Process,correlation and parameter fitting in species distribution models: a response to Kriticos et al

In a recent article (Dormann et al., 2012, Journal of Biogeography, 39, 2119–2131), we compared different approaches to species distribution modelling and depicted modelling approaches along an axis from purely ‘correlative’ to ‘forward process‐based’ models. In their correspondence, Kriticos et al. (2013, Journal of Biogeography, doi: 10.1111/j.1365‐2699.2012.02791.x ) challenge this view, claiming that our continuum representation neglects differences among models and does not consider the ability of fitted process‐based models to combine the advantages of both process‐based and correlative modelling approaches. Here we clarify that the continuum view resulted from recognition of the manifold differences between models. We also reinforce the point that the current trend towards combining different modelling approaches may lead not only to the desired combination of the advantages but also to the accumulation of the disadvantages of those approaches. This point has not been made sufficiently clear previously.     

A community-based framework for aquatic ecosystem models

Here, we communicate a point of departure in the development of aquatic ecosystem models, namely a new community-based framework, which supports an enhanced and transparent union between the collective expertise that exists in the communities of traditional ecologists and model developers. Through a literature survey, we document the growing importance of numerical aquatic ecosystem models while also noting the difficulties, up until now, of the aquatic scientific community to make significant advances in these models during the past two decades. Through a common forum for aquatic ecosystem modellers we aim to (i) advance collaboration within the aquatic ecosystem modelling community, (ii) enable increased use of models for research, policy and ecosystem-based management, (iii) facilitate a collective framework using common (standardised) code to ensure that model development is incremental, (iv) increase the transparency of model structure, assumptions and techniques, (v) achieve a greater understanding of aquatic ecosystem functioning, (vi) increase the reliability of predictions by aquatic ecosystem models, (vii) stimulate model inter-comparisons including differing model approaches, and (viii) avoid ??re-inventing the wheel??, thus accelerating improvements to aquatic ecosystem models. We intend to achieve this as a community that fosters interactions amongst ecologists and model developers. Further, we outline scientific topics recently articulated by the scientific community, which lend themselves well to being addressed by integrative modelling approaches and serve to motivate the progress and implementation of an open source model framework.     

湖泊生态系统动力学模型研究进展

从系统分析在湖泊生态系统动力学研究中的作用出发,对湖泊生态系统的动力学建模过程、方法和软件等进行了总结.在此基础上,综述了国内外湖泊生态系统动力学模型的发展.从1960年代至今,湖泊生态系统动力学模型从简单的零维模型发展到复杂的水质水动力学生态综合模型和生态结构动力学模型,如LakeWeb模型.中国的湖泊生态系统动力学模型研究始于20世纪80年代,主要集中在滇池、太湖、东湖和巢湖等富营养化严重的湖泊以及其他水体.目前,已经开发一些软件用于湖泊生态系统动力学模拟,主要有CEQUALICM、WASP、AQUATOX、PAMOLARE、CAEDYM等,以及用来模拟湖泊能流的软件ECOPATH.湖泊生态系统动力学模型还在监测、数据共享和模型结构、参数选取和不确定性分析等方面存在不足,需在今后的研究中加以改进.     

Modeling ecosystem impacts of the invasive Japanese smelt Hypomesus nipponensis in Lake Erhai,southwestern China

Introductions or alien species invasions will induce changes in aquatic ecosystems but are rarely reported in Chinese highland lakes. The Japanese smelt (Hypomesus nipponensis) invaded and has become a dominant fish species in Lake Erhai, a highland lake in southwestern China, since 2016. Here, we engineered Ecopath models for two different periods, 2008–2009 (preinvasion) and 2016–2018 (postinvasion), in Lake Erhai to model ecosystem impacts from the Japanese smelt invasion. In the dynamic Ecosim model based on the 2016–2018 Ecopath model, we ran three 50-year scenarios to simulate the potential effects of Japanese smelts on the system. Our results showed competition between invasive and native species as well as changes in trophic structures, highlighting the impacts of the invasive species over time. The lake ecosystem additionally experienced significant degradation after invasion, mainly reflected in several related indicators, such as total biomass/total system throughput (TB/TST), total primary production/total biomass (TPP/TB), total primary production/total respiration (TPP/TR), Finn's mean path length (FML), Finn's cycling index (FCI) and the Connectance Index (CI). The simulation results indicated that the relative biomass of icefish (Neosalanx taihuensis), bighead carp (Hypophthalmichthys nobilis), sharpbelly (Hemiculter leucisculus), and zooplankton were significantly affected by increasing the strength of the top-down control of the Japanese smelt on its prey. It is also important to do ecological regulation of planktivorous fishes in the studied Lake Erhai, especially the Japanese smelt.     

