Fine root dynamics across pantropical rainforest ecosystems.

Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than aboveground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old-growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi-deciduous, and deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n = 47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water-stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting that root productivity is a particularly strong driver of NPP allocation in tropical mountain regions.

The conservation value of human-modified landscapes for the world's primates.

Land-use change pushes biodiversity into human-modified landscapes, where native ecosystems are surrounded by anthropic land covers (ALCs). Yet, the ability of species to use these emerging covers remains poorly understood. We quantified the use of ALCs by primates worldwide, and analyzed species' attributes that predict such use. Most species use secondary forests and tree plantations, while only few use human settlements. ALCs are used for foraging by at least 86 species with an important conservation outcome: those that tolerate heavily modified ALCs are 26% more likely to have stable or increasing populations than the global average for all primates. There is no phylogenetic signal in ALCs use. Compared to all primates on Earth, species using ALCs are less often threatened with extinction, but more often diurnal, medium or large-bodied, not strictly arboreal, and habitat generalists. These findings provide valuable quantitative information for improving management practices for primate conservation worldwide.

The Environmental Legacy of Modern Tropical Deforestation.

Tropical deforestation has caused a significant share of carbon emissions and species losses, but historical patterns have rarely been explicitly considered when estimating these impacts [1]. A deforestation event today leads to a time-delayed future release of carbon, from the eventual decay either of forest products or of slash left at the site [2]. Similarly, deforestation often does not result in the immediate loss of species, and communities may exhibit a process of "relaxation" to their new equilibrium over time [3]. We used a spatially explicit land cover change model [4] to reconstruct the annual rates and spatial patterns of tropical deforestation that occurred between 1950 and 2009 in the Amazon, in the Congo Basin, and across Southeast Asia. Using these patterns, we estimated the resulting gross vegetation carbon emissions [2, 5] and species losses over time [6]. Importantly, we accounted for the time lags inherent in both the release of carbon and the extinction of species. We show that even if deforestation had completely halted in 2010, time lags ensured there would still be a carbon emissions debt of at least 8.6 petagrams, equivalent to 5-10 years of global deforestation, and an extinction debt of more than 140 bird, mammal, and amphibian forest-specific species, which if paid, would increase the number of 20(th)-century extinctions in these groups by 120%. Given the magnitude of these debts, commitments to reduce emissions and biodiversity loss are unlikely to be realized without specific actions that directly address this damaging environmental legacy.

Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues.

Land cover change (LCC) models are used in many studies of human impacts on the environment, but knowing how well these models predict observed changes in the landscape is a challenge. We used nearly three decades of LCC maps to run several LCC simulations to: (1) determine which parameters associated with drivers of LCC (e.g. roads) get selected for which transition (forest to deforested, regeneration to deforested or deforested to regeneration); (2) investigate how the parameter values vary through time with respect to the different activities (e.g. farming); and (3) quantify the influence of choosing a particular time period for model calibration and validation on the performance of LCC models. We found that deforestation of primary forests tends to occur along roads (included in 95 % of models) and outside protected areas (included in all models), reflecting farming establishment. Regeneration tends to occur far from roads (included in 78 % of the models) and inside protected areas (included in 38 % of the models), reflecting the processes of land abandonment. Our temporal analysis of model parameters revealed a degree of variation through time (e.g. effectiveness of protected areas rose by 73 %, p < 0.001), but for the majority of parameters there was no significant trend. The degree to which model predictions agreed with observed change was heavily dependent on the year used for calibration (p < 0.001). The next generation of LCC models may need to embed trends in parameter values to allow the processes determining LCC to change through time and exert their influence on model predictions.

Road networks predict human influence on Amazonian bird communities.

