Advanced Modelling Techniques Studying Global Changes in Environmental Sciences

By Elsevier Science

Advanced Modelling Techniques Studying Global Changes in Environmental Sciences discusses the need for immediate and effective action, guided by a scientific understanding of ecosystem function, to alleviate current pressures on the environment.

Research, especially in Ecological Modeling, is crucial to support the sustainable development paradigm, in which the economy, society, and the environment are integrated and positively reinforce each other.

Content from this book is drawn from the 2013 conference of the International Society for Ecological Modeling (ISEM), an important and active research community contributing to this arena.

Some progress towards gaining a better understanding of the processes of global change has been achieved, but much more is needed. This conference provides a forum to present current research using models to investigate actions towards mitigating and adapting to change.

• Presents state-of-the-art modeling techniques
• Drawn from the 2013 conference of the International Society for Ecological Modeling (ISEM), an important and active research community contributing to this arena
• Integrates knowledge of advanced modeling techniques in ecological and environmental sciences
• Describes new applications for sustainability

• Language: English
• Publisher: Elsevier Science
• Release date: Oct 8, 2015
• ISBN: 9780444635433

Book preview

Preface

The nineteenth global conference of the International Society for Ecological Modelling (ISEM) was hosted by the Université de Toulouse and Météo France in Toulouse, France, from October 28 to 31, 2013. The conference was titled Ecological Modelling of Ecosystem Sustainability in the context of Global Change. This name implies that the core topics were new ecological modeling techniques and new model types and applications, particularly for modeling ecosystem sustainability and the ecological consequences of climatic and global changes. Selected conference papers covering these themes are published in this book, and Ecological Modelling, Volume 306, offers many other relevant papers presented at the conference.

We live in a changing world, and without immediate and effective action, our planet will face unyielding pressure on the environment, as has been discussed again and again since the first Limits to Growth book was published in 1972. In spite of the warnings from this famous book published by the Club of Rome, not much action has been taken in the political arena over the last 40 years. There is a growing understanding that society could imitate the methods that nature is using to maintain sustainability, however. Therefore, action needs to be guided by a scientific and holistic understanding of ecosystem function. Research, especially in ecological modeling and systems ecology, is thus crucial to support the sustainable development paradigm, in which the economy, society, and environment are integrated and positively reinforce each other. The nineteenth global ISEM conference contributed to this development, which will require many small steps in the right direction before our interactions with mother Earth have achieved a sustainable level. Some progress toward gaining a better understanding of the processes of global change was achieved at the nineteenth global ISEM conference, but much more is needed, due to the many global problems that we are confronting.

The Chapters 2 to 4 cover new modeling techniques, model types, and applications that can help us in our effort to investigate the changes in ecosystems resulting from climatic shifts and other radical changes, because these factors will inevitably change ecosystem structure, function, and networks. We are now able, to certain extent, to model these changes, in part, by using new model types and techniques.

A review of Ecological Modelling, ISEM's official scientific journal, reveals that we have been able to widen our model results by including not only spatial distribution, but also species distribution and species sensitivity distribution. In this way, it has been possible to improve ecosystem risk assessments. These new developments in modeling are presented in Chapters 5 to 8.

Climate changes have created a growing interest in forest ecosystems, agricultural ecosystems, and urban ecosystems, because these ecosystems are expected to be particularly sensitive to such changes. Therefore, it is important to gain increasing experience in the development of models for these three types of ecosystems, particularly under the conditions of a changing climate. Chapters 9 to 11 cover the latest progress in modeling these three ecosystems, particularly given changing (growing) climatic impacts.

The last four chapters of the book, Chapters 12 to 15, focus on marine ecosystems and how they are influenced by the changing climate, as well as how the important element cycles (nitrogen and carbon) are influenced by the ecosystem (in this case a mangrove ecosystem) and thus, indirectly, by climatic changes. Primary marine production may reduce climatic changes, due to carbon dioxide uptake, and it is important that we obtain good estimates of primary production in aquatic ecosystems, which is the topic of the last paper, Chapter 15.

The nineteenth International ISEM Conference in Toulouse in 2013, as well as the papers published in this volume and in Ecological Modelling, Volume 306, demonstrate that the fields of ecological modeling and systems ecology are continuously developing and expanding, in the sense that we can better cover more and more problems and questions via ecological modeling. Ecological modeling is a crucial discipline in our effort to shift the development from what was presented in Limits to Growth in 1972 toward more sustainable methods based on important actions informed by a holistic approach.

