| 1. |
- 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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| 2. |
- Bradter, Ute, et al.
(författare)
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Can opportunistically collected Citizen Science data fill a data gap for habitat suitability models of less common species?
- 2018
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 9, s. 1667-1678
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Tidskriftsartikel (refereegranskat)abstract
- 1. Opportunistically collected species observations contributed by volunteer reporters are increasingly available for species and regions for which systematically collected data are not available. However, it is unclear if they are suitable to produce reliable habitat suitability models (HSMs), and hence if the species-habitat relationships found and habitat suitability maps produced can be used with confidence to advice conservation management and address basic and applied research questions.2. We evaluated HSMs with opportunistically collected observations against HSMs with systematically collected observations. We enhanced the opportunistically collected presence-only data by adding inferred species absences. To obtain inferred absences, we asked individual reporters about their identification skills and if they reported certain species consistently and combined this information with their observations. We evaluated several HSM methods using a forest bird species, Siberian jay (Perisoreus infaustus), in Sweden: logistic regression with inferred absences, two versions of MaxEnt, a model combining presence-absence with presence-only observations and a Bayesian site-occupancy-detection model.3. All HSM methods produced nationwide habitat suitability maps of Siberian jay that agreed well with systematically collected observations (AUC: 086-0.88) and were very similar to a habitat suitability map produced from the HSM with systematically collected observations (Spearman rho: 0.94-0.98). At finer geographical scales there were differences among methods.4. At finer scale, the resulting habitat suitability maps from logistic regression with inferred absences agreed better with results from systematically collected observations than other methods. The species-habitat relationships found with logistic regression also agreed well with those found from systematically collected data and with prior expectations based on the species ecology.5. Synthesis and application. For many regions and species, systematically collected data are not available. By using inferred absences from high-quality, opportunistically collected contributions of few very active reporters in logistic regression we obtained HSMs that produced results similar to those from a systematic survey. Adding high-quality inferred absences to opportunistically collected data is likely possible for many less common species across various organism groups. Well-performing HSMs are important to facilitate applications such as spatial conservation planning and prioritization, monitoring of invasive species, understanding species habitat requirements or climate change studies.
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| 3. |
- Certain, Grégoire, et al.
(författare)
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How do MAR(1) models cope with hidden nonlinearities in ecological dynamics?
- 2018
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 9, s. 1975-1995
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Tidskriftsartikel (refereegranskat)abstract
- 1. Multivariate autoregressive (MAR) models are an increasingly popular technique to infer interaction strengths between species in a community and to predict the community response to environmental change. The most commonly employed MAR(1) models, with one time lag, can be viewed either as multispecies competition models with Gompertz density dependence or, more generally, as a linear approximation of more complex, nonlinear dynamics around stable equilibria. This latter interpretation allows for broader applicability, but may come at a cost in terms of interpretation of estimates and reliability of both short- and long-term predictions.2. We investigate what these costs might be by fitting MAR(1) models to simulated 2-species competition, consumer-resource and host-parasitoid systems, as well as a larger food web influenced by the environment. We review how MAR(1) coefficients can be interpreted and evaluate how reliable are estimates of interaction strength, rank, or sign; accuracy of short-term forecasts; as well as the ability of MAR(1) models to predict the long-term responses of communities submitted to environmental change such as PRESS perturbations.3. The net effects of species j on species i are usually (90%-95%) well recovered in terms of sign or rank, with the notable exception of overcompensatory dynamics. In actual values, net effects of species j on species i are not well recovered when the underlying dynamics are nonlinear. MAR(1) models are better at making short-term qualitative forecasts (next point going up or down) than at predicting long-term responses to environmental perturbations, which can be severely over- as well as underestimated.4. We conclude that when applying MAR(1) models to ecological data, inferences on net effects among species should be limited to signs, or the Gompertz assumption should be tested and discussed. This particular assumption on density-dependence (log-linearity) is also required for unbiased long-term predictions. Overall, we think that MAR(1) models are highly useful tools to resolve and characterize community dynamics, but we recommend to use them in conjunction with alternative, nonlinear models resembling the ecological context in order to improve their interpretation in specific applications.