Effects of a half a millennium winter on a deep lake – a shape of things to come?

Analyses of the effects of extreme climate periods have been used as a tool to predict ecosystem functioning and processes in a warmer world. The winter half‐year 2006/2007 (w06/07) has been extremely warm and was estimated to be a half‐a‐millennium event in central Europe. Here we analyse the consequences of w06/07 for the temperatures, mixing dynamics, phenologies and population developments of algae and daphnids (thereafter w06/07 limnology) in a deep central European lake and investigate to what extent analysis of w06/07 limnology can really be used as a predictive tool regarding future warming. Different approaches were used to put the observations during w06/07 into context: (1) a comparison of w06/07 limnology with long‐term data, (2) a comparison of w06/07 limnology with that of the preceding year, and (3) modelling of temperature and mixing dynamics using numerical experiments. These analyses revealed that w06/07 limnology in Lake Constance was indeed very special as the lake did not mix below 60 m depth throughout winter. Because of this, anomalies of variables associated strongly with mixing behaviour, e.g., Schmidt stability and a measure for phosphorus upward mixing during winter exceeded several standard deviations the long‐term mean of these variables. However, our modelling results suggest that this extreme hydrodynamical behaviour was only partially due to w06/07 meteorology per se, but depended also strongly on the large difference in air temperature to the previous cold winter which resulted in complete mixing and considerable cooling of the water column. Furthermore, modelling results demonstrated that with respect to absolute water temperatures, the model ‘w06/07’ most likely underestimates the increase in water temperature in a warmer world as one warm winter is not sufficient to rise water temperatures in a deep lake up to those expected under a future climate.     

Castles built on sand or predictive limnology in action? Part A: Evaluation of an integrated modelling framework to guide adaptive management implementation in Lake Erie

We present a technical analysis of all the recent modelling work that has been conducted to support the adaptive management process in Lake Erie; the most biologically productive system of the Great Lakes. With a wealth of models developed, Lake Erie represents a unique case study where an impressive variety of data-driven and process-based models have been developed to elucidate the major watershed and aquatic processes underlying the local water quality problems. In the Maumee River watershed, the primary contributor of total phosphorus loading (~30%) into Lake Erie, the modelling work is based on five independent applications of the same process-based model, i.e., the Soil and Water Assessment Tool (SWAT). The five SWAT models showed nearly excellent goodness-of-fit against monthly flow rates and phosphorus loading empirical estimates based on a single downstream station, but little emphasis was placed on evaluating the robustness of the hydrological or nutrient loading predictions with a finer (daily) temporal resolution, and even less so in capturing the impact of episodic/extreme precipitation events. The multi-model ensemble for the Lake Erie itself has been based on a wide range of data-driven and process-based models that span the entire complexity spectrum. Consistent with the general trend in the international modelling literature, the performance of the aquatic ecological models in Lake Erie declined from physical, chemical to biological variables. Temperature and dissolved oxygen variability were successfully reproduced, but less so the ambient nutrient levels. Model performance for cyanobacteria was inferior relative to chlorophyll a concentrations and zooplankton abundance. With respect to the projected responses of Lake Erie to nutrient loading reduction, we express our skepticism with the optimistic predictions of the extent and duration of hypoxia, given our limited knowledge of the sediment diagenesis processes in the central basin and the lack of data related to the vertical profiles of organic matter and phosphorus fractionation or sedimentation/burial rates. Our study also questions the adequacy of the coarse spatiotemporal (seasonal/annual, basin- or lake-wide) scales characterizing the philosophy of both the modelling enterprise and water quality management objectives in Lake Erie. We conclude by arguing that one of the priorities of the local research agenda must be to consolidate the ensemble character of the modelling work in Lake Erie. The wide variety of models that have been developed to understand the major causal linkages/ecosystem processes underlying the local water quality problems are a unique feature that should be cherished and further augmented.     

