2022
DOI: 10.1007/s10531-022-02432-7
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Distribution and phenology of monarch butterfly larvae and their milkweed hosts in the South Central US

Abstract: South Central US milkweeds (Asclepias) are critical adult nectar and larval food resources for producing the rst spring and last summer/fall generations of declining eastern migratory monarch butter ies (Danaus plexippus). This study addresses multiple gaps in assessment of monarch conservation priorities for the South Central US through analyses of monarch larval host selectivity, phenology, and spatial density, as well as the phenology, niche modeled distribution, and land cover selectivity of important milk… Show more

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Cited by 7 publications
(7 citation statements)
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“…This extent generally agrees with the study extent in Texas used by Ref. 7 and their modelled kernel densities of monarch larvae. We combined the municipalities in Mexico and counties in Texas for a final study extent (Fig.…”
Section: Methodssupporting
confidence: 87%
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“…This extent generally agrees with the study extent in Texas used by Ref. 7 and their modelled kernel densities of monarch larvae. We combined the municipalities in Mexico and counties in Texas for a final study extent (Fig.…”
Section: Methodssupporting
confidence: 87%
“…For example, central Texas counties dominated by urbanization and eastern Texas showed larger declines in nectar and milkweed. These same regions may have the highest spring suitability for adults 3 and the urban counties around San Antonio-Austin and Dallas-Fort Worth were most utilized by caterpillars 7 . More formal analysis of these patterns is needed, but this concordance suggests strategies to enhance habitat in urban areas 44 , 60 and rights of ways in central Texas may help counter the conversion of rangelands and shrublands to urban in these areas.…”
Section: Discussionmentioning
confidence: 99%
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“…The AUC bgp is less than (as much as 8%) or equal to AUC psa , since AUC psa has lower false negative error rates as a result of absence points being represented solely by pseudoabsence points that are buffered a certain distance (2 km in this study) from presence points. In contrast, AUC bgp uses background points as absence points and includes presence points, which thus increases the false negative error rate (see Supplement in Tracy et al 2022). We utilized the following general categorization of AUC values to describe the performance of our nal feature selected models: ≤ 0.5 = none; 0.5 < AUC < 0.7 = poor; 0.7 ≤ AUC < 0.8 = acceptable; 0.8 ≤ AUC < 0.9 = excellent; 0.9 ≤ AUC = outstanding (Hosmer et al 2013).…”
Section: Study Area and Roadkill Observationsmentioning
confidence: 99%