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Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern

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DIVERSITY AND DISTRIBUTIONS 2014年第3期20卷 334-343页

作者： Warren, Dan L. Wright, Amber N. Seifert, Stephanie N. Shaffer, H. Bradley Australian Natl Univ Div Evolut Ecol & Genet Canberra ACT 2602 Australia Univ Calif Davis Dept Ecol & Evolut Davis CA 95616 USA Univ Penn Dept Biol Philadelphia PA 19104 USA Univ Calif Los Angeles Dept Ecol & Evolutionary Biol Los Angeles CA 90095 USA Univ Calif Los Angeles Inst Environm & Sustainabil La Kretz Ctr Calif Conservat Sci Los Angeles CA 90095 USA

Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of model complexity and spatial sampling bias on niche models for 90 vertebrate taxa of conservation concern.

关键词： Climate change conservation maximum entropy model complexity niche modelling sampling bias species distribution modelling

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Groundwater recharge predictions in contrasted climate: The effect of model complexity and calibration period on recharge rates

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ENVIRONMENTAL modelLING & SOFTWARE 2018年 103卷 74-89页

作者： Moeck, Christian von Freyberg, Jana Schirmer, Mario Eawag Swiss Fed Inst Aquat Sci & Technol Water Resources & Drinking Water Uberlandstr 133 CH-8600 Dubendorf Switzerland ETH Dept Environm Syst Sci Univ Str 16 CH-8092 Zurich Switzerland Swiss Fed Res Inst WSL Mt Hydrol & Mass Movements Zurcherstr 111 CH-8903 Birmensdorf Switzerland Univ Neuchatel Ctr Hydrogeol & Geotherm CHYN Rue Emile Argand 11 CH-2000 Neuchatel Switzerland

We systematically evaluated the effect of model complexity and calibration strategy on estimated recharge using four varyingly complex models and a unique long-term recharge data set. A differential split sample test was carried out by using six calibration periods with climatically contrasting conditions in a constrained Monte Carlo approach. All models performed better during calibration than during validation due to differences in model structures and climatic conditions. The two more complex, physically-based models predicted the observed recharge with relatively small uncertainties, even when calibration and prediction periods had different climatic conditions. In contrast, the more simplistic soil-water balance model significantly underestimated the recharge rates. The fourth, semi-mechanistic model captured the observed recharge rates, but with a larger uncertainty range than the physically-based models. Our results may have relevant implications for a broad range of applications when recharge models are used as decision-making tools. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： Groundwater recharge model complexity Calibration period Split sample test Contrasted climate Droughts

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The effects of scaling and model complexity in simulating the transport of inorganic micropollutants in a lowland river reach

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WATER QUALITY RESEARCH JOURNAL OF CANADA 2006年第1期41卷 24-36页

作者： Lindenschmidt, Karl-Erich Wodrich, Rene Hesse, Cornelia Geoforschungszentrum Potsdam Sect Engn Hydrol Telegrafenberg 5 4 D-14473 Potsdam Germany Appl Univ Magdeburg Stendal D-39114 Magdeburg Germany Potsdam Inst Climate Impact Res PIK Dept Global Change & Nat Syst D-14412 Potsdam Germany

A hypothesis stating that more complex descriptions of processes in models simulate reality better (less error) but with more unreliable predictability (more sensitivity) is tested using a river water quality model. This hypothesis was extended stating that applying the model on a domain of smaller scale requires greater complexity to capture the same accuracy as in largescale model applications which, however, leads to increased model sensitivity. The sediment and pollutant transport model TOXI, a module in the WASP5 package, was applied to two case studies of different scale: a 90-km course of the 5th order (sensu Strahler 1952) lower Saale river, Germany (large scale), and the lock-and-weir system at Calbe (small scale) situated on the same river course. A sensitivity analysis of several parameters relating to the physical and chemical transport processes of suspended solids, chloride, arsenic, iron and zinc shows that the coefficient, which partitions the total heavy metal mass into its dissolved and sorbed fraction, is a very sensitive parameter. Hence, the complexity of the sorptive process was varied to test the hypotheses.

关键词： model complexity error sensitivity scale Saale river TOXI WASP5

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Estimating groundwater recharge rates in the Upper Awash Basin, Ethiopia under different combinations of model complexity and objective functions

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HYDROLOGY RESEARCH 2024年第3期55卷 263-279页

作者： Redda, Muauz Amare Birhanu, Behailu Hussien, Bediru Addis Ababa Univ Ethiopian Inst Water Resource Addis Ababa Ethiopia Addis Ababa Univ Sch Earth Sci Addis Ababa Ethiopia Addis Ababa Sci & Technol Univ Sch Earth Sci & Engn Addis Ababa Ethiopia

