Landscape heterogeneity filters functional traits of rice arthropods in tropical agroecosystems.

Biological control services of agroecosystems depend on the functional diversity of species traits. However, the relationship between arthropod traits and landscape heterogeneity is still poorly understood, especially in tropical rice agroecosystems, which harbor a high diversity of often specialized species. We investigated how landscape heterogeneity, measured by three metrics of landscape composition and configuration, influenced body size, functional group composition, dispersal ability, and vertical distribution of rice arthropods in the Philippines. We found that landscape composition and configuration acted to filter arthropod traits in tropical rice agroecosystems. Landscape diversity and rice habitat fragmentation were the two main gradients influencing rice-arthropod traits, indicating that different rice arthropods have distinct habitat requirements. Whereas small parasitoids and species mostly present in the rice canopy were favored in landscapes with high compositional heterogeneity, predators and medium-sized species occupying the base of the rice plant, including planthoppers, mostly occurred in highly fragmented rice habitats. We demonstrate the importance of landscape heterogeneity as an ecological filter for rice arthropods, identifying how the different components of landscape heterogeneity selected for or against specific functional traits. However, the contrasting effects of landscape parameters on different groups of natural enemies indicate that not all beneficial rice arthropods can be promoted at the same time when using a single land management strategy. Increasing compositional heterogeneity in rice landscapes can promote parasitoids but may also negatively affect predators. Future research should focus on identifying trade-offs between fragmented rice habitats and structurally diverse landscapes to maximize the presence of multiple groups of beneficial arthropods.

Archetype models upscale understanding of natural pest control response to land-use change.

Control of crop pests by shifting host plant availability and natural enemy activity at landscape scales has great potential to enhance the sustainability of agriculture. However, mainstreaming natural pest control requires improved understanding of how its benefits can be realized across a variety of agroecological contexts. Empirical studies suggest significant but highly variable responses of natural pest control to land-use change. Current ecological models are either too specific to provide insight across agroecosystems or too generic to guide management with actionable predictions. We suggest obtaining the full benefit of available empirical, theoretical, and methodological knowledge by combining trait-mediated understanding from correlative studies with the explicit representation of causal relationships achieved by mechanistic modeling. To link these frameworks, we adapt the concept of archetypes, or context-specific generalizations, from sustainability science. Similar responses of natural pest control to land-use gradients across cases that share key attributes, such as functional traits of focal organisms, indicate general processes that drive system behavior in a context-sensitive manner. Based on such observations of natural pest control, a systematic definition of archetypes can provide the basis for mechanistic models of intermediate generality that cover all major agroecosystems worldwide. Example applications demonstrate the potential for upscaling understanding and improving predictions of natural pest control, based on knowledge transfer and scientific synthesis. A broader application of this mechanistic archetype approach promises to enhance ecology's contribution to natural resource management across diverse regions and social-ecological contexts.

How range residency and long-range perception change encounter rates.

Encounter rates link movement strategies to intra- and inter-specific interactions, and therefore translate individual movement behavior into higher-level ecological processes. Indeed, a large body of interacting population theory rests on the law of mass action, which can be derived from assumptions of Brownian motion in an enclosed container with exclusively local perception. These assumptions imply completely uniform space use, individual home ranges equivalent to the population range, and encounter dependent on movement paths actually crossing. Mounting empirical evidence, however, suggests that animals use space non-uniformly, occupy home ranges substantially smaller than the population range, and are often capable of nonlocal perception. Here, we explore how these empirically supported behaviors change pairwise encounter rates. Specifically, we derive novel analytical expressions for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform space use and allows individual home ranges to differ from the population range. We compare OU-based encounter predictions to those of Reflected Brownian Motion, from which the law of mass action can be derived. For both models, we further explore how the interplay between the scale of perception and home-range size affects encounter rates. We find that neglecting realistic movement and perceptual behaviors can lead to systematic, non-negligible biases in encounter-rate predictions.

Conventional land-use intensification reduces species richness and increases production: A global meta-analysis.

Most current research on land-use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, we conducted a global meta-analysis synthesizing 115 studies which collected data for both variables at the same locations. We extracted 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. We found that, across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (-8.9%). However, analysis of sub-groups revealed inconsistent results. For example, small intensification steps within low intensity systems did not affect yield or species richness. Within high-intensity systems species losses were non-significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (-22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (-21.2%). In addition, this meta-analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land-use intensity. Our findings suggest that, in many cases, conventional land-use intensification drives a trade-off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results should guide future research to close existing research gaps and to understand the circumstances required to achieve such win-win or win-no-harm situations in conventional agriculture.

