Building large-scale spatially explicit models to predict the distribution of suitable habitat patches for the Greater rhea (Rhea americana), a near-threatened species

Abstract

We developed large-scale spatially explicit models to predict the distribution of suitable habitat patches for the Greater rhea (Rhea americana), a near-threatened species, in two areas of central Argentina with different land use: a grassland area (ca. 4943 km2) mainly devoted to cattle grazing and an agro-ecosystem area (ca. 4006 km2) mostly used for crop production. The models were developed using logistic regression and were based on current records of Greater rhea occurrence coupled with remote sensing data, including land cover and human presence variables. The habitat suitability maps generated were used to predict the suitable habitat patch structure for wild rhea populations in each area. Fifty-one percent of the total grassland area was suitable for the species, being represented by a single large patch that included 62% of the individual locations. In the agro-ecosystem, only 28% of the total area was suitable, which was distributed among four patches. Seventy percent of rhea observations were in suitable habitat, with all rheas grouped in the largest patch. Conservation efforts for preserving wild rhea populations should be focused on maintaining habitats similar to grasslands, which are less profitable for landowners at present. Consequently, the protection of the pampas grasslands, a key habitat for this species as well as for others with similar habitat requirements, will demand strong conservation actions through the reconciliation of interests between producers and conservationists, since the proportion of croplands is increasing.