Pollination is a well-studied and at the same time a threatened ecosystem service. A significant part of global crop production depends on or profits from pollination by animals. Using detailed ...information on global crop yields of 60 pollination dependent or profiting crops, we provide a map of global pollination benefits on a 5' by 5' latitude-longitude grid. The current spatial pattern of pollination benefits is only partly correlated with climate variables and the distribution of cropland. The resulting map of pollination benefits identifies hot spots of pollination benefits at sufficient detail to guide political decisions on where to protect pollination services by investing in structural diversity of land use. Additionally, we investigated the vulnerability of the national economies with respect to potential decline of pollination services as the portion of the (agricultural) economy depending on pollination benefits. While the general dependency of the agricultural economy on pollination seems to be stable from 1993 until 2009, we see increases in producer prices for pollination dependent crops, which we interpret as an early warning signal for a conflict between pollination service and other land uses at the global scale. Our spatially explicit analysis of global pollination benefit points to hot spots for the generation of pollination benefits and can serve as a base for further planning of land use, protection sites and agricultural policies for maintaining pollination services.
Summary
Ecological networks are often composed of different subcommunities (often referred to as modules). Identifying such modules has the potential to develop a better understanding of the assembly ...of ecological communities and to investigate functional overlap or specialization.
The most informative form of networks are quantitative or weighted networks. Here, we introduce an algorithm to identify modules in quantitative bipartite (or two‐mode) networks. It is based on the hierarchical random graphs concept of Clauset et al. (2008 Nature 453: 98–101) and is extended to include quantitative information and adapted to work with bipartite graphs. We define the algorithm, which we call QuanBiMo, sketch its performance on simulated data and illustrate its potential usefulness with a case study.
Modules are detected with a higher accuracy in simulated quantitative networks than in their binary counterparts. Even at high levels of noise, QuanBiMo still classifies 70% of links correctly as within‐ or between‐modules. Recursively applying the algorithm results in additional information of within‐module organization of the network.
The algorithm introduced here must be seen as a considerable improvement over the current standard of algorithms for binary networks. Due to its higher sensitivity, it is likely to lead to be useful for detecting modules in the typically noisy data of ecological networks.
Ecological networks depict the interactions between species, mainly based on observations in the field. The information contained in such interaction matrices depends on the sampling design, and ...typically, compounds preferences (specialization) and abundances (activity). Null models are the primary vehicles to disentangle the effects of specialization from those of sampling and abundance, but they ignore the feedback of network structure on abundances. Hence, network structure, as exemplified here by modularity, is difficult to link to specific causes. Indeed, various processes lead to modularity and to specific interaction patterns more generally. Inferring (co)evolutionary dynamics is even more challenging, as competition and trait matching yield identical patterns of interactions. A satisfactory resolution of the underlying factors determining network structure will require substantial additional information, not only on independently assessed abundances, but also on traits, and ideally on fitness consequences as measured in experimental setups.
Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of ...digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically‐based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta‐analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web‐based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.
Solitary bees are important but declining wild pollinators. During daily foraging in agricultural landscapes, they encounter a mosaic of patches with nest and foraging habitat and unsuitable matrix. ...It is insufficiently clear how spatial allocation of nesting and foraging resources and foraging traits of bees affect their daily foraging performance. We investigated potential brood cell construction (as proxy of fitness), number of visited flowers, foraging habitat visitation and foraging distance (pollination proxies) with the model SOLBEE (simulating pollen transport by solitary bees, tested and validated in an earlier study), for landscapes varying in landscape fragmentation and spatial allocation of nesting and foraging resources. Simulated bees varied in body size and nesting preference. We aimed to understand effects of landscape fragmentation and bee traits on bee fitness and the pollination services bees provide, as well as interactions between them, and the general consequences it has to our understanding of the system. This broad scope gives multiple key results. 1) Body size determines fitness more than landscape fragmentation, with large bees building fewer brood cells. High pollen requirements for large bees and the related high time budgets for visiting many flowers may not compensate for faster flight speeds and short handling times on flowers, giving them overall a disadvantage compared to small bees. 2) Nest preference does affect distribution of bees over the landscape, with cavity-nesting bees being restricted to nesting along field edges, which inevitably leads to performance reductions. Fragmentation mitigates this for cavity-nesting bees through increased edge habitat. 3) Landscape fragmentation alone had a relatively small effect on all responses. Instead, the local ratio of nest to foraging habitat affected bee fitness positively through reduced local competition. The spatial coverage of pollination increases steeply in response to this ratio for all bee sizes. The nest to foraging habitat ratio, a strong habitat proxy incorporating fragmentation could be a promising and practical measure for comparing landscape suitability for pollinators. 4) The number of flower visits was hardly affected by resource allocation, but predominantly by bee size. 5) In landscapes with the highest visitation coverage, bees flew least far, suggesting that these pollination proxies are subject to a trade-off between either longer pollen transport distances or a better pollination coverage, linked to how nests are distributed over the landscape rather than being affected by bee size.
