DOI: 10.5061/dryad.mcvdnck30
title: Model data to investigate wood frog abundance in 17-year post harvest variable retention mixed wood forests

description: Variable retention forest harvesting aims to reduce negative effect of
 harvesting on forest biodiversity, but its effectiveness is not well
 understood for many taxa. To better understand the effects of
 variable retention forest management and environmental features on
 amphibians, we used pitfall traps to capture wood frogs (Lithobates
 sylvaticus) across 4 levels of retention harvest (clearcut [0%], 20%, 50%,
 and unharvested control [100%]), and 2 forest types (deciduous and
 coniferous), in 17-year post-harvest forests in northwest Alberta. We
 mapped breeding sites and used a terrain moisture index (Depth-to-Water)
 derived from airborne LiDAR to examine relationships between relative
 abundance, breeding site proximity and soil moisture. Retention level
 alone had no effect on relative abundance, but in late summer (July and
 August) there was a significant interaction between retention level and
 forest type: capture rates decreased with amount of retention for
 deciduous forests, but increased with amount of retention in conifer
 forests. During late summer, capture rates were higher in conifer forests
 than in deciduous forests, with soil moisture (lower Depth-to-Water)
 positively related to capture rates. Though timber retention may be
 beneficial to wood frogs in the short-term, any impacts of forest
 harvesting on wood frog abundance was undetectable in stands 17 years
 post-harvest.  
description: The dataset was collected at the EMEND forestry experiment in
 northwest Alberta in 2014 and 2015.  Pitfall trapping
 arrays were used to capture wood frogs (Lithobates sylvaticus) across 4
 levels of retention harvest (clearcut [0%], 20%, 50%, and unharvested
 control [100%]), and 2 forest types (deciduous and coniferous), in 17-year
 post-harvest forests in northwest Alberta. We mapped breeding sites and
 used a terrain moisture index (Depth-to-Water) derived from airborne LiDAR
 to examine relationships between relative abundance, breeding site
 proximity and soil moisture.  General Linear Mixed
 models (GLMMs) were only used with 2015 data as this dataset covered the
 entire wood frog active season (May to August) and included more study
 sites (4) and more pitfall arrays (64).  Sampling in 2014 was only
 performed in two study sites (one deciduous-dominated and one
 conifer-dominated), and was only done from July to August. 
 WAM is an airborne LiDAR-based terrain moisture model that
 predicts expected flow channels and associated water accumulation (depth
 to water) patterns at 1 m resolution of digital elevation. Mean DTW was calculated using the focal
 statistics tool in ArcMap 10.3 from 20 m circular buffers around each
 pitfall array which prevented overlapping buffers between adjacent
 arrays.  A total of 64 pitfall trapping arrays were
 sampled across 4 study sites in 2015, and individual arrays acted as the
 independent unit of observation for analysis. Total captures were
 converted to Catch-Per-Unit-Effort (CPUE) and natural log (Ln) transformed
 to meet assumptions of General Linear Mixed Models (GLMMs). A constant of
 10 was added to all CPUE values prior to
 transformation.  Breeding proximity
 (distance of each array to nearest breeding site) and DTW were
 normalized/standardized for analysis by calculating z-scores. When running
 models, R sometimes warns you that variables are on very different scales
 and to consider rescaling. Normalizing or standardizing your variable
 gives you it values in standard deviations (standardized values measured
 as distance from the mean).  z-score = (raw value -
 mean)/standard deviation
description: Dataset was used wih R version 4.1.2