Sven Erik Jørgensen, President of ISEM Copenhagen

July 20, 2015.

Chapter 1

Introduction

Global changes and sustainable ecosystem management

Young-Seuk Parka,*; Sovan Lekb,*    a Department of Biology, and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Dongdaemun-gu, Seoul 02447, Republic of Korea

b University of Toulouse, Lab EDB (Evolution & Diversité Biologique), UMR CNRS 118 route de Narbonne, 31062 Toulouse cedex 9, France

* Corresponding authors: email address: [e-mail], [e-mail]

Abstract

This chapter introduces the necessity for ecological models for global changes and sustainable ecosystem management. It gives an overview of the advanced modeling techniques for studying global change in environmental sciences. Various modeling approaches are introduced in this book. Each chapter reviews modeling methods in terms of how they can be used for determining the impacts of environmental changes, global warming and climate change in particular. After the introduction of the model algorithms, their applications are presented.

Keywords

Ecological modeling

Global changes

Sustainable ecosystem management

Ecosystem services

1.1 Effects of Global Changes

We are living in a world that is constantly changing due to natural and anthropogenic factors. Never before on Earth has the importance of sustainable development resonated with so many people. Therefore, without immediate and effective efforts to conserve our planet, the environment will face unyielding pressure. In ecology, as well as human society, one of the issues of serious concern is the impact of global warming on ecosystems. The increase in the global average temperature is leading to melting of Arctic ice, thereby increasing the sea levels. This is likely to continue in the future (IPCC, 2007; Li et al., 2013). Global warming poses a considerable threat to global biodiversity (Harte et al., 2004; Thomas et al., 2004). It has been predicted that approximately 20–30% of the plant and animal species assessed to date will be at an increased risk of extinction if the global average temperature increases more than 1.5–2.5 °C relative to the 1980–1999 levels (IPCC, 2007). Global warming is likely to substantially alter many ecosystem services that play a crucial role in sustaining human well-being and economic viability (Li et al., 2013). However, there are uncertainties in the effects of global warming on ecosystems due to the complicated interactions among ecosystem components (Parmesan and Yohe, 2003). Therefore, it is critical to improve our understanding of the relationships between climate change and ecosystem functions in diverse ecosystems to better understand the consequences of global warming and to develop effective means of adapting to these consequences.

There have been many ecological studies on the effects of global change on various biota, such as butterflies, birds, fish, plants, and corals during the last century (e.g., Thomas and Lennon, 1999; Peterson et al., 2002; Li et al., 2013; Warren and Chick, 2013). To adapt to the global changes, organisms can have two different strategies: ecological strategy and genetic strategy (Li et al., 2015). Organisms can shift their distributional ranges to track more favorable habitats as the ecological strategy, or persist in their original habitats through phenotypic plasticity or rapid evolutionary adaptation as the genetic strategy (Walther et al., 2002; Davis et al., 2005; Colwell et al., 2008; Bertrand et al., 2011). The sensitivity of organisms to environmental conditions determines their geographical distribution and abundance in particular habitats (Bêche and Resh, 2007). With respect to environmental changes, the sensitivity of a species is usually characterized by its optimal value with a measure of niche breadth or tolerance about the optimum (Li et al., 2012). Species will become extinct if they fail to adapt to new environmental conditions through either the ecological or genetic strategy (Li et al., 2015). Therefore, active adaptive conservation requires a scientific understanding of the structures and functioning of ecosystems, and research, especially in ecological modelling, is crucial for supporting the sustainable development paradigm, in which the economy, society, and environment are integrated and positively reinforce each other.

1.2 Sustainable Ecosystem Management

Ecological modelling can assist in the implementation of sustainable development, mathematical models, and systems analysis that describe how ecological processes can support the sustainable management of resources (Park et al., 2015). Sustainability, defined as the maintenance of natural capital and resources (Goodland, 1995), is an increasingly used term as a guide for future development (Odum and Barrett, 2005). Sustainability can be considered in terms of three aspects: environmental, economic, and social domains (Figure 1.1; WCED, 1987). Environmental sustainability is the ability of the environment to support a defined level of environmental quality and natural resource extraction rates indefinitely; economic sustainability is the ability of an economy to support a defined level of economic production indefinitely; and social sustainability is the ability of a social system, such as a country, family, or organization, to function at a defined level of social well-being and harmony indefinitely (http://Thwink.org/; http://www.thwink.org/sustain/glossary/). The sustainability assessment of socioecological systems requires a systemic perspective to address the close relationships between the environmental and socioeconomic processes, and ecological modelling contributes to facilitating the development of sustainable management planning of target ecosystems (Park et al., 2015).