Road building can lead to significant deleterious impacts on biodiversity, varying from direct road-kill mortality and direct habitat loss associated with road construction, to more subtle indirect impacts from edge effects and fragmentation. However, little work has been done to evaluate the specific effects of road networks and biodiversity loss beyond the more generalized effects of habitat loss. Here, we compared forest bird species richness and composition in the municipalities of Santarém and Belterra in Pará state, eastern Brazilian Amazon, with a road network metric called 'roadless volume (RV)' at the scale of small hydrological catchments (averaging 3721 ha). We found a significant positive relationship between RV and both forest bird richness and the average number of unique species (species represented by a single record) recorded at each site. Forest bird community composition was also significantly affected by RV. Moreover, there was no significant correlation between RV and forest cover, suggesting that road networks may impact biodiversity independently of changes in forest cover. However, variance partitioning analysis indicated that RV has partially independent and therefore additive effects, suggesting that RV and forest cover are best used in a complementary manner to investigate changes in biodiversity. Road impacts on avian species richness and composition independent of habitat loss may result from road-dependent habitat disturbance and fragmentation effects that are not captured by total percentage habitat cover, such as selective logging, fire, hunting, traffic disturbance, edge effects and road-induced fragmentation.

The transparency, reliability and utility of tropical rainforest land-use and land-cover change models.

Land-use and land-cover (LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South-East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio-temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: (1) a lack of openness with regard to describing and making available the model inputs and model code; (2) the difficulties of conducting appropriate model validations; and (3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land-use policies based on plausible future scenarios, but, to do that reliably may require further improvements in the discipline.

Predictive modelling of contagious deforestation in the Brazilian Amazon.

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.

Optimising bait for pitfall trapping of Amazonian dung beetles (Coleoptera: Scarabaeinae).

The accurate sampling of communities is vital to any investigation of ecological processes and biodiversity. Dung beetles have emerged as a widely used focal taxon in environmental studies and can be sampled quickly and inexpensively using baited pitfalls. Although there is now a wealth of available data on dung beetle communities from around the world, there is a lack of standardisation between sampling protocols for accurately sampling dung beetle communities. In particular, bait choice is often led by the idiosyncrasies of the researcher, logistic problems and the dung sources available, which leads to difficulties for inter-study comparisons. In general, human dung is the preferred choice, however, it is often in short supply, which can severely limit sampling effort. By contrast, pigs may produce up to 20 times the volume. We tested the ability of human and pig dung to attract a primary forest dung beetle assemblage, as well as three mixes of the two baits in different proportions. Analyses focussed on the comparability of sampling with pig or human-pig dung mixes with studies that have sampled using human dung. There were no significant differences between richness and abundance sampled by each bait. The assemblages sampled were remarkably consistent across baits, and ordination analyses showed that the assemblages sampled by mixed dung baits were not significantly different from that captured by pure human dung, with the assemblages sampled by 10% and 90% pig mixes structurally most similar to assemblages sampled by human dung. We suggest that a 10:90 human:pig ratio, or similar, is an ideal compromise between sampling efficiency, inter-study comparability and the availability of large quantities of bait for sampling Amazonian dung beetles. Assessing the comparability of assemblage samples collected using different baits represents an important step to facilitating large-scale meta-analyses of dung beetle assemblages collected using non-standard methodology.

Using landscape history to predict biodiversity patterns in fragmented landscapes.

Landscape ecology plays a vital role in understanding the impacts of land-use change on biodiversity, but it is not a predictive discipline, lacking theoretical models that quantitatively predict biodiversity patterns from first principles. Here, we draw heavily on ideas from phylogenetics to fill this gap, basing our approach on the insight that habitat fragments have a shared history. We develop a landscape 'terrageny', which represents the historical spatial separation of habitat fragments in the same way that a phylogeny represents evolutionary divergence among species. Combining a random sampling model with a terrageny generates numerical predictions about the expected proportion of species shared between any two fragments, the locations of locally endemic species, and the number of species that have been driven locally extinct. The model predicts that community similarity declines with terragenetic distance, and that local endemics are more likely to be found in terragenetically distinctive fragments than in large fragments. We derive equations to quantify the variance around predictions, and show that ignoring the spatial structure of fragmented landscapes leads to over-estimates of local extinction rates at the landscape scale. We argue that ignoring the shared history of habitat fragments limits our ability to understand biodiversity changes in human-modified landscapes.

Fragmentation impairs the microclimate buffering effect of tropical forests.

