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The modeling and mapping of hotspots and coldspots ecosystem services (ESs) is an essential factor in the decision-making process for ESs conservation. Moreover, spatial prioritization is a serious stage in conservation planning. In the present research, based on the InVEST software, Getis-Ord statistics (G i * ), and a set of GIS methods, we quantified and mapped the variation and overlapping among three ESs (carbon storage, soil retention, and habitat quality). Furthermore, an approach was proffered for detecting priority areas to protect multiple ecosystem services. Hotspots recognized via the G i * statistics technique contain a higher capacity for supplying ESs than other areas. This means that protecting these areas with a bigger number of overlapped hotspots can provide more services. Results indicated that population growth accompanied by the increase in construction sites and low-yield agricultural lands in the Zayanderood dam watershed basin has resulted in ES losses. This situation is represented by increasing soil erosion, reduced carbon storage, reduced biodiversity, and fragmented habitat distribution due to land-use change. The statistically significant carbon storage, soil retention, and habitat quality hotspots with above 95% confidence level account for 21.5%, 39.3%, and 16.9% of the study area, respectively. Therefore, a clear framework was presented in this study for setting ES-based conservation priority. Decision makers and land-use planners can also combine this technique into their framework to identify and conserve ES hotspots to support their targeted ecosystem policies., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The biodiversity of an ecosystem is greatly influenced by the spatio-temporal pattern of the landscape. Understanding how landscape type affects habitat quality (HQ) is important for maintaining environmental and ecological sustainability, preserving biodiversity, and guaranteeing ecological health. This research examined the relationship between the HQ and landscape pattern. The study presented an interpretation of the biodiversity variation associated with the landscape pattern in the Zayanderud Dam watershed area by integrating the Land Change Modeler and the InVEST model. Landsat images and maximum likelihood classification were used to analyze the spatio-temporal characteristics of the landscape pattern in 1991 and 2021. The future landscape pattern in 2051 was simulated using a Land Change Modeler. Subsequently, the InVEST model and the landscape maps were used to identify the spatial distribution of HQ and its changes over three periods. The mean values of the HQ in the study area were 0.601, 0.489, and 0.391, respectively, demonstrating a decreasing trend. The effect of landscape pattern change on HQ was also assessed based on landscape metrics, including PD, NP, SHDI, and CONTAG. HQ had a significant positive correlation with the CONTAG parameter (R = 0.78). Additionally, it had a significant inverse correlation with NP (R = - 0.83), PD (R = - 0.61), and SHDI (R = - 0.42). The results showed that the habitats in the northern region had lower quality compared to those in the southern parts of the Zayanderud Dam watershed. The density, diversity, and connectivity of landscape patches significantly influence the HQ in the study area. This research has the potential to enhance understanding of the impacts of land change patterns on biodiversity and establish a scientific basis for the conservation of natural habitats. Additionally, it can facilitate efficient decision-making and planning related to biodiversity conservation and landscape management., (© 2024. The Author(s).)