Estimating the Characteristic Spatiotemporal Variation in Habitat Quality Using the InVEST Model—A Case Study from Guangdong–Hong Kong–Macao Greater Bay Area

Abstract

The intensity of human activity, habitat loss and habitat degradation have significant impacts on biodiversity. Habitat quality plays an important role in spatial dynamics when evaluating fragmented landscapes and the effectiveness of biodiversity conservation. This study aimed to evaluate the status and characteristic variation in habitat quality to analyze the underlying factors affecting habitat quality in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Here, we applied Kendall’s rank correlation method to calculate the sensitivity of habitat types to threat factors for the Integrated Valuation of Ecosystem Services and Tradeoffs habitat quality (InVEST-HQ) model. The spatiotemporal variation in habitat quality of the GBA in the period 1995–2015 was estimated based on the InVEST-HQ model. We analyzed the characteristic habitat quality using different ecosystem classifications and at different elevation gradients. Fractional vegetation cover, the proportion of impervious surface, population distribution and gross domestic product were included as the effect factors for habitat quality. The correlation between the effect factors and habitat quality was analyzed using Pearson’s correlation tests. The results showed that the spatial pattern of habitat quality decreased from fringe areas to central areas in the GBA, that the forest ecosystem had the highest value of habitat quality, and that habitat quality increased with elevation. In the period from 1995 to 2015, habitat quality declined markedly and this could be related to vegetation loss, land use change and intensity of human activity. Built-up land expansion and forest land fragmentation were clear markers of land use change. This study has great significance as an operational approach to mitigating the tradeoff between natural environment conservation and rapid economic development.

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

“…In these areas, the habitat quality was improved. This is consistent with the findings of many existing studies that different land-use changes have different spatial and temporal effects on habitat quality, that is, conversion from other land-use types to forest land and grassland is positively correlated with habitat changes, while conversion to construction land is negatively correlated with habitat changes [23,56]. On the whole, although Liulin County has implemented a series of ecological protection and restoration projects, such as the Grain for Green Project, three North Shelterbelt Project, the Fragile Ecosystem Protection of Lvliang Mountain, mined land reclamation, and ecological restoration, the destruction of ecologically functional land by human activities outweighed the positive effects of these projects, leading to the habitat quality decrease of the whole county.…”

“…Early studies utilize biodiversity and habitat data acquired through field investigation to assess habitat quality [10][11][12], but its application is usually restricted to small areas and short time scale due to the time-consuming and laborious field investigation [13,14]. In recent years, with the development of 3S technologies, ecological models, such as the Habitat Suitability Index model (HIS) [15][16][17], the SoLVES model [18][19][20][21] and the InVEST model [22][23][24], are developed to quantitatively assess regional habitat quality. In particular, the InVEST model is the most popular among these models due to its advantages of ready data availability, accurate analysis ability and spatial visualization of assessment results [13,25,26], which enable the assessment to be conducted at large scales and within long-time span [14].…”

“…Threat factor parameters were determined based on empirical values and expert knowledge (Table 1). Referring to the InVEST instruction manual [51] and the related papers [13,23], sensitivity parameters were determined (Table 2). In this study, ArcGIS 10.4 was used to process and analyze shp and tif data, InVEST model 3.7 was used to evaluate and simulate the degree of habitat degradation and habitat quality, and GeoDetector_2015 was used to analyze the influencing factors of habitat quality.…”

Landscape sensitivity-based scenario analysis using flus model: a case of Asarsuyu watershed

“…Landscape, shaped by ecological and geomorphological processes (Swanson ve diğerleri 1988;Renwick 1992), also interacts with land cover change. Urbanization and the expansion of agricultural areas, increasing urban population leads to fragmentation/loss of natural landscapes and their transformation into impermeable surfaces, water supply problem and gradually decreasing biodiversity that supports ecosystem functioning (Das & Das 2019;Luo et al 2020;Shao et al 2021;Wu et al 2021).…”

Diagnosis of Key Ecological Problems and Layout of Ecological Restoration Projects- A Case Study of the Aksu River Basin, China

“…As the natural environment and utility on which human beings depend for survival, it is of great signi cance to scienti cally evaluate the spatial and temporal patterns and interaction mechanisms of ecosystem services to determine the basis of the regional ecological environment and construct regional ESPs. In recent years, there has been an increasing number of studies based on ecosystem service assessments 33,34,28 .…”