Figure 1.1 Three domains of sustainability.

Figure 1.2 shows trends in the frequency of occurrence of four terms, sustainability, climate change, global warming, and ecosystem services, from a corpus of books published in English from 1960 to 2008, as evaluated by the Google Books Ngram Viewer. Interestingly, three ngrams, sustainability, climate change, and global warming, display similar trends, with a rapid increase starting in the late 1980s, reflecting social and scientific interest in these topics. The concept of ecosystem services was more recently developed, as is reflected in the graph. The Google Books Ngram Viewer (https://books.google.com/ngrams/) is an online viewer based on the database of Google Books (Michel et al., 2010). It creates a graph using frequencies of any word or short sentence using yearly counts of ngrams found in the sources printed between 1800 and 2012 (Wikipedia; https://en.wikipedia.org/wiki/Google_Ngram_Viewer).

Figure 1.2 Trends in the frequency of occurrence of four terms: sustainability, climate change, global warming, and ecosystem services from a corpus of books published in English from 1960 to 2008, as evaluated by the Google Books Ngram Viewer. Interestingly, the frequency of books using three of these terms rapidly increased starting in the late 1980s, reflecting social and scientific interest in these topics.

1.3 Outline of This Book

This introductory chapter presents the necessity of ecological models for sustainable ecosystem management. Various modelling approaches are introduced in this book, and each chapter reviews modelling methods in terms of how they can be used for determining the impacts of environmental changes; in particular, global warming and climate change. After the introduction of the model algorithms, their applications are presented.

1.3.1 Review of ecological models

Chapter 2 reviews trends in the research and development of modelling techniques in ecological studies. Modelling techniques have been routinely employed in understanding complex ecological problems over the last several decades. In Chapter 2, Guo and his colleagues outline both the development history and research trends of ecological modelling. The history of five generations of ecological models over the last several decades are described and reviewed. Then, a bibliometric analysis describes the research trends in ecological modelling applications during 1991–2013 from the following perspectives: publication output and language, subject categories, country distribution and international cooperation networks, and author keyword analysis. Finally, based on the quantitative results, some frequently used and fast-developing models and algorithms are briefly reviewed.

1.3.2 Ecological network analysis and structurally dynamic models

Ecological network analysis is a systems-oriented methodology to analyze within-system interactions for identifying holistic properties that are otherwise not evident from direct observations (Fath et al., 2007; Park et al., 2015). Ecological network analysis relies on compartmental models that are constructed to represent the transactions of energy or matter within ecosystems. To facilitate the evaluation of an ecosystem, various system-wide measures have been proposed to capture its holistic properties (Jørgensen et al., 2013). Chapter 3 describes system-wide measures in ecological network analysis (Patten, 1978; Fath and Patten, 1999; Ulanowicz, 2004) developed to capture essential information about ecosystem structure and function. In Chapter 3, Kazanci and Ma use 52 real-life ecosystem models selected from the literature to investigate the relationships among these measures. They classify them into several groups based on their similarities, providing better information about the nature and capability of measures used for ecological network analysis.

Structurally dynamic models can account for adaptation and shifts in species composition, and can be developed by two methods: use of expert knowledge and use of a goal function. The idea of structurally dynamic models is to continuously determine a new set of parameters that are better fitted to the prevailing conditions of the ecosystem (Park et al., 2005). Chapter 4 presents the application of structurally dynamic models to determine the impacts of climate changes. In Chapter 4, Jørgensen proposes to use the work energy of the ecosystem as the goal function in structurally dynamic models. He presents the theoretical background for the development of structurally dynamic models by using the goal function (work energy), and introduces the application of structurally dynamic models for the assessment of ecological changes due to climate change, showing that structurally dynamic models are appropriate for the application of ecosystem changes resulting from global warming.

