Search

Includes supplementary figures S1-S7 and tables S1-S2, and legends for other supplementary materials

The Tea Bag Index (TBI) is a standardised and cheap method to quantify microbial-driven decomposition by measuring the mass loss of tea within tea bags. Termites are known to damage the bags to access the content, rendering the method less suitable for termite-rich ecosystems. Extension of the TBI to accommodate and incorporate the influence of termites would broaden its applicability to include termite-rich ecosystems, such as tropical forests. We extended the original TBI by applying physical and chemical termite-exclusion methods. Tea mass loss and the proportion of tea bags detected by termites in the original TBI were also recorded to infer the role of termites in litter decomposition. TBI estimates derived from the original and extended TBI were compared, benchmarked against global estimates, and validated with time-series mass loss data. Using the original TBI, we found that termites damaged up to 80 % of tea bags and consumed the recalcitrant fraction of tea in several of them, leaving only 20 % of tea bags from which TBI estimates could be retrieved. The physical termite-exclusion treatment completely eliminated termite-infringement, thus preserving the full sample size for estimating TBI parameters. The chemical termite-exclusion treatment also successfully excluded termites, but potentially inhibited microbial decomposition and made TBI estimates unreliable. In the absence of termite-infringement, both the TBI estimates and time-series analysis revealed a low decomposition rate compared to other measurements in tropical and temperate regions. We propose an extended TBI, in which the physical termite-exclusion treatment is used to preserve the retrieval rate of TBI parameters and reliably measure microbial-driven decomposition, while the original TBI is used to incorporate the contribution of termites in driving litter mass loss. By characterising both termite- and microbial-driven decomposition, the extended TBI will provide a comprehensive understanding of decomposition and its drivers in termite-rich ecosystems, and permit global comparisons.

Southeast (SE) Asia holds high regional biodiversity and endemism levels but is also one of the world's most threatened regions. Local, regional and global threats could have severe consequences for the future survival of many species and the provision of ecosystem services.In the face of myriad pressing environmental problems, we carried out a research prioritisation exercise involving 64 experts whose research relates to conservation biology and sustainability in SE Asia. Experts proposed the most pressing research questions which, if answered, would advance the goals of biodiversity conservation and sustainable development in SE Asia. We received a total of 333 questions through three rounds of elicitation, ranked them (by votes) following a workshop and grouped them into themes.The top 100 questions depict SE Asia as a region where strong pressures on biodiversity interact in complex and poorly understood ways. They point to a lack of information about multiple facets of the environment, while exposing the many threats to biodiversity and human wellbeing. The themes that emerged indicate the need to evaluate specific drivers of biodiversity loss (wildlife harvesting, agricultural expansion, climate change, infrastructure development, pollution) and even to identify which species and habitats are most at risk. They also suggest the need to study the effectiveness of practice-based solutions (protected areas, ecological restoration), the human dimension (social interventions, organisational systems and processes and, the impacts of biodiversity loss and conservation interventions on people). Finally, they highlight gaps in fundamental knowledge of ecosystem function. These 100 questions should help prioritise and coordinate research, conservation, education and outreach activities and the distribution of scarce conservation resources in SE Asia.

International audience; Quantifying the effects of species interactions is key to understanding the relationships between biodiversity and ecosystem functioning but remains elusive due to combinatorics issues. Functional groups have been commonly used to capture the diversity of forms and functions and thus simplify the reality. However, the explicit incorporation of species interactions is still lacking in functional group-based approaches. Here, we propose a new approach based on an a posteriori clustering of species to quantify the effects of species interactions on ecosystem functioning.We first decompose the observed ecosystem function using null models, in which species diversity does not affect ecosystem function, to separate the effects of species interactions and species composition. This allows the identification of a posteriori functional groups that have contrasting diversity effects on ecosystem functioning. We then develop a formal combinatorial model of species interactions in which an ecosystem is described as a combination of co-occurring functional groups, which we call an assembly motif. Each assembly motif corresponds to a particular biotic environment. We demonstrate the relevance of our approach using datasets from a microbial experiment and the long-term Cedar Creek Biodiversity II experiment.We show that our a posteriori approach is more accurate, more efficient and more parsimonious than a priori approaches. The discrepancy between a priori and a posteriori approaches results from the way each clustering is set up: a priori approaches are based on ecosystem or species properties, such as ecosystem size (number of species or functional groups) or species' functional traits, whereas our a posteriori approach is based only on the observed interaction and composition effects on ecosystem functioning.Our findings demonstrate that an a posteriori approach is highly explanatory: it identifies who interacts with whom, and quantifies the effects of species interactions on ecosystem functioning. They also highlight that a combinatorial modelling of ecosystem functioning can predict the functioning of an ecosystem without any hypothesis about the biotic or environmental determinants or any information on species functional traits. It only requires the species composition of the ecosystem and the observed functioning of others that share the same assembly motif.

This posters was presented at the ACES ESP conference in Washington D.C. December 2018. It is a visualization of the research with the same name. AbstractEffective urban planning depends on knowing homebuyers' preferences for neighbourhood features that provide different amenities, such as managed parks and trees. As the expansion of tropical urban areas into biodiversity hotspots is predicted to more than double by 2030, knowing homebuyers utility from different vegetation types can contribute to global biodiversity conservation strategies. We used the hedonic pricing method to estimate the economic value of managed, spontaneous and high conservation value vegetation to Singapore public housing using a mixed effects model. On average vegetation had positive effects on property selling price, accounting for 3% of the average property's value, or a total of S$179 million for all public housing apartments sold over 13 months. These effects were almost entirely driven by managed vegetation, which had positive marginal effects on price for 98.1% of properties. The estimated marginal effects of high conservation value vegetation were mostly neutral, with some being negative (90.5%% properties), but positive for properties without much managed vegetation nearby. The estimated marginal effects of spontaneous vegetation were negative but mostly small.