Pronounced genetic differentiation of small,isolated and fragmented tilapia populations inhabiting the Magadi Soda Lake in Kenya

Lake Magadi, an alkaline hypersaline lake in Kenya, is one of the most extreme water bodies known. Although its water temperatures often exceed 40°C, a particular lineage of ‘dwarf’ tilapia, Alcolapia grahami, has evolved remarkable adaptations to survive in this hostile environment. Magadi tilapia exists in small fragmented populations in isolated lagoons within Lake Magadi and its satellite Lake, Little Magadi. In spite of the potential this tilapia holds for understanding evolutionary processes in stressful environments, few genetic studies have focused on this species. We examined the genetic diversity and spatial genetic relationships of Magadi tilapia populations using microsatellite and mitochondrial markers. High levels of genetic variation were found to be supporting the hypothesis that A. grahami populations represent remnants of a much larger fish population that inhabited paleo-lake Orolonga. In contrast to previous studies, we found a well-supported genetic structure of A. grahami consisting of three differentiated genetic clusters (a) Little Magadi, (b) Fish Spring Lagoon and (c) Rest of Magadi. Given the importance of this species to the Magadi ecosystem and its potential evolutionary significance, the three genetic clusters should be considered as separate gene pools and conservation strategies aimed at protecting the species based on these clusters are recommended.     

Disentangling natural and anthropogenic drivers of changes in a shallow lake using palaeolimnology and historical archives

Shallow lakes are susceptible to catastrophic regime shifts characterised by the presence or absence or macrophytes. However, the long-term controls on macrophyte succession in shallow lakes are incompletely understood. To investigate this, we analysed multiple sediment proxies in Lake Rotcze (Eastern Poland), a small, shallow and densely macrophyte-covered lake to (1) reconstruct the ‘reference conditions’ (sensu WFD) and development of the lake in recent centuries, (2) compare historical evidence with the sedimentary record, and (3) identify the natural and anthropogenic drivers of macrophyte succession. Before the twentieth century, conditions in the lake may be referred to as ‘reference conditions’. Subsequently forest clearance in the catchment resulted in lower water transparency, but concurrent catchment drainage lowered water levels and increased macrophyte development. Since 1950 elevated nutrient supply and climatically driven increases in water levels led to the deterioration of water transparency and partial macrophyte withdrawal. At the end of the twentieth century lake-level drawdown led to low phytoplankton biomass and clear water creating a novel ecosystem where macrophytes invade the whole lake. These patterns suggest that both natural and anthropogenically induced water level fluctuations have been critical drivers of macrophyte development.     

Palaeolimnological analyses as information source for large lake biomonitoring

A routinely applicable palaeolimnological sampling and analysis programme for large lake monitoring was developed as part of the Lake Saimaa Biomonitoring Project (1990–1993). The scope of palaeolimnological analyses is to gain background information of the recent past (ca. 0–200 yr) of the lake ecosystem that is being monitored. We analyzed short sediment cores from three contrasting basins of the complex lake Saimaa in eastern Finland (nutrient-poor clearwater Lake Puruvesi; oligotrophic mesohumic Lake Paasivesi, and eutrophied and polluted mesohumic Lake Haukivesi). Each basin shows a unique developmental history under the various forms of human influence on the ecosystem. The following research approaches are evaluated in the study: echosounding, dating (²¹⁰Pb varves, soot stratigraphy), element stratigraphies, biological remains (diatoms, cladocera, chironomids, sedimentary pigments).     