BACKGROUND: Tropical forest species are among the most sensitive to changing climatic conditions, and the forest they inhabit helps to buffer their microclimate from the variable climatic conditions outside the forest. However, habitat fragmentation and edge effects exposes vegetation to outside microclimatic conditions, thereby reducing the ability of the forest to buffer climatic variation. In this paper, we ask what proportion of forest in a fragmented ecosystem is impacted by altered microclimate conditions driven by edge effects, and extrapolate these results to the whole Atlantic Forest biome, one of the most disturbed biodiversity hotspots. To address these questions, we collected above and below ground temperature for a full year using temperature sensors placed in forest fragments of different sizes, and at different distances from the forest edge. PRINCIPAL FINDINGS: In the Atlantic forests of Brazil, we found that the buffering effect of forests reduced maximum outside temperatures by one third or more at ground level within a forest, with the buffering effect being stronger below-ground than one metre above-ground. The temperature buffering effect of forests was, however, reduced near forest edges with the edge effect extending up to 20 m inside the forest. The heavily fragmented nature of the Brazilian Atlantic forest means that 12% of the remaining biome experiences altered microclimate conditions. CONCLUSIONS: Our results add further information about the extent of edge effects in the Atlantic Forest, and we suggest that maintaining a low perimeter-to-area ratio may be a judicious method for minimizing the amount of forest area that experiences altered microclimatic conditions in this ecosystem.

Response to comment on "Extinction debt and windows of conservation opportunity in the Brazilian Amazon".

Halley et al. purport to show a power-law relationship between fragment size and relaxation rates. We use a much more extensive data set to show that area dependence of relaxation rates exists only for very small fragment sizes (<60 hectares), which has limited relevance for our analyses conducted using 250,000-hectare grid squares. We also show that the example of Halley et al. is based on an unrealistic fragmentation model with an infinite number of fragments that have average size of zero hectares. A more realistic formulation of the model shows that relaxation is much less dependent on fragmentation than Halley et al. present.

Changes in size of deforested patches in the Brazilian Amazon.

Different deforestation agents, such as small farmers and large agricultural businesses, create different spatial patterns of deforestation. We analyzed the proportion of deforestation associated with different-sized clearings in the Brazilian Amazon from 2002 through 2009. We used annual deforestation maps to determine total area deforested and the size distribution of deforested patches per year. The size distribution of deforested areas changed over time in a consistent, directional manner. Large clearings (>1000 ha) comprised progressively smaller amounts of total annual deforestation. The number of smaller clearings (6.25-50.00 ha) remained unchanged over time. Small clearings accounted for 73% of all deforestation in 2009, up from 30% in 2002, whereas the proportion of deforestation attributable to large clearings decreased from 13% to 3% between 2002 and 2009. Large clearings were concentrated in Mato Grosso, but also occurred in eastern Pará and in Rondônia. In 2002 large clearings accounted for 17%, 15%, and 10% of all deforestation in Mato Grosso, Pará, and Rondônia, respectively. Even in these states, where there is a highly developed agricultural business dominated by soybean production and cattle ranching, the proportional contribution of large clearings to total deforestation declined. By 2009 large clearings accounted for 2.5%, 3.5%, and 1% of all deforestation in Mato Grosso, Pará, and Rondônia, respectively. These changes in deforestation patch size are coincident with the implementation of new conservation policies by the Brazilian government, which suggests that these policies are not effectively reducing the number of small clearings in primary forest, whether these are caused by large landholders or smallholders, but have been more effective at reducing the frequency of larger clearings.

Extinction debt and windows of conservation opportunity in the Brazilian Amazon.

Predicting when future species extinctions will occur is necessary for directing conservation investments but has proved difficult. We developed a new method for predicting extinctions over time, accounting for the timing and magnitude of habitat loss. We applied this to the Brazilian Amazon, predicting that local extinctions of forest-dependent vertebrate species have thus far been minimal (1% of species by 2008), with more than 80% of extinctions expected to be incurred from historical habitat loss still to come. Realistic deforestation scenarios suggest that local regions will lose an average of nine vertebrate species and have a further 16 committed to extinction by 2050. There is a window of opportunity to dilute the legacy of historical deforestation by concentrating conservation efforts in areas with greatest debt.

Spatial pattern of standing timber value across the Brazilian Amazon.

