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Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances

Citation

Oeser, Julian et al. (2020), Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances, Dryad, Dataset, https://doi.org/10.5061/dryad.hdr7sqvdw

Abstract

Disturbances play a key role in driving forest ecosystem dynamics, but how disturbances shape wildlife habitat across space and time often remains unclear. A major reason for this is a lack of information about changes in habitat suitability across large areas and longer time periods. Here, we use a novel approach based on Landsat satellite image time series to map seasonal habitat suitability annually from 1986 to 2017. Our approach involves characterizing forest disturbance dynamics using Landsat-based metrics, harmonizing these metrics through a temporal segmentation algorithm, and then using them together with GPS telemetry data in habitat models. We apply this framework to assess how natural forest disturbances and post-disturbance salvage logging affect habitat suitability for two ungulates, roe deer (Capreolus capreolus) and red deer (Cervus elaphus), over 32 years in a Central European forest landscape. We found that red and roe deer differed in their response to forest disturbances. Habitat suitability for red deer consistently improved after disturbances, whereas the suitability of disturbed sites was more variable for roe deer depending on season (lower during winter than summer) and disturbance agent (lower in windthrow versus bark-beetle-affected stands). Salvage logging altered the suitability of bark beetle-affected stands for deer, having negative effects on red deer and mixed effects on roe deer, but generally did not have clear effects on habitat suitability in windthrows. Our results highlight long-lasting legacy effects of forest disturbances on deer habitat. For example, bark beetle disturbances improved red deer habitat suitability for at least 25 years. The duration of disturbance impacts generally increased with elevation. Methodologically, our approach proved effective for improving the robustness of habitat reconstructions from Landsat time series: integrating multi-year telemetry data into single, multi-temporal habitat models improved model transferability in time. Likewise, temporally segmenting the Landsat-based metrics increased the temporal consistency of our habitat suitability maps. As the frequency of natural forest disturbances is increasing across the globe, their impacts on wildlife habitat should be considered in wildlife and forest management. Our approach offers a widely applicable method for monitoring habitat suitability changes caused by landscape dynamics such as forest disturbance

Methods

GPS telemetry data and Landsat-based spectral-temporal metrics used for creating Maxent habitat models. Dataset includes presence observations (obtained from GPS telemetry) as well as randomly-sampled background points (pseudo-absences). All observations are augmented with spectral-temporal metrics (10th, 50th and 90th percentile) calculated for the Tasseled Cap indices brightness, greenness and wetness using all available Landsat satellite observations within a three-year moving window, which additionally were harmonized across time using the LandTrendr algorithm.

Explanation of data frame columns:

id - Name of deer individual

date - Observation date (MM/DD/YYY)

hour - Daytime hour

year - Year

species - Deer species (red deer / roe deer)

presence - Binary 1/0, indicating whether the observation is a presence location obtained from GPS telemetry or a randomly sampled background point (pseudoabsence)

greenness_p10 - 10th percentile of Tasseled Cap greenness

greenness_p50 - 50th percentile of Tasseled Cap greenness

greenness_p90 - 90th percentile of Tasseled Cap greenness

brightness_p10 - 10th percentile of Tasseled Cap brightness

brightness_p50 - 50th percentile of Tasseled Cap brightness

brightness_p90 - 90th percentile of Tasseled Cap brightness

wetness_p10 - 10th percentile of Tasseled Cap wetness

wetness_p50 - 50th percentile of Tasseled Cap wetness

wetness_p90 - 90th percentile of Tasseled Cap wetness

season - "summer" or "winter", indicating which seasonal habitat model the observation was used for

 

The Google Earth Engine Code for calculating the time series of Landsat-based metrics is provided under the following link:

https://code.earthengine.google.com/?accept_repo=users/julianoeser/Oeser_et_al_2020_Landsat_habitat_dynamics

Funding

Interreg, Award: Ziel ETZ Free State of Bavaria – Czech Republic 2014-2020 (INTERREG V)