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  • Gardner, Emma, et al. (författare)
  • Reliably predicting pollinator abundance : Challenges of calibrating process-based ecological models
  • 2020
  • Ingår i: Methods in Ecology and Evolution. - : John Wiley and Sons. - 2041-210X. ; 11:12, s. 1673-1689
  • 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.
  • Jonsson, Mattias, et al. (författare)
  • Ecological production functions for biological control services in agricultural landscapes
  • 2014
  • Ingår i: Methods in Ecology and Evolution. - : John Wiley and Sons. - 2041-210X. ; 5:3, s. 243-252
  • 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.
  • Löfgren, Oskar, et al. (författare)
  • Landscape history confounds the ability of the NDVI to detect fine-scale variation in grassland communities
  • 2018
  • Ingår i: Methods in Ecology and Evolution. - : John Wiley and Sons. - 2041-210X. ; 9:9, s. 2009-2018
  • Tidskriftsartikel (refereegranskat)abstract
    • The NDVI is a remotely sensed vegetation index that is frequently used in ecological studies. There is, however, a lack of studies that evaluate the ability of the NDVI to detect fine-scale variation in grassland plant community composition and species richness. Ellenberg indicators characterize the environmental preferences of plant species—and community-mean Ellenberg values have been used to explore the environmental drivers of community assembly. We used variation partitioning to test the ability of satellite-based NDVI to explain community-mean Ellenberg nutrient (mN) and moisture (mF) indices, and the richness of habitat-specialist species in dry grasslands of different ages. The grasslands represent a gradient of decreasing soil nutrient status. If community composition is determined by the responses of individual species to the underlying environmental conditions and if, at the same time, community composition determines the optical characteristics of the vegetation canopy, then positive relationships between the NDVI and mN and mF are expected. Many grassland specialists are intolerant of nutrient-rich soils. If specialist richness is negatively related to soil-nutrient levels, then a negative association between the NDVI and specialist richness is expected. However, because grassland community composition is not only influenced by abiotic variables but also by other spatial and temporal drivers, we included spatial variables and grassland age in the statistical analyses. The NDVI explained the majority of the variation in mF, and also contributed to a substantial proportion of the variation in mN. However, variation in specialist richness and the lowest values of mN were explained by grassland age and spatial variables—but were poorly explained by the NDVI. Synthesis and applications. The NDVI showed a good ability to detect variation in plant community composition, and should provide a valuable tool for assessing fine-scale environmental variation in grasslands or for monitoring changes in grassland habitat properties. However, because the concentration of grassland specialists not only depends on environmental variables but also on the age and spatial context of the grasslands, the NDVI is unlikely to allow the identification of grasslands with high numbers of specialist species.
  • Moller, Anders Pape, et al. (författare)
  • Clutch-size variation in Western Palaearctic secondary hole-nesting passerine birds in relation to nest box design
  • 2014
  • Ingår i: Methods in Ecology and Evolution. - : John Wiley and Sons. - 2041-210X. ; 5:4, s. 353-362
  • Tidskriftsartikel (refereegranskat)abstract
    • Secondary hole-nesting birds that do not construct nest holes themselves and hence regularly breed in nest boxes constitute important model systems for field studies in many biological disciplines with hundreds of scientists and amateurs involved. Those research groups are spread over wide geographic areas that experience considerable variation in environmental conditions, and researchers provide nest boxes of varying designs that may inadvertently introduce spatial and temporal variation in reproductive parameters. We quantified the relationship between mean clutch size and nest box size and material after controlling for a range of environmental variables in four of the most widely used model species in the Western Palaearctic: great tit Parus major, blue tit Cyanistes caeruleus, pied flycatcher Ficedula hypoleuca and collared flycatcher F.albicollis from 365 populations and 79610 clutches. Nest floor area and nest box material varied non-randomly across latitudes and longitudes, showing that scientists did not adopt a random box design. Clutch size increased with nest floor area in great tits, but not in blue tits and flycatchers. Clutch size of blue tits was larger in wooden than in concrete nest boxes. These findings demonstrate that the size of nest boxes and material used to construct nest boxes can differentially affect clutch size in different species. The findings also suggest that the nest box design may affect not only focal species, but also indirectly other species through the effects of nest box design on productivity and therefore potentially population density and hence interspecific competition.
  • Olsson, Ola, et al. (författare)
  • Efficient, automated and robust pollen analysis using deep learning
  • 2021
  • Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons Inc.. - 2041-210X. ; 12:5, s. 850-862
  • 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.
