Integrating Habitat Suitability and Quality Assessments to Identify Conservation Priorities for Cycas panzhihuaensis

TL;DR

This study identifies conservation priorities for Cycas panzhihuaensis by combining habitat suitability and quality assessments in the Jinsha River region.

Contribution

The novel integration of MaxEnt and InVEST models provides a spatially explicit framework for conservation planning in human-impacted landscapes.

Findings

Highly suitable habitat for Cycas panzhihuaensis spans 799.12 km2, concentrated in dry-hot valleys of the Jinsha, Yalong, and Anning Rivers.

January land surface temperature is the most significant environmental driver of the species' distribution.

Conservation hotspots show moderate spatial autocorrelation and overlap with areas at risk of habitat degradation.

Abstract

This study assessed the conservation priorities for Cycas panzhihuaensis, a relict plant endemic to the dry-hot valleys of the Jinsha River, by integrating habitat suitability prediction with habitat quality evaluation. We used the MaxEnt model to identify its potential distribution and key environmental drivers and the InVEST model to evaluate habitat quality and degradation risk within the study area. Conservation priorities—categorized as hotspots, transition zones, and coldspots—were delineated by overlaying suitability classes with habitat quality levels. Spatial clustering of hotspots was examined using global spatial autocorrelation analysis. The results indicate that: (1) The highly suitable habitat for C. panzhihuaensis covers an area of 799.12 km2, primarily concentrated in the dry-hot valleys of the Jinsha, Yalong, and Anning Rivers. January land surface temperature was the most significant environmental determinant of its distribution (contribution: 36.1%). (2) The overall habitat quality of the study region was relatively low (mean: 0.38), with a moderate risk of degradation. Areas of severe degradation spanned 14,629.31 km2 (26.10% of the total area), largely coinciding with the river valleys and showing substantial overlap with the species’ suitable habitat. (3) The identified conservation hotspots (799.63 km2) exhibited a moderate and statistically significant positive spatial autocorrelation (global Moran’s I = 0.326). This integrated approach provides a spatially explicit framework for conservation planning, offering valuable insights applicable to other rare species in human-impacted landscapes.

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