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  • 1
    Language: English
    In: Global change biology, 2016-01, Vol.22 (1), p.137-150
    Description: Recognition of the importance of intraspecific variation in ecological processes has been growing, but empirical studies and models of global change have only begun to address this issue in detail. This review discusses sources and patterns of intraspecific trait variation and their consequences for understanding how ecological processes and patterns will respond to global change. We examine how current ecological models and theories incorporate intraspecific variation, review existing data sources that could help parameterize models that account for intraspecific variation in global change predictions, and discuss new data that may be needed. We provide guidelines on when it is most important to consider intraspecific variation, such as when trait variation is heritable or when nonlinear relationships are involved. We also highlight benefits and limitations of different model types and argue that many common modeling approaches such as matrix population models or global dynamic vegetation models can allow a stronger consideration of intraspecific trait variation if the necessary data are available. We recommend that existing data need to be made more accessible, though in some cases, new experiments are needed to disentangle causes of variation.
    Subject(s): species range ; population differentiation ; global change ; intraspecific variation ; genetic variation ; trait ; evolution ; population dynamics ; Biological Evolution ; Genetic Variation ; Models, Theoretical ; Phenotype ; Climate Change ; Epigenesis, Genetic ; Ecological and Environmental Phenomena ; Index Medicus
    ISSN: 1354-1013
    E-ISSN: 1365-2486
    Source: Alma/SFX Local Collection
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 2
    Language: English
    In: Diversity & distributions, 2021-03-01, Vol.27 (3), p.392-401
    Description: Aim Empirical studies have often reported parallel patterns of genetic and species diversity, but the strength and generality of this association, as well as its origin, are still debated. Particularly in human‐dominated landscapes with complex histories of land use histories, more complicated and partly diverging patterns have been observed. In this study, we examine whether species and genetic diversity correlate across grasslands with different levels of land use pressure and spatial differentiation in habitat quality and heterogeneity. Location We selected eight extensively used (grazed, unfertilized) dry grasslands and eight intensively used (mown, fertilized) hay meadows in southeastern Germany. Methods We used vegetation surveys and molecular markers of six widespread dry grassland and six hay meadow plant species to compare species and genetic alpha and beta diversity between the two grassland types. Results Species diversity patterns expectedly showed higher alpha diversity, stronger spatial structure and less turnover in dry grasslands than in hay meadows. Neither of the corresponding genetic diversity patterns showed the same significant trends. Main conclusion Our results question the idea that species and genetic diversity patterns will always show similar patterns. Likely, genetic and species diversity emerge partly from shared, partly from different processes, including the regional species pool, environmental heterogeneity, fragmentation and land use history. The practical conservation implication is that species and genetic diversity are not generally interchangeable. Looking at species and genetic patterns together, however, may eventually lead to a better understanding of the complex processes that shape the structure and dynamics of ecological communities.
    Subject(s): BIODIVERSITY RESEARCH ; genetic diversity ; land use ; grazing ; hay meadow ; mowing ; species diversity ; dry grassland ; species genetic diversity correlation ; Genetic research ; Grasslands ; Wildlife conservation ; Land use ; Biological diversity
    ISSN: 1366-9516
    E-ISSN: 1472-4642
    Source: Academic Search Ultimate
    Source: ProQuest Central
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  • 3
    Language: English
    In: Ecography (Copenhagen), 2017-02, Vol.40 (2), p.267-280
    Description: Macroecology and biogeography are concerned with understanding biodiversity patterns across space and time. In the past, the two disciplines have addressed this question mainly with correlative approaches, despite frequent calls for more mechanistic explanations. Recent advances in computational power, theoretical understanding, and statistical tools are, however, currently facilitating the development of more system‐oriented, mechanistic models. We review these models, identify different model types and theoretical frameworks, compare their processes and properties, and summarize emergent findings. We show that ecological (physiology, demographics, dispersal, biotic interactions) and evolutionary processes, as well as environmental and human‐induced drivers, are increasingly modelled mechanistically; and that new insights into biodiversity dynamics emerge from these models. Yet, substantial challenges still lie ahead for this young research field. Among these, we identify scaling, calibration, validation, and balancing complexity as pressing issues. Moreover, particular process combinations are still understudied, and so far models tend to be developed for specific applications. Future work should aim at developing more flexible and modular models that not only allow different ecological theories to be expressed and contrasted, but which are also built for tight integration with all macroecological data sources. Moving the field towards such a ‘systems macroecology’ will test and improve our understanding of the causal pathways through which eco‐evolutionary processes create diversity patterns across spatial and temporal scales.
