| 1. |
- Gardner, Emma, et al.
(författare)
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Reliably predicting pollinator abundance : Challenges of calibrating process-based ecological models
- 2020
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 11:12, s. 1673-1689
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Tidskriftsartikel (refereegranskat)abstract
- 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. 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. 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. 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 are 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.
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| 2. |
- Jonsson, Mattias, et al.
(författare)
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Ecological production functions for biological control services in agricultural landscapes
- 2014
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 5:3, s. 243-252
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Tidskriftsartikel (refereegranskat)abstract
- Research relating to ecosystem services has increased, partly because of drastic declines in biodiversity in agricultural landscapes. However, the mechanistic linkages between land use, biodiversity and service provision are poorly understood and synthesized. This is particularly true for many ecosystem services provided by mobile organisms such as natural enemies to crop pests. These species are not only influenced by local land use but also by landscape composition at larger spatial scales. We present a conceptual ecological production function framework for predicting land-use impact on biological control of pests by natural enemies. We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45-70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization. We encourage scientists working with biological control to adopt the conceptual framework presented here and to develop production functions for other crop-pest systems. If this kind of ecological production function is combined with production functions for other services, the joint model will be a powerful tool for managing ecosystem services and planning for sustainable agriculture at the landscape scale.
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| 3. |
- Olsson, Ola, et al.
(författare)
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Efficient, automated and robust pollen analysis using deep learning
- 2021
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 12:5, s. 850-862
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Tidskriftsartikel (refereegranskat)abstract
- Pollen analysis is an important tool in many fields, including pollination ecology, paleoclimatology, paleoecology, honey quality control, and even medicine and forensics. However, labour‐intensive manual pollen analysis often constrains the number of samples processed or the number of pollen analysed per sample. Thus, there is a desire to develop reliable, high‐throughput, automated systems. We present an automated method for pollen analysis, based on deep learning convolutional neural networks (CNN). We scanned microscope slides with fuchsine stained, fresh pollen and automatically extracted images of all individual pollen grains. CNN models were trained on reference samples (122,000 pollen grains, from 347 flowers of 83 species of 17 families). The models were used to classify images of different pollen grains in a series of experiments. We also propose an adjustment to reduce overestimation of sample diversity in cases where samples are likely to contain few species. Accuracy of a model for 83 species was 0.98 when all samples of each species were first pooled, and then split into a training and a validation set (splitting experiment). However, accuracy was much lower (0.41) when individual reference samples from different flowers were kept separate, and one such sample was used for validation of models trained on remaining samples of the species (leave‐one‐out experiment). We therefore combined species into 28 pollen types where a new leave‐one‐out experiment revealed an overall accuracy of 0.68, and recall rates >0.90 in most pollen types. When validating against 63,650 manually identified pollen grains from 370 bumblebee samples, we obtained an accuracy of 0.79, but our adjustment procedure increased this to 0.85. Validation through splitting experiments may overestimate robustness of CNN pollen analysis in new contexts (samples). Nevertheless, our method has the potential to allow large quantities of real pollen data to be analysed with reasonable accuracy. Although compiling pollen reference libraries is time‐consuming, this is simplified by our method, and can lead to widely accessible and shareable resources for pollen analysis.
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| 4. |
- Sahlin, Ullrika, et al.
(författare)
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A benefit analysis of screening for invasive species - base-rate uncertainty and the value of information
- 2011
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 2:5, s. 500-508
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Tidskriftsartikel (refereegranskat)abstract
- 1.. Implementation of the full spectra of screening tools to prevent the introduction of invasive species results in a need to evaluate the cost-efficiency of gathering the information needed to screen for these species. 2. We show how the Bayesian value of information approach can be used to derive the benefit of a screening model based on species traits, which in combination with the base rate of invasiveness, i.e. the proportion of invasive species among those introduced and established, predicts species-specific invasiveness. 3. A pre-posterior Bayesian analysis demonstrated that the benefit of the screening model of invasiveness depends on both the accuracy in predictions and the uncertainty in the base rate of invasiveness. However, even though increasing model accuracy always generates higher model benefit, acknowledging or neglecting the uncertainty in the base rate of invasiveness does not. This means that uncertainty in the base rate is important to consider in the cost-benefit analysis of the screening model. 4. As an example, we derived the benefit of basing decisions on a screening model trained for a data set on species traits of invasive and non-invasive marine macroalgae introduced into Europe. The benefit ranged from 0.6% to 19% of the loss of introducing an invasive species, where the actual value can be estimated if we know the monetary values of impacts from introducing invasive and not introducing non-invasive species. 5. Cost-benefit analyses of screening models for invasive species is one means to reach efficient management of the risks of non-indigenous species. Value of information is a useful tool for benefit analysis of predictive models with respect to decision-making, which goes beyond the investigations of model accuracy. Here, we use value of information analysis to evaluate which sources of uncertainty that is most worth while to reduce and how to set the cost of gathering further species-specific information which will improve the accuracy of a screening.
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