The Amazon is a globally important system, providing a host of ecosystem services from climate regulation to food sources. It is also home to a quarter of all global diversity. Large swathes of forest are removed each year, and many models have attempted to predict the spatial patterns of this forest loss. The spatial patterns of deforestation are determined largely by the patterns of roads that open access to frontier areas and expansion of the road network in the Amazon is largely determined by profit seeking logging activities. Here we present predictions for the spatial distribution of standing value of timber across the Amazon. We show that the patterns of timber value reflect large-scale ecological gradients, determining the spatial distribution of functional traits of trees which are, in turn, correlated with timber values. We expect that understanding the spatial patterns of timber value across the Amazon will aid predictions of logging movements and thus predictions of potential future road developments. These predictions in turn will be of great use in estimating the spatial patterns of deforestation in this globally important biome.

Unraveling the drivers of community dissimilarity and species extinction in fragmented landscapes.

Communities in fragmented landscapes are often assumed to be structured by species extinction due to habitat loss, which has led to extensive use of the species-area relationship (SAR) in fragmentation studies. However, the use of the SAR presupposes that habitat loss leads species to extinction but does not allow for extinction to be offset by colonization of disturbed-habitat specialists. Moreover, the use of SAR assumes that species richness is a good proxy of community changes in fragmented landscapes. Here, we assessed how communities dwelling in fragmented landscapes are influenced by habitat loss at multiple scales; then we estimated the ability of models ruled by SAR and by species turnover in successfully predicting changes in community composition, and asked whether species richness is indeed an informative community metric. To address these issues, we used a data set consisting of 140 bird species sampled in 65 patches, from six landscapes with different proportions of forest cover in the Atlantic Forest of Brazil. We compared empirical patterns against simulations of over 8 million communities structured by different magnitudes of the power-law SAR and with species-specific rules to assign species to sites. Empirical results showed that, while bird community composition was strongly influenced by habitat loss at the patch and landscape scale, species richness remained largely unaffected. Modeling results revealed that the compositional changes observed in the Atlantic Forest bird metacommunity were only matched by models with either unrealistic magnitudes of the SAR or by models ruled by species turnover, akin to what would be observed along natural gradients. We show that, in the presence of such compositional turnover, species richness is poorly correlated with species extinction, and z values of the SAR strongly underestimate the effects of habitat loss. We suggest that the observed compositional changes are driven by each species reaching its individual extinction threshold: either a threshold of forest cover for species that disappear with habitat loss, or of matrix cover for species that benefit from habitat loss.

Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon.

Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large-scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.

Boom-and-bust development patterns across the Amazon deforestation frontier.

The Brazilian Amazon is globally important for biodiversity, climate, and geochemical cycles, but is also among the least developed regions in Brazil. Economic development is often pursued through forest conversion for cattle ranching and agriculture, mediated by logging. However, on the basis of an assessment of 286 municipalities in different stages of deforestation, we found a boom-and-bust pattern in levels of human development across the deforestation frontier. Relative standards of living, literacy, and life expectancy increase as deforestation begins but then decline as the frontier evolves, so that pre- and postfrontier levels of human development are similarly low. New financial incentives and policies are creating opportunities for a more sustained development trajectory that is not based on the depletion of nature and ecosystem services.

Habitat fragmentation, variable edge effects, and the landscape-divergence hypothesis.

Edge effects are major drivers of change in many fragmented landscapes, but are often highly variable in space and time. Here we assess variability in edge effects altering Amazon forest dynamics, plant community composition, invading species, and carbon storage, in the world's largest and longest-running experimental study of habitat fragmentation. Despite detailed knowledge of local landscape conditions, spatial variability in edge effects was only partially foreseeable: relatively predictable effects were caused by the differing proximity of plots to forest edge and varying matrix vegetation, but windstorms generated much random variability. Temporal variability in edge phenomena was also only partially predictable: forest dynamics varied somewhat with fragment age, but also fluctuated markedly over time, evidently because of sporadic droughts and windstorms. Given the acute sensitivity of habitat fragments to local landscape and weather dynamics, we predict that fragments within the same landscape will tend to converge in species composition, whereas those in different landscapes will diverge in composition. This 'landscape-divergence hypothesis', if generally valid, will have key implications for biodiversity-conservation strategies and for understanding the dynamics of fragmented ecosystems.