    Subject(s): Biogeography ; Models ; Computer-generated environments ; Computer simulation ; Analysis
    ISSN: 0906-7590
    E-ISSN: 1600-0587
    Source: Directory of Open Access Journals
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  • 4
    Language: English
    In: Ecology letters, 2011-08, Vol.14 (8), p.816-827
    Description: Ecology Letters (2011) 14: 816–827 Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity. Many important systems in ecology and biology, however, are difficult to capture with statistical models. Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well‐established statistical theory such as maximum likelihood and Bayesian statistics. A number of new methods, among them Approximate Bayesian Computing and Pattern‐Oriented Modelling, bypass this limitation. These methods share three main principles: aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling. We discuss principles as well as advantages and caveats of these methods, and demonstrate their potential for integrating stochastic simulation models into a unified framework for statistical modelling.
    Subject(s): inverse modelling ; likelihood‐free inference ; likelihood approximation ; Bayesian statistics ; indirect inference ; maximum likelihood ; model selection ; parameter estimation ; stochastic simulation ; intractable likelihood ; Fundamental and applied biological sciences. Psychology ; General aspects ; Animal, plant and microbial ecology ; General aspects. Techniques ; Biological and medical sciences ; Methods and techniques (sampling, tagging, trapping, modelling...) ; Animal and plant ecology ; Markov Chains ; Algorithms ; Computer Simulation ; Ecosystem ; Bayes Theorem ; Statistics, Nonparametric ; Models, Statistical ; Monte Carlo Method ; Computer simulation ; Models ; Computer-generated environments ; Index Medicus
    ISSN: 1461-023X
    E-ISSN: 1461-0248
    Source: Alma/SFX Local Collection
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 5
    Language: English
    In: Science (American Association for the Advancement of Science), 2018-05-25, Vol.360 (6391), p.eaar2435
    Description: LaManna (Reports, 30 June 2017, p. 1389) found higher conspecific negative density dependence in tree communities at lower latitudes, yielding a possible mechanistic explanation for the latitudinal diversity gradient. We show that their results are artifacts of a selective data transformation and a forced zero intercept in their fitted model. A corrected analysis shows no latitudinal trend.
    Subject(s): Index Medicus
    ISSN: 0036-8075
    E-ISSN: 1095-9203
    Source: Single Journals
    Source: Academic Search Ultimate
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 6
    Language: English
    In: Ecography (Copenhagen), 2017-08, Vol.40 (8), p.913-929
    Description: Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross‐validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross‐validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non‐causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross‐validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non‐random and blocked cross‐validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross‐validation is nearly universally more appropriate than random cross‐validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross‐validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
    Subject(s): Phylogeny
    ISSN: 0906-7590
    E-ISSN: 1600-0587
    Source: Directory of Open Access Journals
    Source: Alma/SFX Local Collection
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  • 7
    Language: English
    In: Journal of biogeography, 2012-12-01, Vol.39 (12), p.2119-2131
    Description: Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative—process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process—correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.
    Subject(s): Climate change ; Conservation biology ; Ecological modeling ; Correlatives ; Climate models ; Species ; Modeling ; Parametric models ; Applied ecology ; Environmental conservation ; process‐based model ; parameterization ; Hypothesis generation ; species distribution model ; mechanistic model ; uncertainty ; SDM ; validation ; Biogeochemistry ; Biological diversity conservation ; Forest ecology ; Models ; Universities and colleges ; Biometry ; Biological diversity
    ISSN: 0305-0270
    E-ISSN: 1365-2699
    Source: Alma/SFX Local Collection
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  • 8
    Language: English
    In: Environmental toxicology and chemistry, 2020-11, Vol.39 (11), p.2109-2123
    Description: Current regulatory guidelines for pesticide risk assessment recommend that nonsignificant results should be complemented by the minimum detectable difference (MDD), a statistical indicator that is used to decide whether the experiment could have detected biologically relevant effects. We review the statistical theory of the MDD and perform simulations to understand its properties and error rates. Most importantly, we compare the skill of the MDD in distinguishing between true and false negatives (i.e., type II errors) with 2 alternatives: the minimum detectable effect (MDE), an indicator based on a post hoc power analysis common in medical studies; and confidence intervals (CIs). Our results demonstrate that MDD and MDE only differ in that the power of the MDD depends on the sample size. Moreover, although both MDD and MDE have some skill in distinguishing between false negatives and true absence of an effect, they do not perform as well as using CI upper bounds to establish trust in a nonsignificant result. The reason is that, unlike the CI, neither MDD nor MDE consider the estimated effect size in their calculation. We also show that MDD and MDE are no better than CIs in identifying larger effects among the false negatives. We conclude that, although MDDs are useful, CIs are preferable for deciding whether to treat a nonsignificant test result as a true negative, or for determining an upper bound for an unknown true effect. Environ Toxicol Chem 2020;39:2109–2123. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. In ecotoxicological risk assessment, the minimum detectable difference (MDD) is used as a secondary filter to check whether nonsignificant test results are based on sufficient power and should thus be trusted. In our review, we show that the MDD has some skill in distinguishing between true absence of effects and false negatives (type II errors), but that confidence intervals (CIs) clearly outperform MDDs in this task, and should thus be preferred. MDE = minimum detectable effect; pCI = proportional upper bound of the confidence interval; pMDD = proportional MDD; pMDE = proportional MDE.
