Context-oriented model validation of individual-based models in ecology: A hierarchically structured approach to validate qualitative,compositional and quantitative characteristics

Validation constitutes a vital process in model development and application, as it ensures the applicability of a model for the intended purposes and trustworthy results within the range of model assumptions. Commonly, independent empirical data sets are statistically compared with the generated model results, which is an adequate approach for models which operate on a single hierarchical level, such as most equation-based models. Individual-based models (IBM) can operate on different organisational levels synchronously and have an inherent complex and variable interaction structure for many applications. Thus a plain comparison of data congruity on the result levels might leave too many questions unanswered. However, a more comprehensive assessment of model validity can require additional investigations which encompass also qualitative and structural relationships.Here we describe a hierarchically structured validation which is oriented towards the investigated context of the model and allows organising the validation process in close relation to the different hierarchical levels which are covered in the model. The context oriented organisation protocol for validation includes the following steps: (1) assessing the different model levels separately, then, (2) applying a set of different techniques such as visual inspection, statistical comparison, involvement of experts, aggregation of data on higher integration levels and experimental validation.The context oriented approach accounts for the specificity of individual-based models – i.e., the dynamic self-organisation of model outcomes from biologically underpinned individual interactions without an inherent determination of properties on higher hierarchical levels – and extends the potential of the validation process qualitatively, as it allows to assess complex structural and causal relations and multi-level feedback processes of the developed models.     

Generalized additive models in plant ecology

Abstract. Generalized additive models (GAMs) are a non-parametric extension of generalized linear models (GLMs). They are introduced here as an exploratory tool in the analysis of species distributions with respect to climate. An important result is that the long-debated question of whether a response curve, in one dimension, is actually symmetric and bell-shaped or not, can be tested using GAMs. GAMs and GLMs are discussed and are illustrated by three examples using binary data. A grey-scale plot of one of the fits is constructed to indicate which areas on a map seem climatically suitable for that species. This is useful for species introductions. Further applications are mentioned.     

Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW

Abstract Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.     

The equilibrium assumption in estimating the parameters of metapopulation models

1.  The construction of a predictive metapopulation model includes three steps: the choice of factors affecting metapopulation dynamics, the choice of model structure, and finally parameter estimation and model testing.
2.  Unless the assumption is made that the metapopulation is at stochastic quasi-equilibrium and unless the method of parameter estimation of model parameters uses that assumption, estimates from a limited amount of data will usually predict a trend in metapopulation size.
3.  This implicit estimation of a trend occurs because extinction-colonization stochasticity, possibly amplified by regional stochasticity, leads to unequal numbers of observed extinction and colonization events during a short study period.
4.  Metapopulation models, such as those based on the logistic regression model, that rely on observed population turnover events in parameter estimation are sensitive to the implicit estimation of a trend.
5.  A new parameter estimation method, based on Monte Carlo inference for statistically implicit models, allows an explicit decision about whether metapopulation quasi-stability is assumed or not.
6. Our confidence in metapopulation model parameter estimates that have been produced from only a few years of data is decreased by the need to know before parameter estimation whether the metapopulation is in quasi-stable state or not.
7. The choice of whether metapopulation stability is assumed or not in parameter estimation should be done consciously. Typical data sets cover only a few years and rarely allow a statistical test of a possible trend. While making the decision about stability one should consider any information about the landscape history and species and metapopulation characteristics.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Trade-offs in covariate selection for species distribution models: a methodological comparison

Species distribution models (SDMs) are a common approach to describing species’ space-use and spatially-explicit abundance. With a myriad of model types, methods and parameterization options available, it is challenging to make informed decisions about how to build robust SDMs appropriate for a given purpose. One key component of SDM development is the appropriate parameterization of covariates, such as the inclusion of covariates that reflect underlying processes (e.g. abiotic and biotic covariates) and covariates that act as proxies for unobserved processes (e.g. space and time covariates). It is unclear how different SDMs apportion variance among a suite of covariates, and how parameterization decisions influence model accuracy and performance. To examine trade-offs in covariation parameterization in SDMs, we explore the attribution of spatiotemporal and environmental variation across a suite of SDMs. We first used simulated species distributions with known environmental preferences to compare three types of SDM: a machine learning model (boosted regression tree), a semi-parametric model (generalized additive model) and a spatiotemporal mixed-effects model (vector autoregressive spatiotemporal model, VAST). We then applied the same comparative framework to a case study with three fish species (arrowtooth flounder, pacific cod and walleye pollock) in the eastern Bering Sea, USA. Model type and covariate parameterization both had significant effects on model accuracy and performance. We found that including either spatiotemporal or environmental covariates typically reproduced patterns of species distribution and abundance across the three models tested, but model accuracy and performance was maximized when including both spatiotemporal and environmental covariates in the same model framework. Our results reveal trade-offs in the current generation of SDM tools between accurately estimating species abundance, accurately estimating spatial patterns, and accurately quantifying underlying species–environment relationships. These comparisons between model types and parameterization options can help SDM users better understand sources of model bias and estimate error.     