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| 4. |
- Chapron, Guillaume
(författare)
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Complex decisions made simple: a primer on stochastic dynamic programming
- 2013
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 4, s. 872-884
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Tidskriftsartikel (refereegranskat)abstract
- 5. Stochastic dynamic programming is a powerful technique to make decisions in presence of uncertainty about biological stochastic systems changing through time. We hope this review will provide an entry point into the technical literature about SDP and will improve its application in ecology.
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| 5. |
- Cirtwill, Alyssa, et al.
(författare)
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A quantitative framework for investigating the reliability of empirical network construction
- 2019
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Ingår i: Methods in Ecology and Evolution. - : WILEY. - 2041-210X. ; 10:6, s. 902-911
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Tidskriftsartikel (refereegranskat)abstract
- Descriptions of ecological networks typically assume that the same interspecific interactions occur each time a community is observed. This contrasts with the known stochasticity of ecological communities: community composition, species abundances and link structure all vary in space and time. Moreover, finite sampling generates variation in the set of interactions actually observed. For interactions that have not been observed, most datasets will not contain enough information for the ecologist to be confident that unobserved interactions truly did not occur. Here, we develop the conceptual and analytical tools needed to capture uncertainty in the estimation of pairwise interactions. To define the problem, we identify the different contributions to the uncertainty of an interaction. We then outline a framework to quantify the uncertainty around each interaction by combining data on observed co-occurrences with prior knowledge. We illustrate this framework using perhaps the most extensively sampled network to date. We found significant uncertainty in estimates for the probability of most pairwise interactions. This uncertainty can, however, be constrained with informative priors. This uncertainty scaled up to summary measures of network structure such as connectance and nestedness. Even with informative priors, we are likely to miss many interactions that may occur rarely or under different local conditions. Overall, we demonstrate the importance of acknowledging the uncertainty inherent in network studies, and the utility of treating interactions as probabilities in pinpointing areas where more study is needed. Most importantly, we stress that networks are best thought of as systems constructed from random variables, the stochastic nature of which must be acknowledged for an accurate representation. Doing so will fundamentally change network analyses and yield greater realism.
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| 6. |
- Dettki, Holger
(författare)
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A standardisation framework for bio-logging data to advance ecological research and conservation
- 2021
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 12, s. 996-1007
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Tidskriftsartikel (refereegranskat)abstract
- Bio-logging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bio-logging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bio-logging data into research and management recommendations.We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracy-of-use, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security.We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing.Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and inter-governmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bio-logging data formats across all fields in animal ecology.
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| 7. |
- Ekström, Magnus, 1966-, et al.
(författare)
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Estimating density from presence/absence data in clustered populations
- 2020
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 11:3, s. 390-402
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Tidskriftsartikel (refereegranskat)abstract
- Inventories of plant populations are fundamental in ecological research and monitoring, but such surveys are often prone to field assessment errors. Presence/absence (P/A) sampling may have advantages over plant cover assessments for reducing such errors. However, the linking between P/A data and plant density depends on model assumptions for plant spatial distributions. Previous studies have shown, for example, how that plant density can be estimated under Poisson model assumptions on the plant locations. In this study, new methods are developed and evaluated for linking P/A data with plant density assuming that plants occur in clustered spatial patterns. New theory was derived for estimating plant density under Neyman-Scott-type cluster models such as the Matern and Thomas cluster processes. Suggested estimators, corresponding confidence intervals and a proposed goodness-of-fit test were evaluated in a Monte Carlo simulation study assuming a Matern cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empirical application. The simulation study showed that our methods work well for large enough sample sizes. The judgment of what is' large enough' is often difficult, but simulations indicate that a sample size is large enough when the sampling distributions of the parameter estimators are symmetric or mildly skewed. Bootstrap may be used to check whether this is true. The empirical results suggest that the derived methodology may be useful for estimating density of plants such as Leucanthemum vulgare and Scorzonera humilis. By developing estimators of plant density from P/A data under realistic model assumptions about plants' spatial distributions, P/A sampling will become a more useful tool for inventories of plant populations. Our new theory is an important step in this direction.
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| 8. |
- Goodman, Rosa
(författare)
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Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR
- 2018
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 9, s. 223-234
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Tidskriftsartikel (refereegranskat)abstract
- 1. Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above-ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three-dimensional (3D) tree modelling of TLS point clouds.2. Twenty-nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species-specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements.3. AGB estimates by the TLS-QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV-RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%-54.95% CV-RMSE and CCC of 0.89-0.73). TLS-QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%-35.7%) increasing linearly with tree size. The TLS-QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics.4. Our TLS-QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non-destructive method can be further used for testing and calibrating new allometric models, reducing the current under-representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests.
