Geology topics

Reza Goljani Amirkhiz

Publications and source records attributed to Reza Goljani Amirkhiz.

2 recordsLinked to original sources

USGS2023

Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains

Context Yellow sweetclover ( Melilotus officinalis ; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions causing substantial widespread blooms of YSC across western South Dakota (SD). Objectives We aimed to develop a generalized prediction model to predict the relative abundance of YSC in suitable habitats across rangelands of western South Dakota for 2019. Our research questions are: (1) What is the spatial extent of YSC across western South Dakota? (2) Which model can accurately predict the habitat and percent cover of YSC? and (3) What significant biophysical drivers affect its presence across western South Dakota? Methods We trained machine learning models with in situ data (2016–2021), Sentinel 2A-derived surface reflectance and indices (10 m, 20 m) and site-specific variables of climate, topography, and edaphic factors to optimize model performance. Results We identified moisture proxies (Shortwave Infrared reflectance and variability in Tasseled Cap Wetness) as the important predictors to explain the YSC presence. Land Surface Water Index and variability in summer temperature were the top predictors in explaining the YSC abundance. We demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of this invasive plant. We delineated major YSC hotspots in Butte, Pennington, and Corson Counties of South Dakota. The floodplains of major rivers, including White and Bad Rivers, and areas around Badlands National Park also showed a higher occurrence probability and cover percentage. Conclusions These prediction maps could aid land managers in devising management strategies for the regions that are prone to YSC outbreaks. The management workflow can also serve as a prototype for mapping other invasive plant species in similar regions.

Montana, North Dakota, South Dakota, Wyoming↗

USGS2018

Predicting spatial factors associated with cattle depredations by the Mexican wolf (Canis lupus baileyi) with recommendations for depredation risk modeling

Aim Predation on livestock is one of the primary concerns for Mexican wolf ( Canis lupus baileyi ) recovery because it causes economic losses and negative attitudes toward wolves. Our objectives were to develop a spatial risk model of cattle depredation by Mexican wolves in the USA portion of their recovery area to help reduce the potential for future depredations. Location Arizona and New Mexico, USA. Methods We used a presence-only maximum entropy modeling approach (Maxent) to develop a risk model based on confirmed depredation incidents on public lands . In addition to landscape and human variables, we developed a model for annual livestock density using linear regression analysis of Animal Unit Month (AUM), and models for abundance of elk ( Cervus canadensis ), mule deer ( Odocoileus hemionus ) and white-tailed deer ( Odocoileus virginiana ) using Maxent, to include them as biotic variables in the risk model. We followed current recommendations for controlling model complexity and other sources of bias. Results The primary factors associated with increased risk of depredation by Mexican wolf were higher canopy cover variation and higher relative abundance of elk. Additional factors with increased risk but smaller effect were gentle and open terrain, and greater distances from roads and developed areas. Main conclusions The risk map revealed areas with relatively high potential for cattle depredations that can inform future expansion of Mexican wolf distribution (e.g., by avoiding hotspots) and prioritize areas for depredation risk mitigation including the implementation of active non-lethal methods in depredation hotspots. We suggest that livestock be better protected in or moved from potential hotspots, especially during periods when they are vulnerable to depredation (e.g. calving season). Our approach to create natural prey and livestock abundance variables can facilitate the process of spatial risk modeling when limitations in availability of abundance data are a challenge, especially in large-scale studies.

Arizona, New Mexico↗