  • Pontarp, Mikael, et al. (författare)
  • Inferring community assembly processes from macroscopic patterns using dynamic eco-evolutionary models and Approximate Bayesian Computation (ABC)
  • 2019
  • Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X .- 2041-210X. ; 10:4, s. 450-460
  • Forskningsöversikt (refereegranskat)abstract
    • Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as a mechanistic and causal link between process and pattern is often lacking, and evolutionary processes and trophic interactions are ignored.Here, we revisit the current knowledge on community assembly across scales and, in line with several reviews that have outlined challenges associated with current inference techniques, we identify a discrepancy between the current paradigm of eco-evolutionary community assembly and current inference techniques that focus mainly on competition and habitat filtering. We argue that trait-based dynamic eco-evolutionary models in combination with recently developed model fitting and model evaluation techniques can provide avenues for more accurate, reliable, and inclusive inference. To exemplify, we implement a trait-based, spatially explicit eco-evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources.Through a case study on inference of prey and predator niche width in an eco-evolutionary context, we demonstrate how inclusive and mechanistic approaches-eco-evolutionary modelling and Approximate Bayesian Computation (ABC)-can enable inference of assembly processes that have been largely neglected by traditional techniques despite the ubiquity of such processes.Much literature points to the limitations of current inference techniques, but concrete solutions to such limitations are few. Many of the challenges associated with novel inference techniques are, however, already to some extent resolved in other fields and thus ready to be put into action in a more formal way for inferring processes of community assembly from signals in various data sources.
  • Radersma, Reinder, et al. (författare)
  • A new permutation technique to explore and control for spatial autocorrelation
  • 2015
  • Ingår i: Methods in Ecology and Evolution. - : John Wiley and Sons. - 2041-210X. ; 6:9, s. 1026-1033
  • Tidskriftsartikel (refereegranskat)abstract
    • 1. Permutation tests are important in ecology and evolution as they enable robust analysis of small sample sizes and control for various forms of dependencies among observations. A common source of dependence is spatial autocorrelation. Accounting for spatial autocorrelation is often crucial, because many ecological and evolutionary processes are spatially restricted, such as gene flow, dispersal, mate choice, inter-and intraspecific competition, mutualism and predation. 2. Here we discuss various ways of controlling for spatial autocorrelation in permutation tests; we highlight their particular properties and assumptions and introduce a new permutation technique which explores and controls for spatial autocorrelation: the floating grid permutation technique (FGPT). 3. The FGPT is a method to randomize observations with known geographical locations. Within the randomization process, the probability an observation is assigned to any of the spatial locations is a negative function of the distance between its original and assigned location. The slope of this function depends on a preset parameter, and by exploring its parameter space, non-random ecological and evolutionary processes can be both assessed and controlled at multiple spatial scales. 4. We show that the FGPT has acceptable type-I-error rates. We applied the FGPT to simulated univariate and bivariate data sets in which both negative and positive spatial autocorrelation were present. In comparison with a method that uses eigenvector decomposition to separate negative from positive spatial autocorrelation, the FGPT performed better for negative spatial autocorrelation alone, equal for positive spatial autocorrelation alone and equal or slightly worse for simultaneous negative and positive spatial autocorrelation. For the bivariate data, it performed equally to a bootstrapping technique in which sampling probabilities were weighted by distance. The FGPT benefits from a large flexibility for application to bivariate (e.g. dyadic interactions) and multivariate observations (e.g. genetic marker-based relatedness measures) and has a large freedom in the choice of test statistic. It also has the potential to identify two spatial autocorrelation patterns, even if both result in positive spatial autocorrelation, given that they operate at different spatial scales. 5. The Floating Grid Permutation Technique is available as the R-package fgpt in CRAN.
  • Mukherjee, Nibedita, et al. (författare)
  • Comparison of techniques for eliciting views and judgements in decision‐making
  • 2018
  • Ingår i: Methods in Ecology and Evolution. - 2041-210X .- 2041-210X. ; 9, s. 54-63
  • Tidskriftsartikel (refereegranskat)abstract
    • 1. Decision-making is a complex process that typically includes a series of stages: identifying the issue, considering possible options, making judgements and then making a decision by combining information and values. The current status quo relies heavily on the informational aspect of decision-making with little or no emphasis on the value positions that affect decisions.2. There is increasing realization of the importance of adopting rigorous methods for each stage such that the information, views and judgements of stakeholders and experts are used in a systematic and repeatable manner. Though there are several methodological textbooks which discuss a plethora of social science techniques, it is hard to judge the suitability of any given technique for a given decision problem.3. In decision-making, the three critical aspects are “what” decision is to be made, “who” makes the decisions and “how” the decisions are made. The methods covered in this paper focus on “how” decisions can be made. We compare six techniques: Focus Group Discussion (FGD), Interviews, Q methodology, Multi-criteria Decision Analysis (MCDA), Nominal Group Technique and the Delphi technique specifically in the context of biodiversity conservation. All of these techniques (with the exception of MCDA) help in understanding human values and the underlying perspectives which shape decisions.4. Based on structured reviews of 423 papers covering all six methods, we compare the conceptual and logistical characteristics of the methods, and map their suitability for the different stages of the decision- making process. While interviews and FGD are well-known, techniques such the Nominal Group technique and Q methodology are relatively under- used. In situations where conflict is high, we recommend using the Q methodology and Delphi technique to elicit judgements. Where conflict is low, and a consensus is needed urgently, the Nominal Group technique may be more suitable.5. We present a nuanced synthesis of methods aimed at users. The comparison of the different techniques might be useful for project managers, academics or practitioners in the planning phases of their projects and help in making better informed methodological choices.
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