1.3.3 Behavioral monitoring and species distribution models

The assessment of ecosystems through efficient monitoring systems is fundamental for effective management of ecosystems. The first stage in sustainable ecosystem management is the detection of disturbances, such as toxicants, in the target ecosystem (Bae and Park, 2014). With advances in both computer hardware and software in combination with information and communication technologies, biological early warning systems have been developed that are based on the different responses of organisms to disturbance. Monitoring of animal behavior based on the continuous observation of movement behavior seems to be most effective in linking small- and large-scale assessments. In Chapter 5, Chon and Kim describe modelling animal behavior to monitor the effects of stressors in ecosystems. Monitoring based on the behavior of organisms garners special attention in biological assessments in terms of both the prediction and management aspects of aquatic ecosystem management. They review the recent technical advancements across different timescales from seconds to days for a practical approach in addressing behavioral status by observing the overall movements of small fishes in a confined observation arena under stressful conditions. To evaluate the behavioral status, hidden Markov model, Fourier and wavelet transforms, and intermittency approaches have been used.

Generally, species distribution models have been developed to quantify the association between the occurrence of species and environments, including habitat conditions and meteorological factors, and have recently been widely implemented in both basic and applied ecology, especially for species conservation and biodiversity management (Guo et al., 2015). In Chapter 6, among the various species distribution models developed during the last decade, van Echelpoel and colleagues review five selected modelling techniques: decision trees, generalized linear models, artificial neural networks, fuzzy logic, and Bayesian belief networks, and present examples for each modelling technique. They characterize the benefits and drawbacks of each modelling technique to aid the selection of the most suitable one.

1.3.4 Ecological risk assessment

Ecological risk assessment is the process of estimating the likelihood that a particular event will occur under a given set of circumstances (Maltby et al., 2005; Domene et al., 2008; Xu et al., 2014), aiming to provide a quantitative basis for balancing and comparing risks associated with environmental problems and a systematic means of improving the estimation and understanding of those risks (Graham et al., 1991). Chapters 7 and 8 present ecological models related to ecological risk assessment. In Chapter 7, Zhang and Liu review the ecological risk assessment model methods, and present an application of AQUATOX models for the ecosystem risk assessment of polycyclic aromatic hydrocarbons (PAHs) in lake ecosystems. The PAH risk estimates demonstrate that the estimated risk for natural ecosystems cannot be fully explained by single species toxicity data alone, so the AQUATOX model could provide a good basis for ascertaining ecological protection levels of chemicals of concern for aquatic ecosystems. They show that the AQUATOX model can potentially be used to provide necessary information for the early warning and rapid forecasting of pollutant transport and fate in the management of chemicals.

Two key steps in ecological risk assessment are the selection of the best-fitting model for the species sensitivity distribution and uncertainty analysis. Chapter 8 presents the Bayesian matbugs calculator as a platform to select the best model for a species sensitivity distribution and to assess ecological risk at high, mid, and low levels, and shows a case study of ecological risk assessment and priority setting for 32 toxic mechanisms of typical persistent toxic substances in river systems with a high level of ecological risk.

1.3.5 Agriculture and forest ecosystems

Chapter 9 reviews state-of-the-art models simulating mixedwood stands. After reviewing almost 400 peer-reviewed publications, Blanco and colleagues identify the four most common models for simulating mixed forest stands: two in boreal/temperate ecosystems and two in tropical/subtropical environments. They compare the different modelling approaches, and suggest multimodel exercises as a way to both compare model performance and to reduce simulation uncertainty due to model selection.

Sustainable ecosystem management has also become a major challenge in agroecosystems. Chapter 10 introduces several mathematical and computer formalisms resulting from work on artificial intelligence, operations research, and planning recently applied to agroecosystem management. It presents some studies by the Modelling of Agro-systems and Decisions team on modelling and simulation of complex systems to exploit agronomic models and decisions models. It also provides strategic design formalisms such as weighted constraint networks and Markov decision-making processes, and the coupling between the simulation and decision topics. Finally, it illustrates some of these methods based on recent studies implementing agroecosystem management.

1.3.6 Urban ecosystems

Chapter 11 presents ecosystem services in relation to the carbon cycle of an urban system. The imbalance in the carbon cycle in an urban system is due to the greater emission of carbon into the atmosphere than carbon sequestration. Mandal and Ray identify the ecosystem services and disservices in the Asansol–Durgapur Planning Area in eastern India. They reveal that urban forests and agriculture play pivotal roles in carbon sequestration and emission processes simultaneously, while transport, household, cattle, and industry sectors are responsible for carbon emissions only.