Aim Spatial autocorrelation (SAC) in data, i.e. the higher similarity of closer samples, is a common phenomenon in ecology. SAC is starting to be considered in the analysis of species distribution ...data, and over the last 10 years several studies have incorporated SAC into statistical models (here termed 'spatial models'). Here, I address the question of whether incorporating SAC affects estimates of model coefficients and inference from statistical models. Methods I review ecological studies that compare spatial and non-spatial models. Results In all cases coefficient estimates for environmental correlates of species distributions were affected by SAC, leading to a mis-estimation of on average c. 25%. Model fit was also improved by incorporating SAC. Main conclusions These biased estimates and incorrect model specifications have implications for predicting species occurrences under changing environmental conditions. Spatial models are therefore required to estimate correctly the effects of environmental drivers on species present distributions, for a statistically unbiased identification of the drivers of distribution, and hence for more accurate forecasts of future distributions.
Biodiversity is important for many ecosystem processes. Global declines in pollinator diversity and abundance have been recognized, raising concerns about a pollination crisis of crops and wild ...plants. However, experimental evidence for effects of pollinator species diversity on plant reproduction is extremely scarce. We established communities with 1-5 bee species to test how seed production of a plant community is determined by bee diversity. Higher bee diversity resulted in higher seed production, but the strongest difference was observed for one compared to more than one bee species. Functional complementarity among bee species had a far higher explanatory power than bee diversity, suggesting that additional bee species only benefit pollination when they increase coverage of functional niches. In our experiment, complementarity was driven by differences in flower and temperature preferences. Interspecific interactions among bee species contributed to realized functional complementarity, as bees reduced interspecific overlap by shifting to alternative flowers in the presence of other species. This increased the number of plant species visited by a bee community and demonstrates a new mechanism for a biodiversity-function relationship ("interactive complementarity"). In conclusion, our results highlight both the importance of bee functional diversity for the reproduction of plant communities and the need to identify complementarity traits for accurately predicting pollination services by different bee communities.
AIM: Species distribution models (SDMs) are common tools in biogeography and conservation ecology. It has been repeatedly claimed that aggregated (stacked) SDMs (S‐SDMs) will overestimate species ...richness. One recently suggested solution to this problem is to use macroecological models of species richness to constrain S‐SDMs. Here, we examine current practice in the development of S‐SDMs to identify methodological problems, provide tools to overcome these issues, and quantify the performance of correctly stacked S‐SDMs alongside macroecological models. LOCATIONS: Barents Sea, Europe and Dutch Wadden Sea. METHODS: We present formal mathematical arguments demonstrating how S‐SDMs should and should not be stacked. We then compare the performance of macroecological models and correctly stacked S‐SDMs on the same data to determine if the former can be used to constrain the latter. Next, we develop a maximum‐likelihood approach to adjusting S‐SDMs and discuss how it could potentially be used in combination with macroecological models. Finally, we use this tool to quantify how S‐SDMs deviate from observed richness in four very different case studies. RESULTS: We demonstrate that stacking methods based on thresholding site‐level occurrence probabilities will almost always be biased, and that these biases will tend toward systematic overprediction of richness. Next, we show that correctly stacked S‐SDMs perform very similarly to macroecological models in that they both have a tendency to overpredict richness in species‐poor sites and underpredict it in species‐rich sites. MAIN CONCLUSIONS: Our results suggest that the perception that S‐SDMs consistently overpredict richness is driven largely by incorrect stacking methods. With these biases removed, S‐SDMs perform similarly to macroecological models, suggesting that combining the two model classes will not offer much improvement. However, if situations where coupling S‐SDMs and macroecological models would be beneficial are subsequently identified, the tools we develop would facilitate such a synthesis.
1. Ecosystem services are defined as the benefits that humans obtain from ecosystems. Employing the ecosystem service concept is intended to support the development of policies and instruments that ...integrate social, economic and ecological perspectives. In recent years, this concept has become the paradigm of ecosystem management. 2. The prolific use of the term 'ecosystem services' in scientific studies has given rise to concerns about its arbitrary application. A quantitative review of recent literature shows the diversity of approaches and uncovers a lack of consistent methodology. 3. From this analysis, we have derived four facets that characterise the holistic ideal of ecosystem services research: (i) biophysical realism of ecosystem data and models; (ii) consideration of local trade-offs; (iii) recognition of off-site effects; and (iv) comprehensive but critical involvement of stakeholders within assessment studies. 4. These four facets should be taken as a methodological blueprint for further development and discussion. They should critically reveal and elucidate what may often appear to be ad-hoc approaches to ecosystem service assessments. 5. Synthesis and applications: Based on this quantitative review, we provide guidelines for further development and discussions supporting consistency in applications of the ecosystem service concept as well as the credibility of results, which in turn can make it easier to generalise from the numerous individual studies.
Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for ...parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘ folk lore ’ -thresholds of correlation coefficients between predictor variables of |r| > 0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
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