Investigations of indirect relationships in ecology and environmental sciences: a review and the implications for comparative theoretical ecosystem analysis

Connections between soil microbes, land use and European climate: Insights for management practices

Soil microbial biomass and activity strongly depend on land use, vegetation cover, climate, and soil physicochemical properties. In most cases, this dependence was assessed by one-to-one correlations while by employing network analysis, information about network robustness and the balance between stochasticity and determinism controlling connectivity, was revealed. In this study, we further elaborated on the hypothesis of Smith et al. (2021) that cropland soils depended more on climate variables and therefore are more vulnerable to climate change. We used the same dataset with that of Smith et al. (2021) that contains seasonal microbial, climate and soil variables collected from 881 soil points representing the main land uses in Europe: forests, grassland, cropland. We examined complete (both direct and indirect relationships) and incomplete networks (only direct relationships) and recorded higher robustness in the former. Partial Least Square results showed that on average more than 45% of microbial attributes' variability was predicted by climate and habitat drivers denoting medium to strong effect of habitat filtering. Network architecture slightly affected by season or land use type; it followed the core/periphery structure with positive and negative interactions and no hub nodes. Microbial attributes (biomass, activity and their ratio) mostly belong to core block together with Soil Organic Carbon (SOC), while climate and soil variables to periphery block with the exception of cropland networks, denoting the higher dependence between microbial and climate variables in these latter. All complete networks appeared robust except for cropland and forest in summer, a finding that disagrees with our initial hypothesis about cropland. Networks' connectivity was controlled stronger by stochasticity in forest than in croplands. The lack of human interventions in forest soils increase habitat homogeneity enhancing the influence of stochastic agents such as microbial unlimited dispersal and/or stochastic extinction. The increased stochasticity implies the necessity for proactive management actions.

Uncertainty, Complexity and Constraints: How Do We Robustly Assess Biological Responses under a Rapidly Changing Climate?

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.

Higher Education in Innovation Ecosystems

While higher education has been considered as both an ‘engine’ for innovation and a ‘catalyst’ for sustainability development, the integration of both the ‘innovation engine’ and ‘sustainability catalyst’ roles is best reflected in higher education’s engagement in innovation ecosystems—the theme of this special issue, including 16 articles dealing with the topic from various perspectives. In this editorial, we outline an overarching framework about the relations between higher education and innovation ecosystem. When elaborating the framework, we provide a new definition of innovation ecosystem and identify three roles of university in innovation ecosystems, based on synthesizing relevant literature. The framework could facilitate readers to comprehend each of the collected articles and find synergy among them.

Remote Observation in Habitat Suitability Changes for Waterbirds in the West Songnen Plain, China

Being one of the most important habitats for waterbirds, China’s West Songnen Plain has experienced substantial damage to its ecosystem, especially the loss and degradation of wetlands and grasslands due to anthropogenic disturbances and climate change. These occurrences have led to an obvious decrease in waterbird species and overall population size. Periodic and timely monitoring of changes in habitat suitability and understanding the potential driving factors for waterbirds are essential for maintaining regional ecological security. In this study, land cover changes from 2000 to 2015 in this eco-sensitive plain were examined using Landsat images and an object-based classification method. Four groups of environmental factors, including human disturbance, water situation, food availability, and shelter safety, characterized by remote sensing data were selected to develop a habitat suitability index (HSI) for assessing habitat suitability for waterbirds. HSI was further classified into four grades (optimum, good, general, and poor), and their spatiotemporal patterns were documented from 2000 to 2015. Our results revealed that cropland expansion and wetland shrinkage were the dominant land cover changes. Waterbird habitat areas in the optimum grade experienced a sharp decline by 7195 km2. The habitat area in good suitability experienced reduction at a change rate of −8.64%, from 38,672 km2 to 35,331 km2. In addition, waterbird habitats in the general and poor grades increased overall by 10.31%. More specifically, the total habitat areas with optimum suitable grade, in five national nature reserves over the study region, decreased by 12.21%, while habitat areas with poor suitable grade increased by 3.89%. Changes in habitat suitability could be largely attributed to the increase in human disturbance, including agricultural cultivation from wetlands and grasslands and the expansion of built-up lands. Our findings indicate that additional attention should be directed towards reducing human impact on habitat suitability for sustainable ecosystems.