Aggregated mental models predict observed outcomes following Eurasian Beaver (Castor fiber) reintroduction

Outcome prediction is important for conservation; however, analysis may be hampered by specialist resource deficiencies. Mental modelling techniques offer a potential solution, drawing on accessible sources of knowledge held informally by local stakeholders. Mental models show linked social and ecological variables from the perspectives of community members, whose insights may otherwise be neglected. Currently, an important weakness in conservation mental modelling is inadequate attention paid to real-time model predictive validity. To address this knowledge gap, baseline mental model predictions concerning Beaver (Castor fiber) reintroduction in Southwest England were followed up at three years. Participants were invited to submit outcome observations for concept variables identified in their original models, blind to inferences based on model dynamic analysis, so that the two sets of data could be compared. Individual concept values and models were found to show weak and highly inconsistent predictive validity, however, multi-stakeholder aggregated mental models showed consistently strong predictive performance. This finding was enhanced by setting tighter thresholds for inclusion of individual model items in aggregation procedures. Threshold effects can be interpreted as a reflection of greater agreement: tighter thresholds retain more highly shared model components. It is proposed that enhanced real-time predictive validity for aggregated models is explained by a ‘wisdom of the crowd’ statistical effect, analogous to well-recognised crowd judgement effects observed in relation to much simpler questions. The findings show the scope for stakeholder mental modelling methods as an investigative tool, to supplement more conventional ecosystem assessments in predicting data-poor conservation outcomes.     

An Ecosystem Model for Exploring Lake Restoration Effects on Fish Communities and Fisheries in Florida

Developing quantitative ecosystem–scale expectations of habitat restoration projects and examining trade‐offs associated with alternative approaches has been a challenge for restoration ecology. Many of the largest freshwater lake restoration projects have occurred in Florida to remediate degradation to vegetated littoral habitats resulting from stabilized water levels, but effects across lake food‐webs have not been assessed. We developed an ecosystem model using Ecopath with Ecosim and Ecospace for a generalized large, eutrophic Florida Lake to explore how simulated restoration activities could influence fish communities with emphasis on sport fish abundance. We modeled three habitat restoration scenarios: (1) “no control,” (2) a “10‐year control” that restored littoral habitat every 10 years, and (3) a “combined control” scenario that restored littoral habitat every 10 years with maintenance controls between 10‐year periods. Our “combined control” scenario provided the largest long‐term habitat restoration benefits for sport fish abundance and the fisheries they support. In Ecospace, we simulated a littoral habitat restoration project that reduced lake‐wide tussock coverage from 30 to 15%. Ecospace predicted positive benefits to sport fish and fisheries following the restoration simulation and highlighted the importance of habitat edge effects, spatial design of habitat restoration projects, and sampling designs for evaluating restoration projects.     

太湖湖滨带生态系统健康评价

根据湖滨带生态系统的特点,运用综合健康指数法建立了湖滨带生态系统健康评价体系,由目标层、准则层、指标层构成,其中准则层由湖滨带水质状况、底泥状况、植被状况、其它生物状况(浮游动物、浮游植物、底栖动物)、岸带物理状况5项组成,指标层由总氮、总磷、溶解氧、挺水植物覆盖率等15项指标构成。采用专家打分法、熵值法分别确定了准则层、指标层的权重系数。对太湖湖滨带33个点位进行了采样分析,并进行无量纲化处理后应用到所建立的评价体系中。评价结果显示33个点位中为"很健康"、"健康"、"亚健康"、"疾病"、"严重疾病"的分别占0%、24.2%、21.2%、51.5%及3.0%,也即超过一半的点位处于"疾病"状态。只有东太湖刚刚超过"健康"分数的下限,东部沿岸、贡湖、南部沿岸均处于"亚健康"状态,而梅梁湾、竺山湾、西部沿岸属于"疾病"状态,且竺山湾的生态健康状态最差。该评价结果与太湖湖滨带各分区的实际调查情况相符合,评价方法可靠性、可行性较强,可为其它湖泊湖滨带的生态系统健康评价提供一定的参照。     