A model for range expansion of an introduced species: the common waxbill Estrilda astrild in Portugal

Abstract. The common waxbill Estrilda astrild was first introduced to Portugal from Africa in 1964, and has spread across much of the country and into Spain. We modelled the expansion of the common waxbill on a 20 × 20 km UTM grid in 4‐year periods from 1964 to 1999. The time variation of the square root of the occupied area shows that this expansion process is stabilizing in Portugal, and reasons for this are discussed. Several methods used to model biological expansions are not appropriate for the present case, because little quantitative data are available on the species ecology and because this expansion has been spatially heterogeneous. Instead, colonization on a grid was modelled as a function of several biophysical and spatio‐temporal variables through the fitting of a multivariate autologistic equation. This approach allows examination of the underlying factors affecting the colonization process. In the case of the common waxbill it was associated positively with its occurrence in adjacent cells, and affected negatively by altitude and higher levels of solar radiation.     

Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude

Aims We examine the relationships between the distribution of British ground beetle species and climatic and altitude variables with a view to developing models for evaluating the impact of climate change. Location Data from 1684 10‐km squares in Britain were used to model species–climate/altitude relationships. A validation data set was composed of data from 326 British 10‐km squares not used in the model data set. Methods The relationships between incidence and climate and altitude variables for 137 ground beetle species were investigated using logistic regression. The models produced were subjected to a validation exercise using the Kappa statistic with a second data set of 30 species. Distribution patterns for four species were predicted for Britain using the regression equations generated. Results As many as 136 ground beetle species showed significant relationships with one or more of the altitude and climatic variables but the amount of variation explained by the models was generally poor. Models explaining 20% or more of the variation in species incidence were generated for only 10 species. Mean summer temperature and mean annual temperature were the best predictors for eight and six of these 10 species respectively. Few models based on altitude, annual precipitation and mean winter temperature were good predictors of ground beetle species distribution. The results of the validation exercise were mixed, with models for four species showing good or moderate fits whilst the remainder were poor. Main conclusions Whilst there were many significant relationships between British ground beetle species distributions and altitude and climatic variables, these variables did not appear to be good predictors of ground beetle species distribution. The poor model performance appears to be related to the coarse nature of the response and predictor data sets and the absence of key predictors from the models.     

Testing alternative models for the conservation of koalas in fragmented rural–urban landscapes

Abstract Predicting the various responses of different species to changes in landscape structure is a formidable challenge to landscape ecology. Based on expert knowledge and landscape ecological theory, we develop five competing a priori models for predicting the presence/absence of the Koala (Phascolarctos cinereus) in Noosa Shire, south‐east Queensland (Australia). A priori predictions were nested within three levels of ecological organization: in situ (site level) habitat (<1 ha), patch level (100 ha) and landscape level (100–1000 ha). To test the models, Koala surveys and habitat surveys (n = 245) were conducted across the habitat mosaic. After taking into account tree species preferences, the patch and landscape context, and the neighbourhood effect of adjacent present sites, we applied logistic regression and hierarchical partitioning analyses to rank the alternative models and the explanatory variables. The strongest support was for a multilevel model, with Koala presence best predicted by the proportion of the landscape occupied by high quality habitat, the neighbourhood effect, the mean nearest neighbour distance between forest patches, the density of forest patches and the density of sealed roads. When tested against independent data (n = 105) using a receiver operator characteristic curve, the multilevel model performed moderately well. The study is consistent with recent assertions that habitat loss is the major driver of population decline, however, landscape configuration and roads have an important effect that needs to be incorporated into Koala conservation strategies.     