    Subject(s): Least significant difference ; Minimum detectable change ; Post hoc power ; Risk assessment ; Minimum significant difference ; Statistical ecotoxicology ; Confidence Intervals ; Statistics as Topic ; Pesticides - analysis ; Risk Assessment ; Computer Simulation ; Endpoint Determination ; Index Medicus
    ISSN: 0730-7268
    E-ISSN: 1552-8618
    Source: Alma/SFX Local Collection
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 9
    Language: English
    In: Biodiversity and conservation, 2018-07, Vol.27 (8), p.2021-2028
    Description: Assessing local population size is one of the most common tasks in biodiversity monitoring. Population size estimates are not only important for conservation management and population threat assessment; they also enter many other analyses in landscape ecology and conservation. It is therefore concerning that methods for estimating plant population sizes are not standardized. We surveyed the literature and found that the most commonly used methods are counting either all or only flowering individuals on a site, as well as counting individuals in random plots or plots on transects. Sometimes, these methods are combined in the same study, without assurance that they produce comparable results. We therefore conducted a field study, in which we obtained population size estimates from all four methods for six different calcareous grassland species at 18 study sites. Our results demonstrate not only substantial differences between overall count rates generated by the different methods, but methods that surveyed the whole population also systematically yielded lower counts when species were less visible and when the area was larger, suggesting that these methods suffer from biases that could distort species and site comparisons. We conclude that estimates from different methods should not be mixed, and that plot or transect based surveys have likely smaller biases for large areas or poorly visible individuals, and are therefore preferable.
    Subject(s): Population ecology ; Vegetation monitoring ; Climate Change/Climate Change Impacts ; Conservation ; Plots ; Vegetation surveys ; Ecology ; Biodiversity ; Life Sciences ; Field methods ; Conservation Biology/Ecology ; Counting ; Plant population size ; Plants ; Plant populations ; Comparative analysis ; Methods
    ISSN: 0960-3115
    E-ISSN: 1572-9710
    Source: Alma/SFX Local Collection
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  • 10
    Language: English
    In: Methods in ecology and evolution, 2020-11, Vol.11 (11), p.1470-1475
    Description: Process‐based forest models (PBMs) are important tools for quantifying forest growth and vulnerability, particularly under climate change. The 3‐PG model (Physiological Processes Predicting Growth) is one of the most widely used forest growth simulators for this purpose worldwide. Here, we present r3PG, a new Fortran implementation of 3‐PG, wrapped into an r package. r3PG can simulate monospecific as well as mixtures of evergreen and deciduous tree species in even‐aged or uneven‐aged stands. The combination of Fortran functions with an r interface makes the model extremely fast. This facilitates the use of r3PG for extensive computer experiments and sensitivity analysis. We demonstrate this in a case study including (a) single model runs; (b) a sensitivity analysis and a full Bayesian calibration of the model and (c) spatial simulations of forest growth across Switzerland. r3PG is faster and easier to use than previous implementations of 3‐PG in visual basic. We believe that this will make 3‐PG even more useful and popular for ecologists and climate change scientists.
    Subject(s): 3‐PGmix ; forest productivity ; model calibration ; forest biomass ; 3‐PGpjs
    ISSN: 2041-210X
    E-ISSN: 2041-210X
    Source: Wiley Online Library All Journals
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