基于PCA和SOM模型的龙感湖水质时空动态研究

为评估湖泊渔业模式转型阶段水环境的时空动态,选择长江中下游典型湖泊龙感湖为研究地点,于2017—2018年对该湖的黄梅水域和宿松水域进行周年季度水质监测,通过主成分分析(PCA)和自组织特征映射人工神经网络(SOM)模型定量分析了水体理化参数的时空变化特征,采用综合营养状态指数法(TLI)对水体富营养化状况进行了评价。PCA分析结果表明,龙感湖宿松水域和黄梅水域的水质差异较小,季节动态明显。全湖氨氮夏季平均浓度高达0.64 mg/L;总氮夏季平均浓度为2.30 mg/L,冬季平均浓度为1.04 mg/L;叶绿素a夏季平均含量达95.28μg/L,秋季平均浓度为28.30μg/L; pH夏季最高,达9.27;总磷冬季最高,平均为0.22 mg/L; TLI指数表明龙感湖除秋季属于轻度富营养水体外,其他3个季节均属于中度富营养状态。SOM模型结果具有可视化强的优点,能够更清晰和直观地反映龙感湖水质的时空分布动态。围栏拆除和禁渔等管理措施有助于湖泊渔业环境修复和资源恢复,建议对渔业模式转型后的湖泊生态系统变化进行长期跟踪监测评估。     

Benchmarking novel approaches for modelling species range dynamics

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process‐based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process‐based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.     

Applying ecological models to communities of genetic elements: the case of neutral theory

A promising recent development in molecular biology involves viewing the genome as a mini‐ecosystem, where genetic elements are compared to organisms and the surrounding cellular and genomic structures are regarded as the local environment. Here, we critically evaluate the prospects of ecological neutral theory (ENT), a popular model in ecology, as it applies at the genomic level. This assessment requires an overview of the controversy surrounding neutral models in community ecology. In particular, we discuss the limitations of using ENT both as an explanation of community dynamics and as a null hypothesis. We then analyse a case study in which ENT has been applied to genomic data. Our central finding is that genetic elements do not conform to the requirements of ENT once its assumptions and limitations are made explicit. We further compare this genome‐level application of ENT to two other, more familiar approaches in genomics that rely on neutral mechanisms: Kimura's molecular neutral theory and Lynch's mutational‐hazard model. Interestingly, this comparison reveals that there are two distinct concepts of neutrality associated with these models, which we dub ‘fitness neutrality’ and ‘competitive neutrality’. This distinction helps to clarify the various roles for neutral models in genomics, for example in explaining the evolution of genome size.     

A Catastrophe Model for Water Bloom Prediction: A Case Study of China's Lake Chaohu

Using a case study of Lake Chaohu, the fifth largest lake in China, we constructed a cusp model for water bloom prediction that used TP (total phosphorus), T (temperature), Chla (chlorophyll-a), and DO (dissolved oxygen). These four parameters were assumed to be the most important factors in eutrophication and water bloom of the lake. The model was found to be accurate, because its relative error was around 10%. What is more convincing, according to the catastrophe discriminant of the cusp model, it could be judged that a discontinuous jump of the aquatic ecosystem occurred in July 2004, in Lake Chaohu. This conclusion is consistent with the fact that water blooms arose in August 2004. The cusp model also showed satisfactory precision when applied to forecast the eutrophication trend and prediction of water bloom in Lake Chaohu in 2005. The case study found that water bloom brought on by eutrophication can be fit and predicted by a catastrophe model. We suggest that catastrophe models would be a constructive approach to forecast and judge the outbreak of water bloom in lakes. In addition, by constructing and studying such catastrophe models, lake managers would be able to simulate the effects of different protection and mitigation projects and enrich the scientific basis for the optimization of these projects as well.     

