Assessing the biodiversity value of degraded lowland forest in Sumatra, Indonesia

Forest degradation, forest fires, and wildlife poaching have devastated biodiversity in Indonesia. To assess the impact of forest degradation and the potential for recovery, we used birds as a proxy for biodiversity and assessed density estimates (hereafter density) in the degraded lowland forest of Harapan Rainforest Ecosystem Restoration Concession (HRF) in Sumatra. In this study, a total of 149 bird species (from 5,317 individuals) were recorded. Of the 103 species for which densities could be calculated, 45% were lowland bird specialists (i.e. species occurring below 200 m above sea level in Sumatra), including three globally threatened and 41 Near-Threatened species. Comparison with bird densities in degraded forest of Borneo revealed that there was broad similarity across taxa but three species had significantly higher density, and four had significantly lower density, in HRF. The mosaic of degraded forest habitats in different stages of regeneration in HRF appears to support more individuals of some species, especially woodpeckers, than the Bornean sites, but fewer individuals of other species. Determining bird densities is essential to establish population baselines, allowing comparisons between sites and over time. The present study fills one gap, but we urge others to conduct similar studies to provide a better understanding of the temporal and spatial variation in bird density in Southeast Asia’s degraded forests

Responses of terrestrial herb assemblages to weeding and fertilization in cacao agroforests in Indonesia

Terrestrial herbs are important ecological components in tropical agroforests, but little is known about how they are affected by agricultural management. In cacao agroforests of Central Sulawesi, Indonesia, we studied the change in herb species richness, cover, and biomass over 3years in 86 subplots subjected to high and low weeding frequency as well as fertilized and non-fertilized treatments. We recorded 111 species with rapid changes in species composition between the 3years. Species richness increased sharply in the 2nd year, presumably as a result of changes in the management with the experimental regimes, and decreased in the 3rd, probably due to competitive exclusion. Species richness, cover, and biomass were all significantly higher in the infrequently weeded plots than in the frequently weeded ones, but there were only slight responses to the fertilization treatment. An indicator species analysis recovered 45 species that were typical for a given year and a further eight that were typical for certain treatments, but these species showed no clear patterns relative to their ecology or biogeography. We conclude that the herb assemblages in cacao agroforests are quite resilient against weeding, but that the cover of species shifts rapidly in response to managemen

Soil carbon insures arable crop production against increasing adverse weather due to climate change

Intensification of arable crop production degrades soil health and production potential through loss of soil organic carbon. This, potentially, reduces agriculture's resilience to climate change and thus food security. Furthermore, the expected increase in frequency of adverse and extreme weather events due to climate change are likely to affect crop yields differently, depending on when in the growing season they occur. We show that soil carbon provides farmers with a natural insurance against climate change through a gain in yield stability and more resilient production. To do this, we combined yield observations from 12 sites and 54 years of Swedish long-term agricultural experiments with historical weather data. To account for heterogenous climate effects, we partitioned the growing season into four representative phases for two major cereal crops. Thereby, we provide evidence that higher soil carbon increases yield gains from favourable conditions and reduces yield losses due to adverse weather events and how this occurs over different stages of the growing season. However, agricultural management practices that restore the soil carbon stock, thus contributing to climate change mitigation and adaptation, usually come at the cost of foregone yield for the farmer in the short term. To halt soil degradation and make arable crop production more resilient to climate change, we need agricultural policies that address the public benefits of soil conservation and restoration

Does Landscape Complexity and Semi-Natural Habitat Structure Affect Diversity of Flower-Visiting Insects in Cucumber Fields?

Presence of insects in agricultural habitat is affected by the surrounding circumstances such as the complexity and structure of landscape. Landscape structure is often formed as a consequence of the fragmentation of semi-natural habitat, which can negatively affect species richness and abundance of insects. This study was aimed to study the effect of complexity and structure of landscape on the diversity, abundance and traits of flower-visiting insects in cucumber fields. This study was conducted in cucumber fields surrounded by other agricultural crops, shrubs, semi-natural habitat and housing area, in Bogor, Cianjur and Sukabumi regencies, West Java, Indonesia. In a total of 16 agricultural areas, complexity and parameter of landscape especially class area (CA), number of patches (NumP), mean patch size (MPS), total edge (TE), and mean shape index (MSI) of seminatural habitats were measured. Sampling of flower-visiting insects was conducted using scan sampling methods. The result showed that landscape complexity affected species richness (but not abundance and trait) of flower-visiting insects both for mobile and less-mobile insects. Flower-visiting insects also responded differently to landscape structure. Species richness, abundance and variation of body size of mobile insects were affected by structure of semi-natural habitat

Climate change and ecological intensification of agriculture in sub-Saharan Africa-A systems approach to predict maize yield under push-pull technology