Reliable quantification of groundwater recharge rate is crucial for the sustainable utilization of groundwater resources. However, little information is documented about the uncertainty associated with recharge rate estimation from the different combinations of model complexity and objective function perspectives. Therefore, this study aims to (i) analyze the sensitivity of the model parameters under different combinations of model complexities and objective functions and (ii) estimate the groundwater recharge rate in the Hombele catchment, Upper Awash Basin, Ethiopia, for different combinations of objective functions and model complexities. The effect of these model complexities in estimating groundwater recharge rate and parametrizing model parameters was quantified for the period 1986-2013. A total of 10 combinations of model complexities and objective functions were used for the analysis. The Kling-Gupta efficiency (Nash-Sutcliffe efficiency) values for calibration, validation, and the whole period are 0.89 (0.80), 0.80 (0.73), and 0.87 (0.77), respectively, when a semi-distributed HBV-light model was used. For all objective functions, we found that the average annual recharge rate of the study catchment ranges from 185.9 to 280.5 mm when the HBV-light model was considered as a semi-distributed model. In contrast, the average annual recharge rate ranges from 185.3 to 321.7 mm when applying the HBV-light model as a lumped model.

关键词： groundwater recharge HBV-light model model complexity objective functions

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On hydrological model complexity, its geometrical interpretations and prediction uncertainty

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WATER RESOURCES RESEARCH 2013年第10期49卷 7048-7063页

作者： Arkesteijn, Liselot Pande, Saket Delft Univ Technol Dept Water Management NL-2628 CN Delft Netherlands

Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to minimizing an upper bound on prediction error, defined here as the mathematical expectation of empirical risk. In this paper, we derive an upper bound that is free from assumptions on data and underlying process distribution as well as on independence of model predictions over time. We demonstrate that hydrological model complexity, as defined in the presented theoretical framework, plays an important role in determining the upper bound. The model complexity also acts as a stabilizer to a hydrological model selection problem if it is deemed ill-posed. We provide an algorithm for computing complexity of any arbitrary hydrological model. We also demonstrate that hydrological model complexity has a geometric interpretation as the size of model output space. The presented theory is applied to quantify complexities of two hydrological model structures: SAC-SMA and SIXPAR. It detects that SAC-SMA is indeed more complex than SIXPAR. We also develop an algorithm to estimate the upper bound on prediction error, which is applied on five different rainfall-runoff model structures that vary in complexity. We show that a model selection problem is stabilized by regularizing it with model complexity. complexity regularized model selection yields models that are robust in predicting future but yet unseen data.

关键词： model complexity prediction uncertainty

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Investigation of the model complexity required in runoff simulation at different time scales

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2009年第5期54卷 872-885页

作者： Micovic, Zoran Quick, Michael C. British Columbia Hydro & Power Author Burnaby BC V3N 4X8 Canada Univ British Columbia Dept Civil Engn Vancouver BC V6T 1Z4 Canada

The "optimal" model complexity is defined as the minimum watershed model structure required for realistic representation of runoff processes. This paper examines the effects of model complexity at different time scales, daily and hourly. Two watershed models with different levels of complexity were constructed and their capability to simulate runoff from a watershed was evaluated. Both models were tested on the same watershed using identical meteorological input, thereby assuring that any difference between model outputs is due only to their model structure. It is demonstrated that, at a daily time scale, a simple model gives good results. For the mountain situation, in which snowmelt is a dominant influence, the nonlinearity of the runoff processes is moderate, and therefore a simple model works well. The model produced good results over a period of 28 years of continuous simulation. However, this simpler model was inadequate when tested on an hourly time scale due to greater nonlinear effects, especially when modelling high-intensity rainfall events. Therefore, the hourly simulation benefited from the more complex model structure. These model results show that optimal watershed model complexity depends on temporal resolution, namely the simulation period and the computational time step. It was shown that certain process representations and model parameters that appeared unimportant during the long-term simulation had significant effects on the short-term extreme event model simulation.

关键词： watershed model model complexity runoff simulation temporal resolution UBC watershed model

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Reducing fisheries model complexity using a finite Fourier series reparameterization

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FISHERIES RESEARCH 2007年第3期84卷 390-394页

作者： Subbey, Sam Howell, Daniel Bogstad, Bjarte Asnes, Morten Inst Marine Res N-5817 Bergen Norway

Quantifying fish stocks through modeling involves estimating time-varying parameters such as growth, stock recruitment, mortality rates and fishing levels. Each of these parameters vary non-uniformly and non-smoothly with time. For a given parameter, e.g., growth, the standard approach has been to estimate a single value of the parameter per time-step. When the modeling spans several time-steps this compounds the complexity of the model by increasing the dimension of the parameter space. This paper presents a method for reducing model complexity by effective reparameterization of selected time-varying parameters. The method involves representing the parameters by truncated (i.e., a finite) Fourier series, where the dimension of the Fourier coefficients is very much less than the number of time-steps in the modeling. Example results are presented, which show that the reparameterization can lead to over 40% decrease in the number of model parameters associated with fishing levels, growth and stock recruitment. We illustrate using time-varying parameters associated with fishing levels, growth and stock recruitment, derived from an optimized biological model. The input data for the biological model is based on landed catch-data of Northeast Arctic cod (Gadus morhua). (c) 2007 Elsevier B.V. All rights reserved.