Ecological network analysis of growing tomatoes in an urban rooftop greenhouse

Urban agriculture has emerged as an alternative to conventional rural agriculture seeking to foster a sustainable circular economy in cities. When considering the feasibility of urban agriculture and planning for the future of food production and energy, it is important to understand the relationships between energy flows throughout the system, identify their strengths and weaknesses, and make suggestions to optimize the system. To address this need, we analyzed the energy flows for growing tomatoes at a rooftop greenhouse (RTG). We used life cycle assessment (LCA) to identify the flows within the supply chain. We further analyzed these flows using ecological network analysis (ENA), which allowed a comparison of the industrial system to natural systems. Going beyond LCA, ENA also allowed us to focus more on the relationships between components. Similar to existing ENA studies on urban metabolism, our results showed that the RTG does not mimic the perfect pyramidal structure found in natural ecosystems due to the system's dependency on fossil fuels throughout the supply chain and each industry's significant impact on wasted energy. However, it was discovered that the RTG has strong foundational relationships in its industries, demonstrating overall positive utility; this foundation can be improved by using more renewable energy and increasing the recycling rates throughout the supply chain, which will in turn improve the hierarchy of energy flows and overall energy consumption performance of the system.

Predicting ecosystem components in the Gulf of Mexico and their responses to climate variability with a dynamic Bayesian network model

The Gulf of Mexico is an ecologically and economically important marine ecosystem that is affected by a variety of natural and anthropogenic pressures. These complex and interacting pressures, together with the dynamic environment of the Gulf, present challenges for the effective management of its resources. The recent adoption of Bayesian networks to ecology allows for the discovery and quantification of complex interactions from data after making only a few assumptions about observations of the system. In this study, we apply Bayesian network models, with different levels of structural complexity and a varying number of hidden variables to account for uncertainty when modeling ecosystem dynamics. From these models, we predict focal ecosystem components within the Gulf of Mexico. The predictive ability of the models varied with their structure. The model that performed best was parameterized through data-driven learning techniques and accounted for multiple ecosystem components’ associations and their interactions with human and natural pressures over time. Then, we altered sea surface temperature in the best performing model to explore the response of different ecosystem components to increased temperature. The magnitude and even direction of predicted responses varied by ecosystem components due to heterogeneity in driving factors and their spatial overlap. Our findings suggest that due to varying components’ sensitivity to drivers, changes in temperature will potentially lead to trade-offs in terms of population productivity. We were able to discover meaningful interactions between ecosystem components and their environment and show how sensitive these relationships are to climate perturbations, which increases our understanding of the potential future response of the system to increasing temperature. Our findings demonstrate that accounting for additional sources of variation, by incorporating multiple interactions and pressures in the model layout, has the potential for gaining deeper insights into the structure and dynamics of ecosystems.

An analysis of the impact on land use and ecological vulnerability of the policy of returning farmland to forest in Yan'an, China

During the past decades, land use change has taken place around the Loess Plateau at unprecedented rates. Due to the impact of existing land use policy, great changes have taken place in the land use types in this ecologically vulnerable area. Taking eight counties in Yan'an, Shaanxi province, China, as the study area, this study analyzed the long-term (from 1997 to 2011) changes in land use and ecological vulnerability. Based on thematic mapper (TM) images of Yan'an in 1997, 2004, and 2011, the dynamic changes in land use are analyzed with the application software for remote sensing (RS) and geographic information system (GIS) since the implementation of the policy of returning farmland to forest. Combined with the land use data, the local socio-economic data, and natural resources condition, ecological vulnerability is evaluated using the spatial principal component analysis (SPCA) model in Yan'an region. Using the natural breaks classification (NBC), the evaluation results are divided into five categories: potential, slight, light, medium, and heavy. The results show that although the regional land use types changed markedly, the ecological vulnerability in the study shows greater than average optimism, and the ecological vulnerability index of the southern four counties is lower than that of the northern four counties. In 1997-2011, the eco-environmental quality gradually improved in most areas. However, it gradually deteriorated in some regions.