Phenotypic distribution models corroborate species distribution models: A shift in the role and prevalence of a dominant prairie grass in response to climate change

Phenotypic distribution within species can vary widely across environmental gradients but forecasts of species’ responses to environmental change often assume species respond homogenously across their ranges. We compared predictions from species and phenotype distribution models under future climate scenarios for Andropogon gerardii, a widely distributed, dominant grass found throughout the central United States. Phenotype data on aboveground biomass, height, leaf width, and chlorophyll content were obtained from 33 populations spanning a ~1000 km gradient that encompassed the majority of the species’ environmental range. Species and phenotype distribution models were trained using current climate conditions and projected to future climate scenarios. We used permutation procedures to infer the most important variable for each model. The species‐level response to climate was most sensitive to maximum temperature of the hottest month, but phenotypic variables were most sensitive to mean annual precipitation. The phenotype distribution models predict that A. gerardii could be largely functionally eliminated from where this species currently dominates, with biomass and height declining by up to ~60% and leaf width by ~20%. By the 2070s, the core area of highest suitability for A. gerardii is projected to shift up to ~700 km northeastward. Further, short‐statured phenotypes found in the present‐day short grass prairies on the western periphery of the species’ range will become favored in the current core ~800 km eastward of their current location. Combined, species and phenotype models predict this currently dominant prairie grass will decline in prevalence and stature. Thus, sourcing plant material for grassland restoration and forage should consider changes in the phenotype that will be favored under future climate conditions. Phenotype distribution models account for the role of intraspecific variation in determining responses to anticipated climate change and thereby complement predictions from species distributions models in guiding climate adaptation strategies.     

A plant‐traits approach to assessing the success of alien weed species in irrigated Mediterranean orchards

In addition to their impact on natural habitats, invasive alien plants can have a significant negative effect on agricultural systems and cause economic losses. Flood‐irrigated orchards in the Mediterranean Basin are vulnerable to the invasion of alien weeds, primarily because of the traditional management practices used in the orchards, which are characterized by high soil moisture during the dry summer period, nutrient availability and high levels of disturbance. This study sought to determine whether their biological traits can explain the success of alien weed species. To answer this question, 408 floristic relevés were conducted in 136 flood‐irrigated orchards on the Plains of Lleida (Catalonia, NE of Spain). Richness and cover of native and alien weeds were compared. Furthermore, a set of biological traits were compared between successful and non‐successful weeds for the whole data and separately between native and alien weeds using logistic regression and classification trees. In flood‐irrigated orchards, alien species covered most of their area, even though the richness of alien species was lower than that of the native species. The most important species were C4 species with seeds dispersed by water, and on the other hand, rosulate and caespitose‐reptant hemicryptophytes with long flowering period. Most of these traits fitted with those of the invasive alien weeds, which were mostly C4 species with seeds dispersed by water. Perennial life form characterized successful native weeds. In this study, we discuss how the traditional management of flood irrigation in fruit‐tree orchards favours invasive alien weeds that have specific traits, acting as a reservoir for the spread of alien weeds into other crops and surrounding riparian habitats. We also propose changing management practices in order to avoid the selection of alien weeds and to promote native species.     

Correlation and process in species distribution models: bridging a dichotomy

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.     

基于Logistic回归模型的黄河三角洲淡水恢复湿地大型底栖生物种群分布模拟

掌握大型底栖生物种群分布的时空变化对正确把握湿地生态修复效率、揭示湿地生态演替过程具有重要理论与实践意义。选择黄河三角洲地区一千二自然保护区的淡水恢复湿地为研究区,在2014—2015年大型底栖生物野外采样和优势物种的基础上,选择了琥珀刺沙蚕、中华蜾蠃蜚、摇蚊幼虫作为典型优势物种,构建了基于Logistic回归的淡水恢复湿地大型底栖生物种群分布模拟模型。其中,琥珀刺沙蚕和摇蚊幼虫的模拟结果较好,模拟准确率分别为84.9%和77.9%,而中华蜾蠃蜚的模拟结果不甚理想。对比生态补水前后大型底栖生物的模拟分布结果发现,琥珀刺沙蚕主要集中在潮间带区域,且在春、秋两季的生存概率分布差异不显著;而淡水恢复湿地中摇蚊幼虫的分布概率显著提高,其中高于分割值0.5的栖息面积增长了9.9—10.8倍,表明退化湿地生境正处于向淡水湿地演替进程中。