Tropical forest degradation emits carbon at a rate of~0.5 Pg·y −1 , reduces biodiversity, and facilitates forest clearance. Understanding degradation drivers and patterns is therefore crucial to managing forests to mitigate climate change and reduce biodiversity loss. Putative patterns of degradation affecting forest stocks, carbon, and biodiversity have variously been described previously, but these have not been quantitatively assessed together or tested systematically. Economic theory predicts a systematic allocation of land to its highest use value in response to distance from centers of demand. We tested this theory to see if forest exploitation would expand through time and space as concentric waves, with each wave targeting lower value products. We used forest data along a transect from 10 to 220 km from Dar es Salaam (DES), Tanzania, collected at two points in time (1991 and 2005). Our predictions were confirmed: high-value logging expanded 9 km·y −1 , and an inner wave of lower value charcoal production 2 km·y ; 0.1 species per sample area (0.4 ha)]. Our study suggests that tropical forest degradation can be modeled and predicted, with its attendant loss of some public goods. In sub-Saharan Africa, an area experiencing the highest rate of urban migration worldwide, coupled with a high dependence on forestbased resources, predicting the spatiotemporal patterns of degradation can inform policies designed to extract resources without unsustainably reducing carbon storage and biodiversity. biodiversity conservation | carbon emissions | reducing emissions from deforestation and forest degradation | sustainability | tropical forest degradation

BackgroundWith the recognition that environmental change resulting from anthropogenic activities is causing a global decline in biodiversity, much attention has been devoted to understanding how changes in biodiversity may alter levels of ecosystem functioning. Although environmental complexity has long been recognised as a major driving force in evolutionary processes, it has only recently been incorporated into biodiversity-ecosystem functioning investigations. Environmental complexity is expected to strengthen the positive effect of species richness on ecosystem functioning, mainly because it leads to stronger complementarity effects, such as resource partitioning and facilitative interactions among species when the number of available resource increases.Methodology/Principal FindingsHere we implemented an experiment to test the combined effect of species richness and environmental complexity, more specifically, resource richness on ecosystem functioning over time. We show, using all possible combinations of species within a bacterial community consisting of six species, and all possible combinations of three substrates, that diversity-functioning (metabolic activity) relationships change over time from linear to saturated. This was probably caused by a combination of limited complementarity effects and negative interactions among competing species as the experiment progressed. Even though species richness and resource richness both enhanced ecosystem functioning, they did so independently from each other. Instead there were complex interactions between particular species and substrate combinations.Conclusions/SignificanceOur study shows clearly that both species richness and environmental complexity increase ecosystem functioning. The finding that there was no direct interaction between these two factors, but that instead rather complex interactions between combinations of certain species and resources underlie positive biodiversity ecosystem functioning relationships, suggests that detailed knowledge of how individual species interact with complex natural environments will be required in order to make reliable predictions about how altered levels of biodiversity will most likely affect ecosystem functioning.

The importance of bioturbation in mediating biogeochemical processes in the upper centimetres of oceanic sediments provides a compelling reason for wanting to quantify in situ rates of bioturbation. Whilst several approaches can be used for estimating the rate and extent of bioturbation, most often it is characterized by calculating an intensity coefficient (D b ) and/or a mixed layer depth (L). Using measures of D b (n = 447) and L (n = 784) collated largely from peer-reviewed literature, we have assembled a global database and examined patterns of both L and D b . At the broadest level, this database reveals that there are considerable gaps in our knowledge of bioturbation for all major oceans other than the North Atlantic, and almost universally for the deep ocean. Similarly, there is an appreciable bias towards observations in the Northern Hemisphere, particularly along the coastal regions of North America and Europe. For the assembled dataset, we find large discrepancies in estimations of L and D b that reflect differences in boundary conditions and reaction properties of the methods used. Tracers with longer half-lives tend to give lower D b estimates and deeper mixing depths than tracers with shorter half-lives. Estimates of L based on sediment profile imaging are significantly lower than estimates based on tracer methods. Estimations of L, but not D b , differ between biogeographical realms at the global level and, at least for the Temperate Northern Atlantic realm, also at the regional level. There are significant effects of season irrespective of location, with higher activities (D b ) observed during summer and deeper mixing depths (L) observed during autumn. Our evaluation demonstrates that we have reasonable estimates of bioturbation for only a limited set of conditions and regions of the world. For these data, and based on a conservative global mean (± SD) L of 5.75 ± 5.67 cm (n = 791), we calculate the global volume of bioturbated sediment to be > 20 700 km 3 . Whilst it is clear that the role of benthic invertebrates in mediating global ecosystem processes is substantial, the level of uncertainty at the regional level is unacceptably high for much of the globe.

The term "microbiome" was first coined in 1988 and given the definition of a characteristic microbial community occupying a reasonably well defined habitat which has distinct physio-chemical properties. A more recent term has also emerged, taking this one step further and focusing on diseases in host organisms. The "pathobiome" breaks down the concept of "one pathogen = one disease" and highlights the role of the microbiome, more specifically certain members within the microbiome, in causing pathogenesis. The development of next generation sequencing has allowed large data sets to be amassed describing the microbial communities of many organisms and the field of coral biology is no exception. However, the choices made in the analytical process and the interpretation of these data can significantly affect the outcome and the overall conclusions drawn. In this review we explore the implications of these difficulties, as well as highlighting analytical tools developed in other research fields (such as network analysis) which hold substantial potential in helping to develop a deeper understanding of the role of the microbiome in disease in corals. We also make the case that standardization of methods will substantially improve the collective gain in knowledge across research groups.