Assessing effects of climate change on agricultural systems and the potential for ecological intensification to increase food security in developing countries is essential to guide management, policy-making and future research. 'Push-pull' technology (PPT) is a poly-cropping design developed in eastern Africa that utilizes plant chemicals to mediate plant-insect interactions. PPT application yields significant increases in crop productivity, by reducing pest load and damage caused by arthropods and parasitic weeds, while also bolstering soil fertility. As climate change effects may be species-and/or context-specific, there is need to elucidate how, in interaction with biotic factors, projected climate conditions are likely to influence future functioning of PPT. Here, we first reviewed how changes in temperature, precipitation and atmospheric CO2 concentration can influence PPT components (i.e., land use, soils, crops, weeds, diseases, pests and their natural enemies) across sub-Saharan Africa (SSA). We then imposed these anticipated responses on a landscape-scale qualitative mathematical model of maize production under PPT in eastern Africa, to predict cumulative, structure-mediated impacts of climate change on maize yield. Our review suggests variable impacts of climate change on PPT components in SSA by the end of the 21st century, including reduced soil fertility, increased weed and arthropod pest pressure and increased prevalence of crop diseases, but also increased biological control by pests' natural enemies. Extrapolating empirical evidence of climate effects to predict responses to projected climate conditions is mainly limited by a lack of mechanistic understanding regarding single and interactive effects of climate variables on PPT components. Model predictions of maize yield responses to anticipated impacts of climate change in eastern Africa suggest predominantly negative future trends. Nevertheless, maize yields can be sustained or increased by favourable changes in system components with less certain future behaviour, including higher PPT adoption, preservation of field edge density and agricultural diversification beyond cereal crops

Multifunctional shade‐tree management in tropical agroforestry landscapes – a review

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87099/1/j.1365-2664.2010.01939.x.pd

Models of natural pest control : Towards predictions across agricultural landscapes

Natural control of invertebrate crop pests has the potential to complement or replace conventional insecticide based practices, but its mainstream application is hampered by predictive unreliability across agroecosystems. Inconsistent responses of natural pest control to changes in landscape characteristics have been attributed to ecological complexity and system-specific conditions. Here, we review agroecological models and their potential to provide predictions of natural pest control across agricultural landscapes. Existing models have used a multitude of techniques to represent specific crop-pest-enemy systems at various spatiotemporal scales, but less wealthy regions of the world are underrepresented. A realistic representation of natural pest control across systems appears to be hindered by a practical trade-off between generality and realism. Nonetheless, observations of context-sensitive, trait-mediated responses of natural pest control to land-use gradients indicate the potential of ecological models that explicitly represent the underlying mechanisms. We conclude that modelling natural pest control across agroecosystems should exploit existing mechanistic techniques towards a framework of contextually bound generalizations. Observed similarities in causal relationships can inform the functional grouping of diverse agroecosystems worldwide and the development of the respective models based on general, but context-sensitive, ecological mechanisms. The combined use of qualitative and quantitative techniques should allow the flexible integration of empirical evidence and ecological theory for robust predictions of natural pest control across a wide range of agroecological contexts and levels of knowledge availability. We highlight challenges and promising directions towards developing such a general modelling framework.Peer reviewe

Evaluating predictive performance of statistical models explaining wild bee abundance in a mass‐flowering crop

Wild bee populations are threatened by current agricultural practices in many parts of the world, which may put pollination services and crop yields at risk. Loss of pollination services can potentially be predicted by models that link bee abundances with landscape‐scale land‐use, but there is little knowledge on the degree to which these statistical models are transferable across time and space. This study assesses the transferability of models for wild bee abundance in a mass‐flowering crop across space (from one region to another) and across time (from one year to another). The models used existing data on bumblebee and solitary bee abundance in winter oilseed rape fields, together with high‐resolution land‐use crop‐cover and semi‐natural habitats data, from studies conducted in five different regions located in four countries (Sweden, Germany, Netherlands and the UK), in three different years (2011, 2012, 2013). We developed a hierarchical model combining all studies and evaluated the transferability using cross‐validation. We found that both the landscape‐scale cover of mass‐flowering crops and permanent semi‐natural habitats, including grasslands and forests, are important drivers of wild bee abundance in all regions. However, while the negative effect of increasing mass‐flowering crops on the density of the pollinators is consistent between studies, the direction of the effect of semi‐natural habitat is variable between studies. The transferability of these statistical models is limited, especially across regions, but also across time. Our study demonstrates the limits of using statistical models in conjunction with widely available land‐use crop‐cover classes for extrapolating pollinator density across years and regions, likely in part because input variables such as cover of semi‐natural habitats poorly capture variability in pollinator resources between regions and years

Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions

International audienceAgricultural landscape homogenization has detrimental effects on biodiversity and key ecosystem services. Increasing agricultural landscape heterogeneity by increasing seminatural cover can help to mitigate biodiversity loss. However, the amount of seminatural cover is generally low and difficult to increase in many intensively managed agricultural landscapes. We hypothesized that increasing the heterogeneity of the crop mosaic itself (hereafter “crop heterogeneity”) can also have positive effects on biodiversity. In 8 contrasting regions of Europe and North America, we selected 435 landscapes along independent gradients of crop diversity and mean field size. Within each landscape, we selected 3 sampling sites in 1, 2, or 3 crop types. We sampled 7 taxa (plants, bees, butterflies, hoverflies, carabids, spiders, and birds) and calculated a synthetic index of multitrophic diversity at the landscape level. Increasing crop heterogeneity was more beneficial for multitrophic diversity than increasing seminatural cover. For instance, the effect of decreasing mean field size from 5 to 2.8 ha was as strong as the effect of increasing seminatural cover from 0.5 to 11%. Decreasing mean field size benefited multitrophic diversity even in the absence of seminatural vegetation between fields. Increasing the number of crop types sampled had a positive effect on landscape-level multitrophic diversity. However, the effect of increasing crop diversity in the landscape surrounding fields sampled depended on the amount of seminatural cover. Our study provides large-scale, multitrophic, cross-regional evidence that increasing crop heterogeneity can be an effective way to increase biodiversity in agricultural landscapes without taking land out of agricultural production

Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models

1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate. 2. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process. 3. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores. 4. Our results highlight a key universal challenge of calibrating spatially-explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that is often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data