Harmonizing Biodiversity Conservation and Productivity in the Context of Increasing Demands on Landscapes.

Biodiversity conservation and agricultural production are often seen as mutually exclusive objectives. Strategies for reconciling them are intensely debated. We argue that harmonization between biodiversity conservation and crop production can be improved by increasing our understanding of the underlying relationships between them. We provide a general conceptual framework that links biodiversity and agricultural production through the separate relationships between land use and biodiversity and between land use and production. Hypothesized relationships are derived by synthesizing existing empirical and theoretical ecological knowledge. The framework suggests nonlinear relationships caused by the multifaceted impacts of land use (composition, configuration, and intensity). We propose solutions for overcoming the apparently dichotomous aims of maximizing either biodiversity conservation or agricultural production and suggest new hypotheses that emerge from our proposed framework.

Simulation of forest tree species' bud burst dates for different climate scenarios: chilling requirements and photo-period may limit bud burst advancement.

This study investigates whether the assumed increase of winter and spring temperatures is depicted by phenological models in correspondingly earlier bud burst (BB) dates. Some studies assume that rising temperatures lead to an earlier BB, but even later BB has been detected. The phenological model PIM (promoter-inhibitor-model) fitted to the extensive phenological database of the German Weather Service was driven by several climate scenarios. This model accounts for the complicated mechanistic interactions between chilling requirements, temperature and photo-period. It predicts BB with a r 2 between 0.41 and 0.62 and a RMSE of around 1 week, depending on species. Parameter sensitivities depict species dependent interactions between growth and chilling requirements as well as photo-period. A mean trend to earlier BB was revealed for the period 2002- 2100, varying between -0.05 and -0.11 days per year, depending on species. These trends are lower than for the period 1951- 2009. Within climate scenario period, trends are decreasing for beech and chestnut, stagnating for birch and increasing for oak. Results suggest that not fulfilled chilling requirements accompanied by an increasing dependency on photo-period potentially limit future BB advancement. The combination of a powerful phenological model, a large scale phenological database and several climate scenarios, offers new insights into the mechanistic comprehension of spring phenology.

Biodiversity-production feedback effects lead to intensification traps in agricultural landscapes.

Intensive agriculture with high reliance on pesticides and fertilizers constitutes a major strategy for 'feeding the world'. However, such conventional intensification is linked to diminishing returns and can result in 'intensification traps'-production declines triggered by the negative feedback of biodiversity loss at high input levels. Here we developed a novel framework that accounts for biodiversity feedback on crop yields to evaluate the risk and magnitude of intensification traps. Simulations grounded in systematic literature reviews showed that intensification traps emerge in most landscape types, but to a lesser extent in major cereal production systems. Furthermore, small reductions in maximal production (5-10%) could be frequently transmitted into substantial biodiversity gains, resulting in small-loss large-gain trade-offs prevailing across landscape types. However, sensitivity analyses revealed a strong context dependence of trap emergence, inducing substantial uncertainty in the identification of optimal management at the field scale. Hence, we recommend the development of case-specific safety margins for intensification preventing double losses in biodiversity and food security associated with intensification traps.

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape.

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the "One Sensor at Different Scales" (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R (2) of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.

An open-access global database of meta-analyses investigating yield and biodiversity responses to different management practices.

We here present a database of evidence on the impact of agricultural management practices on biodiversity and yield. This database is the result of a systematic literature review, that aimed to identify meta-analyses that use as their response variables any measure of biodiversity and yield. After screening more than 1,086 titles and abstracts, we identified 33 relevant meta-analyses, from which we extracted the overall estimates, the subgroup estimates as well as all information related to them (effect size metric, taxonomic group, crop type etc.). We also extracted information relative to the empirical studies used for each meta-analysis and recorded the countries in which they took place and assessed the quality of each meta-analysis. Our dataset is publicly accessible and can be used for conducting second-order meta-analyses on the effect of management measures on species richness, taxon abundance, biomass and yields. It can also be used to create evidence maps on agriculture-related questions.

Agriculture and food security under a changing climate: An underestimated challenge.