1.3.7 Estuary and marine ecosystems

Chapters 12–15 introduce ecological models developed in estuary and marine ecosystems. Chapter 12 presents the effects of climate change in estuarine ecosystems with coupled hydrodynamic and biogeochemical models. Coupled hydrodynamics and biogeochemical numerical models jointly simulate the physical, chemical, and biological processes at the relevant spatial and temporal scales. In Chapter 12, Rodrigues and colleagues provide a general overview of some well-established coupled hydrodynamic–biogeochemical models, and discuss the models used to support the study of climate change impacts on estuarine ecosystems. They demonstrate the use of coupled hydrodynamic–biogeochemical models to support the long-term climate adaption management of estuarine ecosystems and they define mitigation and adaptation strategies within a climate change context with a case study: the evaluation of climate change impacts in the lower trophic level dynamics in the Aveiro lagoon.

In Chapter 13, modelling of nitrogen and carbon cycles in the Hooghly estuary along with the adjacent mangrove ecosystem in India are presented by Ray and colleagues. They propose two dynamic models, both with seven compartments, taking into consideration the importance of nitrogen and carbon. They consider nitrogen and carbon of the mangrove litterfall as a source, and their conversion into different organic and inorganic forms as state variables.

A coupled model consisting of hydrodynamic and ecosystem models is presented in Chapter 14 by Kitazawa and Zhang. They introduce detailed algorithms for the coupled model, and apply them to the numerical simulation of eutrophication problems in a semiclosed bay, Tokyo Bay, in Japan.

Finally, several models of phytoplankton functioning in a stationary column of water are proposed in Chapter 15. Based on a numerical solution of one of these models and satellite sea surface sounding data, the vertical distribution of phytoplankton biomass and the multiyear dynamics of average primary production and yearly primary production for the Sea of Japan (East Sea) are obtained.

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Chapter 2

Toward a new generation of ecological modelling techniques

Review and bibliometrics

Chunanbo Guoa,b,*; Young-Seuk Parkc; Yang Liud; Sovan Lekb    a State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China

b University of Toulouse, Lab EDB (Evolution & Diversité Biologique), UMR CNRS 118 route de Narbonne, 31062 Toulouse cedex 9, France

c Department of Biology, and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Dongdaemun-gu, Seoul 02447, Republic of Korea

d Université de Toulouse; INP, UPS; EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement); Toulouse, France

* Corresponding author: email address: [e-mail]

Abstract

Modelling techniques have long been routinely employed in understanding complex ecological problems over the last several decades. It is therefore necessary to outline both the development history and research trends of ecological modelling. This chapter contributes to a global view of the development and research trend of modelling techniques in ecological studies. First of all, the history of five generations of ecological modelling were determined and reviewed over the last several decades. Thereafter, a bibliometric analysis method was performed to systematically reveal the research trends of ecological modelling applications during the period 1991–2013, from the following perspectives: publication output and language, subject categories, country distribution and international cooperation networks, and author keyword analysis. Last, based on the quantitative results, some frequently used and fast-developing models and algorithms are briefly reviewed to provide a primer for ecological model users and contributors.

Keywords

Ecological modelling

Bibliometric analysis

Global review

Research trend

Algorithms

2.1 Introduction

The problems to be solved in ecological studies are always complex because ecological data are subject to nonlinearity and complexity due to many variables and their interactions. It is therefore recommended to study ecological problems using appropriate methods that can handle complexity and linearity. In addition, in modern ecological studies, requirements for knowledge of the interactions between ecosystems and ecological properties have strongly increased. Therefore, over the last several decades, intensive research has employed various modelling techniques to clarify complex ecological problems. Along with the rapid development of computer and information sciences, a large number of modelling techniques and algorithms have been developed, and used for ecosystem management as well as for ecological studies. Therefore, it is necessary to outline some of the routinely applied models, to give a global perspective of the development trends in modelling techniques. Bibliometric analysis includes a series of visual and quantitative procedures to generalize the patterns and dynamics found in scientific publications (Pritchard, 1969). Such analyses have been conducted to reveal the global trends of various fields of research to measure scientific progress (Falagas et al., 2006; Tarkowski, 2007; Li et al., 2008; Xie et al., 2008).