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| 9. |
- Göthe, Emma
(författare)
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A new paradigm for biomonitoring: an example building on the Danish Stream Plant Index
- 2017
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 8, s. 297-307
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Tidskriftsartikel (refereegranskat)abstract
- Despite intensive efforts for more than a decade to develop Water Framework-compliant assessment systems, shortcomings continue to appear. In particular, the lack of reference conditions has hindered the development of assessment systems capturing the heart of the Water Framework Directive (WFD) - that ecological status should be set as the deviation from the natural, undisturbed condition. Recently, the Danish Stream Plant Index (DSPI) was developed. This system contrasts existing systems in that it builds on an expert interpretation of the normative definitions of ecological status classes in the WFD without taking pressure-impact relationships into account. Here, we substantiate the approach taken in the development of DSPI and examine whether the DSPI class decreases with increasing level of anthropogenic stress and, additionally, whether the deviation from the natural undisturbed condition increases with decreasing DSPI class sensu WFD using trait composition of plant assemblages from Danish streams around year 1900 as a reference. We furthermore examine the trait composition of the vegetation in sites classified into different DSPI status classes to explore whether predictable patterns exist that can be used to identify the ultimate cause(s) of failure to meet ecological goals and help guide the selection of appropriate mitigation measures. We observed that DSPI declined with several parameters indicative of environmental stress in Danish streams and, furthermore, that the deviation from the natural undisturbed condition regarding the trait composition of plant communities declined with increasing DSPI, implying that the trait composition of plant communities in the high DSPI status class was most similar to those occurring in Danish streams around year 1900. We also found that trait characteristics capable of disentangling important stressors in Danish streams varied consistently among sites classified into different DSPI classes.Based on our findings, we call for new thinking. We suggest that more effort should be directed at describing reference conditions and interpreting the normative definitions of good, moderate, poor and bad instead of focusing solely on developing assessment systems using pressure-impact frameworks. We find this particularly important with respect to streams as these are seldom impacted by only a single stressor.
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| 10. |
- Hasegawa, Shun
(författare)
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Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models
- 2019
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 10, s. 146-157
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Tidskriftsartikel (refereegranskat)abstract
- Process-based models describing biogeochemical cycling are crucial tools to understanding long-term nutrient dynamics, especially in the context of perturbations, such as climate and land-use change. Such models must effectively synthesize ecological processes and properties. For example, in terrestrial ecosystems, plants are the primary source of bioavailable carbon, but turnover rates of essential nutrients are contingent on interactions between plants and soil biota. Yet, biogeochemical models have traditionally considered plant and soil communities in broad terms. The next generation of models must consider how shifts in their diversity and composition affect ecosystem processes. One promising approach to synthesize plant and soil biodiversity and their interactions into models is to consider their diversity from a functional trait perspective. Plant traits, which include heritable chemical, physical, morphological and phenological characteristics, are increasingly being used to predict ecosystem processes at a range of scales, and to interpret biodiversity-ecosystem functional relationships. There is also emerging evidence that the traits of soil microbial and faunal communities can be correlated with ecosystem functions such as decomposition, nutrient cycling, and greenhouse gas production. Here, we draw on recent advances in measuring and using traits of different biota to predict ecosystem processes, and provide a new perspective as to how biotic traits can be integrated into biogeochemical models. We first describe an explicit trait-based model framework that operates at small scales and uses direct measurements of ecosystem properties; second, an integrated approach that operates at medium scales and includes interactions between biogeochemical cycling and soil food webs; and third, an implicit trait-based model framework that associates soil microbial and faunal functional groups with plant functional groups, and operates at the Earth-system level. In each of these models, we identify opportunities for inclusion of traits from all three groups to reduce model uncertainty and improve understanding of biogeochemical cycles. These model frameworks will generate improved predictive capacity of how changes in biodiversity regulate biogeochemical cycles in terrestrial ecosystems. Further, they will assist in developing a new generation of process-based models that include plant, microbial, and faunal traits and facilitate dialogue between empirical researchers and modellers.
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