Empirical modelling of lake water-temperature relationships: a comparison of approaches

1.  As a result of the role that temperature plays in many aquatic processes, good predictive models of annual maximum near-surface lake water temperature across large spatial scales are needed, particularly given concerns regarding climate change. Comparisons of suitable modelling approaches are required to determine their relative merit and suitability for providing good predictions of current conditions. We developed models predicting annual maximum near-surface lake water temperatures for lakes across Canada using four statistical approaches: multiple regression, regression tree, artificial neural networks and Bayesian multiple regression.
2.  Annual maximum near-surface (from 0 to 2 m) lake water-temperature data were obtained for more than 13 000 lakes and were matched to geographic, climatic, lake morphology, physical habitat and water chemistry data. We modelled 2348 lakes and three subsets thereof encompassing different spatial scales and predictor variables to identify the relative importance of these variables at predicting lake temperature.
3.  Although artificial neural networks were marginally better for three of the four data sets, multiple regression was considered to provide the best solution based on the combination of model performance and computational complexity. Climatic variables and date of sampling were the most important variables for predicting water temperature in our models.
4.  Lake morphology did not play a substantial role in predicting lake temperature across any of the spatial scales. Maximum near-surface temperatures for Canadian lakes appeared to be dominated by large-scale climatic and geographic patterns, rather than lake-specific variables, such as lake morphology and water chemistry.     

The influence of calcium on the chlorophyll–phosphorus relationship and lake Secchi depths

The basic aim of this study was to analyse the influence of calcium on the Chl–TP relationship and to apply the findings to improve dynamic (mechanistically-based) modelling of phosphorus and lake eutrophication. We have analysed long-term data from 73 lakes. The influences of calcium found in these statistical analyses have been integrated into a dynamic foodweb model, the LakeWeb-model, which also includes a mass-balance model for phosphorus. Differences in the model outcome between simulations without and with considerations to the role of calcium are discussed. We can conclude that calcium is an important factor influencing both the Chl–TP relationship and Secchi depths in mesotrophic and eutrophic lakes. Our results also indicate that lakes with long-term median Ca-concentration between 10–30mg/l function as hardwater lakes. The results also stress the importance of taking a holistic view of lakes since the bedrock, soils and land-use activities in the catchment influence the calcium concentration in lakes and therefore the phosphorus cycle, water clarity and the productivity of a given lake. The predictive power of the Chl–TP regression increases markedly if hardwater lakes are omitted from the model domain. For lake foodweb and mass-balance modelling, we show that the inclusion of the presented calcium moderator clearly improved the predictions of lake TP-concentrations in water and sediments, chlorophyll and Secchi depths in Lake Erken, a hardwater lake in Sweden.     

Modelling as a tool to redesign livestock farming systems: a literature review

Livestock farming has recently come under close scrutiny, in response especially to environmental issues. Farmers are encouraged to redesign their livestock farming systems in depth to improve their sustainability. Assuming that modelling can be a relevant tool to address such systemic changes, we sought to answer the following question: ‘How can livestock farming systems be modelled to help farmers redesign their whole farming systems?’ To this end, we made a literature review of the models of livestock farming systems published from 2000 to mid-2009 (n = 79). We used an analysis grid based on three considerations: (i) system definition, (ii) the intended use of the model and (iii) the way in which farmers’ decision-making processes were represented and how agricultural experts and farmers were involved in the modelling processes. Consistent rationales in approaches to supporting changes in livestock farming were identified in three different groups of models, covering 83% of the whole set. These could be defined according to (i) the way in which farmers’ decisions were represented and (ii) the model's type of contribution to supporting changes. The first type gathered models that dynamically simulated the system according to different management options; the farmers’ decision-making processes are assumed to consist in choosing certain values for management factors. Such models allow long-term simulations and endorse different disciplinary viewpoints, but the farmers are weakly involved in their design. Models of the second type can indicate the best combination of farm activities under given constraints, provided the farmers’ objectives are profit maximisation. However, when used to support redesigning processes, they address neither how to implement the optimal solution nor its long-term consequences. Models of the third type enable users to dynamically simulate different options for the farming system, the management of which is assumed to be planned according to the farmers’ general objectives. Although more comprehensive, these models do not easily integrate different disciplinary viewpoints and different subsystems, which limits their usefulness as support tools for redesigning processes. Finally, we concluded about what specific requirements should be for modelling approaches if farmers were to be supported in redesigning their whole livestock farming systems using models.