Pathways to eradicate global hunger while bending the curve of biodiversity loss unanimously suggest changing to less energy-rich diets, closing yield gaps through agroecological principles, adopting modern breeding technologies to foster stress resilience and yields, as well as minimizing harvest losses and food waste. Against the background of a brief history of global agriculture, we review the available evidence on how the global food system might look given a global temperature increase by 3°. We show that a moderate gain in the area suitable for agriculture is confronted with substantial yield losses through strains on crop physiology, multitrophic interactions, and more frequent extreme events. Self-amplifying feedback are unresolved and might lead to further losses. In light of these uncertainties, we see that complexity is underestimated and more systemic research is needed. Efficiency gains in agriculture, albeit indispensable, will not be enough to achieve food security under severe climate change.

Research priorities for global food security under extreme events.

Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.

Distinguishing anthropogenic and natural contributions to coproduction of national crop yields globally.

Crop production is a crucial ecosystem service that requires a combination of natural and anthropogenic contributions to high and stable yields, which is a coproduction process. We analysed this coproduction based on nationally aggregated data for 15 major crops for 67 countries and the European Union with data for four time steps (2000, 2006, 2010, 2014). We found strong increases in fertilizer use, net capital stock and manure use intensity for lower-middle-income countries and stagnation or decrease of these for high-income countries. We used a multiple linear regression model predicting yield to distinguish the effect of anthropogenic contributions (crop-specific fertilizer use intensity, net capital stock intensity, manure use intensity) and natural contributions (crop-specific agricultural suitability, including soil characteristics, topography and climate). We found that in particular fertilizer use intensity, manure use intensity and agricultural suitability explained variation in yields to a considerable degree (R2 = 0.62).

Assumptions in ecosystem service assessments: Increasing transparency for conservation.

Conservation efforts are increasingly supported by ecosystem service assessments. These assessments depend on complex multi-disciplinary methods, and rely on a number of assumptions which reduce complexity. If assumptions are ambiguous or inadequate, misconceptions and misinterpretations may arise when interpreting results of assessments. An interdisciplinary understanding of assumptions in ecosystem service science is needed to provide consistent conservation recommendations. Here, we synthesise and elaborate on 12 prevalent types of assumptions in ecosystem service assessments. These comprise conceptual and ethical foundations of the ecosystem service concept, assumptions on data collection, indication, mapping, and modelling, on socio-economic valuation and value aggregation, as well as about using assessment results for decision-making. We recommend future assessments to increase transparency about assumptions, and to test and validate them and their potential consequences on assessment reliability. This will support the taking up of assessment results in conservation science, policy and practice.

Biodiversity in European agricultural landscapes: transformative societal changes needed.

Reversing the decline of biodiversity in European agricultural landscapes is urgent. We suggest eight measures addressing politics, economics, and civil society to instigate transformative changes in agricultural landscapes. We emphasize the need for a well-informed society and political measures promoting sustainable farming by combining food production and biodiversity conservation.

How can we make progress with decision support systems in landscape and river basin management? Lessons learned from a comparative analysis of four different decision support systems.

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of 'what end-users really need and want' have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives.

Deciphering the Biodiversity-Production Mutualism in the Global Food Security Debate.

Without changes in consumption, along with sharp reductions in food waste and postharvest losses, agricultural production must grow to meet future food demands. The variety of concepts and policies relating to yield increases fail to integrate an important constituent of production and human nutrition - biodiversity. We develop an analytical framework to unpack this biodiversity-production mutualism (BPM), which bridges the research fields of ecology and agroeconomics and makes the trade-off between food security and protection of biodiversity explicit. By applying the framework, the incorporation of agroecological principles in global food systems are quantifiable, informed assessments of green total factor productivity (TFP) are supported, and possible lock-ins of the global food system through overintensification and associated biodiversity loss can be avoided.

Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis.

Meta-analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta-analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta-analyses showing that the majority of meta-analyses encountered incompletely reported studies. We then simulated meta-analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs. Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size-based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta-analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best-case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta-analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.

Identifying Agricultural Frontiers for Modeling Global Cropland Expansion.

The increasing expansion of cropland is major driver of global carbon emissions and biodiversity loss. However, predicting plausible future global distributions of croplands remains challenging. Here, we show that, in general, existing global data aligned with classical economic theories of expansion explain the current (1992) global extent of cropland reasonably well, but not recent expansion (1992-2015). Deviations from models of cropland extent in 1992 ("frontierness") can be used to improve global models of recent expansion, most likely as these deviations are a proxy for cropland expansion under frontier conditions where classical economic theories of expansion are less applicable. Frontierness is insensitive to the land cover dataset used and is particularly effective in improving models that include mosaic land cover classes and the largely smallholder-driven frontier expansion occurring in such areas. Our findings have important implications as the frontierness approach offers a straightforward way to improve global land use change models.