive plantation in upland areas. As açaí palm makes an important contribution to the economy and food security of local communities, identifying management approaches that support biodiversity and ecosystem processes that underpin fruit production on açaí farms is essential. We compared flower-visitor communities and açaí fruit production in floodplain forests and upland plantations, across gradients of local management intensity (i.e. açaí density per ha) and surrounding forest cover. The relative contribution of biotic pollination and degree of pollen limitation were assessed using insect exclusion and hand-pollination experiments. We found that açaí flower visitors are highly diverse (c. 200 distinct taxa) and had variable responses to disturbance. Bee visitation was higher in floodplains and positively related to surrounding forest cover, but other flower visitors, including specialised curculionid beetles, were unresponsive to changes in surrounding forest cover. However, intensive management practices (i.e. high açaí palm densities) in floodplains and uplands had contrasting effects on flower-visitor communities, with flower-visitor richness being lower on intensively managed floodplain farms and ant densities being higher on intensive upland farms. Pollination experiments revealed açaí palm to be highly dependent on biotic pollination. Fruit set in open-pollinated inflorescences was positively related to flower-visitor richness and specialised curculionid beetle visitation, whereas the presence of ants on inflorescences had a negative effect. Synthesis and applications. Our study shows that pollinators are essential for açaí fruit production, but that intensive farming practices have eroded the relationship between surrounding forest cover and ecosystem function in floodplains (i.e. conversion of native forest into simplified agroforests) and increased the frequency of antagonistic interactions in uplands (e.g. high ant densities). These findings underline the value of extensive management practices, such as the maintenance of other tree species within farms and adjacent unmanaged forest patches, to ensure the long-term sustainability of açaí fruit production in the Amazon river delta

Acai fruit production data

Fruit set on marked inflorescences - including data from pollinator-excluded, open and hand-pollinated inflorescences (see README.txt for more details