A new method of regional eco-environmental quality assessment and its application

Eco-environmental quality assessment (EQA) is an intricate and challenging task that must take into account numerous natural, economic, political, and social factors, which are subject to multiple conflicting criteria. In this paper, a methodological reference framework is developed for EQA that combines the fuzzy Delphi method (FDM) and fuzzy analytical hierarchy process (FAHP) with a geographic information system (GIS). The proposed method significantly improves the accuracy and reliability of evaluation results through the incorporation of fuzzy set theory. A GIS not only has the ability to store and analyze large amounts of spatial data from different sources but also provides a consistent visualization environment for displaying the input data and the results of EQA. Furthermore, unlike prior EQAs, the proposed method can support the dynamic estimation of regional eco-environmental quality by updating historical spatiotemporal data at little additional cost. A case study is presented for the western Tibetan Plateau. The study results show that worse, bad, and moderate eco-environmental quality classes comprised 16.58, 20.15, and 24.84% of the total area, respectively. Good and better eco-environmental quality classes accounted for 38.43%. This result indicates that nearly 62% of the total area is eco-environmentally vulnerable. The results verified the usefulness and feasibility of the proposed method. The EQA can also help local managers make scientifically based and effective decisions about Tibetan eco-environmental protection and land use.

Habitat evaluation of wild Amur tiger (Panthera tigris altaica) and conservation priority setting in north-eastern China

The Amur Tiger (Panthera tigris altaica) is one of the world's most endangered species. Recently, habitat fragmentation, food scarcity and human hunting have drastically reduced the population size and distribution areas of Amur tigers in the wild, leaving them on the verge of extinction. Presently, they are only found in the north-eastern part of China. In this study, we developed a reference framework using methods and technologies of analytic hierarchy process (AHP), remote sensing (RS), geographic information system (GIS), GAP analysis and Natural Break (Jenks) classification to evaluate the habitat and to set the conservation priorities for Amur tigers in eastern areas of Heilongjiang and Jilin Provinces of northeast China. We proposed a Habitat Suitability Index (HSI) incorporating 7 factors covering natural conditions and human disturbance. Based on the HSI values, the suitability was classified into five levels from the most to not suitable. Finally, according to results of GAP analysis, we identified six conservation priorities and designed a conservation landscape incorporating four new nature reserves, enlarging two existing ones, and creating four linkages for Amur tigers in northeast China. The case study showed that the core habitats (the most suitable and highly suitable habitats) identified for Amur tigers covered 35,547 km(2), accounting for approximately 26.71% of the total study area (1,33,093 km(2)). However, existing nature reserves protected only (7124 km(2) or) 20.04% of the identified core habitats. Thus, enlargement of current reserves is necessary and urgent for the tiger's conservation and restoration. Moreover, the establishment of wildlife corridors linking core habitats will provide an efficient reserve network for tiger conservation to maintain the evolutionary potential of Amur tigers facing environmental changes.

Revealing ecological networks using Bayesian network inference algorithms

Understanding functional relationships within ecological networks can help reveal keys to ecosystem stability or fragility. Revealing these relationships is complicated by the difficulties of isolating variables or performing experimental manipulations within a natural ecosystem, and thus inferences are often made by matching models to observational data. Such models, however, require assumptions-or detailed measurements-of parameters such as birth and death rate, encounter frequency, territorial exclusion, and predation success. Here, we evaluate the use of a Bayesian network inference algorithm, which can reveal ecological networks based upon species and habitat abundance alone. We test the algorithm's performance and applicability on observational data of avian communities and habitat in the Peak District National Park, United Kingdom. The resulting networks correctly reveal known relationships among habitat types and known interspecific relationships. In addition, the networks produced novel insights into ecosystem structure and identified key species with high connectivity. Thus, Bayesian networks show potential for becoming a valuable tool in ecosystem analysis.

Ecological network analysis: an application to the evaluation of effects of pesticide use in an agricultural environment

Ecological network analysis is used to evaluate the impact of pesticide use on ecological systems in the context of agricultural farmland environments. The aim is to provide support for the design of effective and minimally damaging pest control strategies. The ecological network analysis can identify species that are important to the integrity of the ecological network. The methodology can be used to monitor the impact of shifts in terms of types of pesticide used on the ecological system. The authors' intention is to use this methodology to provide supporting evidence for the UK Voluntary Initiative programme aimed at convincing farmers voluntarily to make improved choices in the use of a wide range of pesticides.