In this chapter, we use a bibliometric method to systematically assess scientific progress in the new generation of ecological modelling techniques during the period 1991–2013. The results could help give a better understanding of the global trends in modelling techniques in ecological studies, and potentially help researchers to better orient their own research in view of the global picture. Based on the quantitative results, some frequently used and fast-developing models are briefly reviewed.

2.2 Historical Development of Ecological Modelling

Jørgensen (2011) pictured a nonlinear time axis that gives approximate information on the development of ecological modelling in a year when the various development steps took place. Generally, there are five generations that can be identified in the historical development of ecological models (Figure 2.1). The first-generation models date back to the early 1920s, models of the oxygen balance in a stream (the Streeter–Phelps model) and the prey–predator relationship (the Lotka–Volterra model). The first biogeochemical model constructed was the Streeter–Phelps BOD/DO model in 1925. It has been used numerous times as an illustration of biogeochemical models and of the practical use of ecological models in environmental management (Jørgensen, 2009a,b). Lotka and Volterra developed the first population model, which is still widely used (Volterra, 1926; Lotka, 1956). Many population models have been developed, tested, and analyzed since then.

Figure 2.1 The development of ecological and environmental models is shown schematically. Adapted from Jørgensen (2011).

The second generation appeared in the 1950s and 1960s; population dynamic models and more complex river models were further developed at that time. The wide use of ecological models in environmental management, which can also be viewed as the third generation of models, started around 1970, when the first eutrophication models emerged and very complex river models were developed. In parallel with this development, ecologists became more quantitative in their approach to environmental sciences, probably because of the needs formulated by environmental management. The quantitative research results from the late 1960s onward have been of enormous importance for the quality of ecological models. They are probably just as important as the developments in computer technology.

The models developed from the mid-1970s to the mid-1980s could be called the fourth generation of models. In this period, models were characterized by a relatively sound ecological basis, along with an emphasis on realism and simplicity. Many models were validated in this period with an acceptable result and for some (but not many) it was even possible to validate the prognosis.

The fifth generation started in the mid-1980s, when numerous new approaches such as machine learning, fuzzy modelling, examinations of catastrophic and chaotic behavior of models, and application of goal functions to account for adaptation and structural changes were proposed. Application of objective and individual modelling, expert knowledge, and artificial intelligence offers some additional advantages in modelling (Jørgensen, 2011).

In the new century, with the rapid development of computing capacity and large databases, numerous new model techniques and algorithms have been proposed and used in ecological studies, such as structurally dynamic models (SDMs), individual-based models (IBMs), artificial neural networks (ANNs), machine learning algorithms, support vector machine (SVM), genetic programming (GP), spatial models, and, recently, some statistical models.

2.3 Bibliometric Analysis of Modelling Approaches

2.3.1 Data Sources and Analysis

A bibliometric analysis presents the previous work on the related model techniques and algorithms in the last three decades (i.e., from 1991 to 2013). The data for this study was obtained from the most popular scientific database—Web of Science (SCI; Thomson Reuters). The search terms used for retrieving records were the full names of the model techniques or algorithms instead of the abbreviations. This would avoid problems of polysemy and result in an accurate data source. Thus, based on expert knowledge, we used the modelling techniques and algorithms listed in Table 2.1 as the query in the database. The query was searched from Title, Keywords, and Abstracts of each paper in the database.

Table 2.1

Modelling Techniques and Algorithms Contained in the Search Procedure

The publications obtained were first handled by eliminating the duplicated records and then filtered by selecting only the records clustered in the Thomson Reuters Web of Science subject categories related to environmental science and ecology; that is, Agricultural Multidisciplinary; Biology; Biodiversity Conservation; Ecology; Environmental Sciences; Evolutionary Biology; Entomology; Fisheries; Forestry; Limnology; Marine & Freshwater Biology; Multidisciplinary Sciences; Oceanography; Parasitology; Ornithology; Plant Sciences; Soil Sciences; Toxicology; Water Resources; and Zoology.

Thereafter, a bibliometric analysis was performed to systematically reveal the research trends of ecological modelling applications from the following perspectives: publication output and language, subject categories, country distribution and international cooperation networks, and author keyword analysis. All the analysis work and plots used R program (R Core Team,