Results for “Journal of Applied Ecology.” — Page 18

USGS2025

Demographic mechanisms of snowshoe hare population cycles in Yukon, Canada

One hundred years have elapsed since Charles Elton (1924) described the periodic fluctuations in North American snowshoe hare abundance, yet mechanisms underlying 9–11-year population cycles in snowshoe hares continue to be debated. We applied multistate capture–mark–recapture models to long-term field data (1977–2020) based on >20,000 captures of >7000 unique snowshoe hares ( Lepus americanus ) from Kluane Lake, Yukon, Canada, to estimate and model state-specific demographic parameters. Juveniles had the lowest and reproductive adult females the highest apparent survival. Apparent survival of all sex-age classes was highest during the mid- and late-breeding seasons and was generally better during the increase phase. Conditional probability of females transitioning from non-reproductive to reproductive state, and reproductive females remaining in the reproductive state, increased substantially as the population transitioned from low to increase phase throughout the breeding season. Analysis of stage-structured matrix population models revealed that population-dynamic characteristics were strongly phase-specific, and also varied across seasons, with the increase phases being characterized by high monthly asymptotic population growth rate. Snowshoe hares experienced short stage-specific generation time during the early breeding season across all phases; they experienced relatively long generation time during the increase and low phase of the mid-breeding season, and the increase and peak phase of the late breeding season. Elasticity analyses showed that asymptotic population growth rate was proportionately most sensitive to changes in survival of adult females across all phases and seasons. However, retrospective life table response experiment analysis showed that rapid growth of the snowshoe hare populations during the increase phase was due to improvements in reproductive transitions and pre-weaning survival, whereas population declines are caused primarily by reduced survival (primarily, pre-weaning survival), with reduced reproductive transitions and smaller litter sizes playing a secondary role. Our results suggest that cyclic populations of snowshoe hares are characterized by complex demographic and population-dynamic patterns, depending on phase of the cycle and reproductive season, and that different demographic mechanisms underlie rapid population growth during the increase phase, and swift population declines as the population transitions from the peak to the decline phase. Because our study represents the first comprehensive demographic and population-dynamic study of a cyclic population, similar studies would be needed to test the generalities of our conclusions. Whereas density-dependent predation has been shown to be the primary cause of phase-related changes in survival, future research should focus on identifying mechanisms underlying phase-related changes in reproductive parameters.

Yukon↗

USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