Monarch Conservation Toolbox

Pilot Projects

Western Monarch Habitat Suitability Model

U.S. Fish and Wildlife Service (USFWS) | Xerces Society

United States


Agency Type

Target or Affiliated Species or Habitat

Original Language

In a large-scale effort to better understand the habitat distribution and population dynamics of the less-understood but still imperiled Western monarch population, the USFWS is collaborating with the Xerces Society for Invertebrate Conservation to compile a database of milkweed presence and monarch occurrence across an 11-state range west of the Rocky Mountains. “Using MaxEnt software, a coarse-scale habitat model and suitability index will be created.

Data collected in 2015 and high resolution pre-2015 datasets, along with key environmental variables the species depends on, will be used to train and validate this model. Modeling results will highlight key western monarch migration and breeding areas, thus allowing various agencies and organizations the ability to better prioritize monarch conservation efforts. If time and sufficient data allow, mid-scale or fine-scale models will be created for some regions, thus providing higher resolution landscape information for refining survey and restoration efforts. An emphasis will be placed on making all resulting data easy to discover and access online.

Data layers resulting from the analysis will be accessible through an online geodata portal, along with a report detailing the project and findings” (Caldwell 2015). Final model results include “relative habitat suitability models of five milkweed species thought to be important to western monarchs that enough data points to allow for creation of reasonably robust models. The species are Asclepias speciosa [showy milkweed], Asclepias fascicularis [narrowleaf milkweed], Asclepias eriocarpa [woollypod milkweed], Asclepias asperula [antelope horn or spider milkweed], and Asclepias cordifolia [heartleaf milkweed] […] These models are imperfect because they are based on limited and spatially biased data, but it is appropriate to use them to help plan prioritize habitat improvement actions at a regional scale, and to identify areas for 2016 surveys” (Eastbrook 2016).

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