关键词： parameter reduction fish stock model complexity Fourier series

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ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models

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METHODS IN ECOLOGY AND EVOLUTION 2014年第11期5卷 1198-1205页

作者： Muscarella, Robert Galante, Peter J. Soley-Guardia, Mariano Boria, Robert A. Kass, Jamie M. Uriarte, Maria Anderson, Robert P. Columbia Univ Dept Ecol Evolut & Environm Biol New York NY 10027 USA CUNY City Coll Dept Biol New York NY 10031 USA CUNY Grad Ctr New York NY 10016 USA Amer Museum Nat Hist Div Vertebrate Zool Mammal New York NY 10024 USA

1. Recent studies have demonstrated a need for increased rigour in building and evaluating ecological niche models (ENMs) based on presence-only occurrence data. Two major goals are to balance goodness-of-fit with model complexity (e.g. by tuning' model settings) and to evaluate models with spatially independent data. These issues are especially critical for data sets suffering from sampling bias, and for studies that require transferring models across space or time (e.g. responses to climate change or spread of invasive species). Efficient implementation of procedures to accomplish these goals, however, requires automation. We developed ENMeval, an R package that: (i) creates data sets for k-fold cross-validation using one of several methods for partitioning occurrence data (including options for spatially independent partitions), (ii) builds a series of candidate models using Maxent with a variety of user-defined settings and (iii) provides multiple evaluation metrics to aid in selecting optimal model settings. The six methods for partitioning data are n-1 jackknife, random k-folds ( = bins), user-specified folds and three methods of masked geographically structured folds. ENMeval quantifies six evaluation metrics: the area under the curve of the receiver-operating characteristic plot for test localities (AUC(TEST)), the difference between training and testing AUC (AUC(DIFF)), two different threshold-based omission rates for test localities and the Akaike information criterion corrected for small sample sizes (AICc). We demonstrate ENMeval by tuning model settings for eight tree species of the genus Coccoloba in Puerto Rico based on AICc. Evaluation metrics varied substantially across model settings, and models selected with AICc differed from default ones. In summary, ENMeval facilitates the production of better ENMs and should promote future methodological research on many outstanding issues.

关键词： ecological niche model species distribution model overfitting model complexity AIC software bioinformatics

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The benefit of data-based model complexity selection via prediction error curves in time-to-event data

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COMPUTATIONAL STATISTICS 2011年第2期26卷 293-302页

作者： Porzelius, Christine Schumacher, Martin Binder, Harald Univ Med Ctr Freiburg Inst Med Biometry & Med Informat D-79104 Freiburg Germany Univ Freiburg Freiburg Ctr Data Anal & Modeling D-79104 Freiburg Germany

The fitting of predictive survival models usually involves determination of model complexity parameters. Up to now, there was no general applicable model selection criterion for semi- or non-parametric approaches. The integrated prediction error curve, an estimator of the integrated Brier score, has the ability to close this gap and allows a reasonable, data-based choice of complexity parameters for any kind of model where risk predictions can be obtained. Random survival forests are used as example throughout the article. Here, a critical complexity parameter might be the number of candidate variables at each node. model selection by our integrated prediction error curve criterion is compared to a frequently used rule of thumb, investigating the potential benefit regarding prediction performance. For that, simulated microarray survival data as well as two real data sets of patients with diffuse large-B-cell lymphoma and of patients with neuroblastoma are used. It is shown, that the optimal parameter value depends on the amount of information in the data and that a data-based selection can therefore be beneficial in several settings.

关键词： model selection model complexity Prediction error curves Time-to-event data Random survival forests

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Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria

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ECOLOGICAL APPLICATIONS 2011年第2期21卷 335-342页

作者： Warren, Dan L. Seifert, Stephanie N. Univ Texas Austin Sect Integrat Biol Austin TX 78712 USA Univ Calif Davis Dept Entomol Davis CA 95616 USA

Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity. model complexity is typically constrained via a process known as L-1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and we compare models selected using information criteria to models selected using other criteria that are common in the literature. We evaluate model performance using occurrence data generated from a known "true" initial Maxent model, using several different metrics for model quality and transferability. We demonstrate that models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species' distributions, and reduced transferability to other time periods. We also demonstrate that information criteria may offer significant advantages over the methods commonly used in the literature.

关键词： Akaike information criterion AUC Bayesian information criterion environmental niche modeling Maxent maximum entropy model complexity model transferability niche shifts species distribution modeling

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