The Review of Ecological Network Indicators in Graph Theory Context: 2014–2021

Hashemi, Rastegar; Darabi, Hassan

doi:10.1007/s41742-022-00404-x

Cited by 13 publications

(5 citation statements)

References 266 publications

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“…The landscape connectivity index represents the value of the landscape patches for species migration and is of great significance for biodiversity protection and ecosystem stability [40]. Numerous indices, such as the possible connectivity index (PC), the integral connectivity index (IIC), and the equivalent connectivity (EC), are now widely adopted in the calculation of landscape connectivity [41]. The PC not only indicates the connectivity of the landscape but can also be used to calculate the patch importance index (dPC).…”

Section: Identification Of Ecological Sources Based On Cmspacimentioning

confidence: 99%

Balancing Urban Expansion and Ecological Connectivity through Ecological Network Optimization—A Case Study of ChangSha County

Liu

Xia

et al. 2023

Land

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The counties have experienced urban expansion and landscape pattern fragmentation. As carriers of new urbanization, the balanced development between urban expansion and landscape connectivity in the counties needs to be emphasized. The uncontrolled expansion of land should be discouraged and planners need to clarify land use expansion patterns. Using Changsha County as the study area, the characteristics of the landscape pattern between 2000 and 2020 were analyzed. The morphological spatial pattern analysis and landscape connectivity method (CMSPACI), as well as the minimum cumulative resistance (MCR) model, was used to construct the ecological network. We also explored the most appropriate corridor width using the buffer zone to guide future land use planning and ecological network planning. The results show that based on CMSPACI the total area of ecological sources identified was 304.91 km2, encompassing a large area of forest parks. The total length of the 25 ecological corridors identified by the MCR model was 431.97 km. Ecological sources and corridors are missing in the central region; so, their pattern was optimized using landscape connectivity and the absence of location as selection criteria. The optimized network indices showed significant improvement. The width of the ecological corridors should be controlled in order to be in the range of 30 m to 50 m to maximize the effect of the corridors on species dispersal and migration. Our proposed research framework for the construction and optimization of EN in Changsha County can provide ideas to balance the contradictions between urban expansion and landscape connectivity in Changsha County.

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Section: Identification Of Ecological Sources Based On Cmspacimentioning

confidence: 99%

Balancing Urban Expansion and Ecological Connectivity through Ecological Network Optimization—A Case Study of ChangSha County

Liu

Xia

et al. 2023

Land

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“…The index can measure the degree of connectivity of regional ecological processes as maintaining good connectivity is essential for biodiversity conservation and ecosystem balance. Landscape vulnerability decreases with increasing IIC levels; the IIC is calculated as follows (Hashemi and Darabi, 2022):…”

Section: Landscape Vulnerabilitymentioning

confidence: 99%

Association between ecological risks and ecosystem services in an urban agglomeration in arid China

Huang

Song

Cao

et al. 2023

Front. Environ. Sci.

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Rapid urbanization leads to changes in ecosystem services and may exacerbate ecological risks. Clarifying the relationship between these two factors in a specific context is essential to explore the integrated management model and achieve sustainable regional development. However, previous studies mainly lack an integrated analysis, fail to clearly explain the mechanism of ecosystem change, and can neither support landscape ecological security construction nor spatial planning and management. This study, using the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM) as an example, applied multi-source data from 2010 to 2020, investigated the changes and relationships between ecological risks and ecosystem services, and proposes an assessment framework. The total ecosystem services (TES) of the studied agglomeration showed a decreasing trend, with an overall loss of 0.43%. Corresponding to the decrease of ecosystem services, the ecological risk was higher in the south and north of the UANSTM and lower in the northwestern, central, and eastern regions. The proportion of ecological high-risk areas was expanding. The key to the relationship between ecological risks and ecosystem services is the change in hydrological conditions. Therefore, we suggest that the UANSTM actively transforms the development and use mode of water resources and coordinates their allocation, aiming to reduce regional ecological risks and optimize the pattern of ecosystem services.

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“…1 2 N where N = 1, 2, 3, … is the number of planning units. (Hashemi & Darabi, 2022;Keeley et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…A popular class of indices considers habitat patches as nodes and the degree of accessibility between them as edges of a graph. The Integral Index of Connectivity (IIC; Pascual‐Hortal & Saura, 2006) and the Probability of Connectivity Index (PCI; Saura & Pascual‐Hortal, 2007) are the graph‐based indices used the most to analyse structural and functional connectivity (Hashemi & Darabi, 2022; Keeley et al., 2021). They have been proven to be good predictors of, among other things, species occupancy and occurrence patterns (Awade et al., 2012; Pereira et al., 2011); designed to be sensitive to even subtle landscape changes, both of which provide a basis for quantifying the importance of landscape elements (Bodin & Saura, 2010); and therefore used for scenario analyses for CCP (Engelhard et al., 2017, Martinez Pardo et al., 2023).…”

Section: Introductionmentioning

confidence: 99%

Connectivity conservation planning through deep reinforcement learning

Equihua,

Beckmann,

Seppelt

2024

Methods Ecol Evol

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The United Nations has declared 2021–2030 the decade on ecosystem restoration with the aim of preventing, stopping and reversing the degradation of the ecosystems of the world, often caused by the fragmentation of natural landscapes. Human activities separate and surround habitats, making them too small to sustain viable animal populations or too far apart to enable foraging and gene flow. Despite the need for strategies to solve fragmentation, it remains unclear how to efficiently reconnect nature. In this paper, we illustrate the potential of deep reinforcement learning (DRL) to tackle the spatial optimisation aspect of connectivity conservation planning. The propensity of spatial optimisation problems to explode in complexity depending on the number of input variables and their states is and will continue to be one of its most serious obstacles. DRL is an emerging class of methods focused on training deep neural networks to solve decision‐making tasks and has been used to learn good heuristics for complex optimisation problems. While the potential of DRL to optimise conservation decisions seems huge, only few examples of its application exist. We applied DRL to two real‐world raster datasets in a connectivity planning setting, targeting graph‐based connectivity indices for optimisation. We show that DRL converges to the known optimums in a small example where the objective is the overall improvement of the Integral Index of Connectivity and the only constraint is the budget. We also show that DRL approximates high‐quality solutions on a large example with additional cost and spatial configuration constraints where the more complex Probability of Connectivity Index is targeted. To the best of our knowledge, there is no software that can target this index for optimisation on raster data of this size. DRL can be used to approximate good solutions in complex spatial optimisation problems even when the conservation feature is non‐linear like graph‐based indices. Furthermore, our methodology decouples the optimisation process and the index calculation, so it can potentially target any other conservation feature implemented in current or future software.

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The Review of Ecological Network Indicators in Graph Theory Context: 2014–2021

Cited by 13 publications

References 266 publications

Balancing Urban Expansion and Ecological Connectivity through Ecological Network Optimization—A Case Study of ChangSha County

Balancing Urban Expansion and Ecological Connectivity through Ecological Network Optimization—A Case Study of ChangSha County

Association between ecological risks and ecosystem services in an urban agglomeration in arid China

Connectivity conservation planning through deep reinforcement learning

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