DOI: 10.5061/dryad.8cz8w9gkd
title: Data from: Deer-mediated ecosystem service vs. disservice depends on forest management intensity

description: MANUSCRIPT ABSTRACT As global terrestrial biodiversity declines via
 land-use intensification, society has placed increasing value on
 non-commercial species as providers of ecosystem services. Yet, many deer
 species and non-crop plants are perceived negatively when they decrease
 crop productivity, leading to reduced economic gains and human-wildlife
 conflict. We hypothesized that deer provide an ecosystem service in forest
 plantations by controlling competition and promoting crop-tree growth,
 although the effects of herbivory may depend on forest management
 intensity. If management negatively affects foraging habitat at local and
 landscape scales, then we expected browsing to shift to less-palatable
 crop trees. To test these hypotheses, we established a 5-year experiment
 that manipulated early forest management intensity via herbicide
 treatments and access of two deer species to vegetation via exclosures.
 Contrary to our hypothesis, deer provided an ecosystem service at high
 management intensities and a disservice occurred with low-intensity
 management. Crop-tree growth and survival was greatest when herbivory and
 herbicides suppressed broadleaf regeneration. In contrast, crop-tree
 growth was lowest when broadleaf vegetation was retained and crop-trees
 were subject to both browse damage and competition. We found a positive,
 yet variable, association between deer detections and stand- and
 landscape-scale broadleaf habitat, and despite initial reductions in
 forage, herbivory pressure was similar among management intensities. When
 broadleaf vegetation was suppressed by herbicides and herbivory, selection
 of herbaceous forage by deer intensified, likely aiding in the service.
 Overall, our findings indicate that the effects of vegetation management
 for promoting timber production are highly dependent on the presence of
 large herbivores. Synthesis and applications: Although deer are thought to
 reduce crop productivity in many systems, we found that herbivory switched
 from reducing crop tree growth where non-crop vegetation was retained, to
 promoting crop tree growth when both herbivory and herbicides suppressed
 competing vegetation. However, the provision of this ecosystem service is
 likely contingent on the amount of forage available in the landscape and
 subsequent foraging pressure. We conclude that nature's capacity to
 provide ecosystem services depends on the intensity of management at local
 and landscape scales.
description: <strong>Description of methods used for
 collection/generation of data</strong>: The experiment was conducted
 using a repeated-measures, split-plot, complete-block design, with three
 fixed factors, deer and elk exclusion plots (225m^2 plots) herbicide
 treated stands (~13 ha stands) and time (5 sampling seasons).<br>
 Within each exclosure and open plot (allowing deer and elk access), we
 repeatedly measured tagged crop trees (planted Pseudotsuga menziesii). For
 each crop tree, we measured the bole diameter at 10 cm from the root
 collar, height from the uphill side of each tree and presence or absence
 of browse damage. From 12 1x1m quadrats, systematically located within
 each open and excluded plot, we measured the visual cover by plant species
 and calculated the average height per species per plot. In open plots, we
 measured the presence or absence of browse damage for each species in each
 1x1m quadrat.<br> In the open plot, we deployed a camera trap
 (Bushnell Trophy Camera, model 119436) and set the camera to capture
 continuously when triggered by motion and body heat. Camera traps were in
 operation from May-October for each year from 2012 to 2015.<br> In
 2014 for excluded and open plots, we also planted two big-leafed maple
 seedlings (Acer macrophyllum) to test the effects of herbivory on a
 consistently planted broadleaf species. In the fall of 2015, we clipped,
 dried (55 deg C) and weighed the aboveground biomass of each living
 maple.<br> With the help of Zhiqiang Yang, we used Gradient Nearest
 Neighbor Analysis to identify 30x30 m pixels, defined as early
 successional broadleaf habitat (i.e. mean quadratic diameter less than
 10cm and with dominant broadleaf composition). 
 <strong>Methods for processing the data</strong>: To estimate crop
 tree volume (CROP.VOLUME), we calculated the bole-only volume of a cone
 (1/3*pi*r^2*height) and summed the volume among all trees, divided by the
 area sampled to obtain volume/ha. To estimate crop
 tree basal area (CROP.BASAL), we calculated the cicular area of each tree
 bole (pi*r^2) and summed the area among all trees To
 estimate crop tree height (CROP.HEIGHT), we took an average height of all
 crop trees. To estimate crop tree survival
 (CROP.ALIVE; CROP.DEAD), we tabulated the number of alive and dead trees,
 which were tagged in the beginning of the experiment.
 To estimate the cover of herbaceous (HERB.COVER), fern
 (FERN.COVER), broadleaf (BROAD.COVER) and forage (FORAGE.COVER) and
 non-forage species (NONFORAGE.COVER), we summed the plot-level cover of
 all species within each group, as defined in Appendix S2, Table
 S1. To estimate the average height of herbaceous
 (HERB.HEIGHT), broadleaf and fern (FERN.HEIGHT) species (BROAD.HEIGHT), we
 took an average height of all species within each group across all
 quadrats. To estimate planted maple biomass
 (MAPLE.BIOMASS), we added the biomass measurements between the two clipped
 maples. To estimate deer and elk detections from
 camera traps (DEER.DETECTIONS), we summed the total number of photos taken
 of individual deer and elk, divided by the number of days that cameras
 were operating to obtain an average number of photos taken per
 day. To estimate stand-scale broadleaf abundance
 (BROADLEAF.STAND), we took the average cover of broadleaf species between
 both open and excluded plots. To estimate
 landscape-scale broadleaf abundance (BROADLEAF.LANDSCAPE), we summed the
 number of pixels defined as broadleaf habitat within a 5km radius from
 each exclosure plot. To estimate the frequency of
 browsed and non-browsed broadleaf species (BROWSE.BROADN, BROWSE.BROADY,
 respectively) and browsed and non-browsed herbaceous species
 (BROWSE.HERBY, BROWSE.HERBN), we tabulated the number of species among all
 quadrats across all years with and without evidence of deer
 herbivory. To estimate the frequency of browsed and
 non-browsed crop trees (BROWSE.CROPY, BROWSE.CROPN), we summed the number
 of browsed and unbrowsed crop trees across all years.
 
 <strong>Environmental/experimental
 conditions</strong>:<br> Non-crop vegetation measurements were
 conducted during the peak in vegetation production for each year
 (~June-August), corresponding to the dry summer season in the
 region.<br> For crop trees, we took measurement in the fall, when
 crop trees were at the peak in annual growth, corresponding to the moist
 fall and winter in the region.<br> Camera traps were deployed from
 May-October for each year.
description: This dataset consists of planted crop-tree growth metrics
 (Pseudotsuga menziesii), non-crop tree vegetation metrics, and foraging
 data for black-tailed deer (Odocoileus hemionus columbianus) and Roosevelt
 elk (Cervus canadensis rooseveli), collected from the Intensive Forest
 Management experiment, Oregon Coast Range, USA, 2011-2015. The objective
 of the experiment was to quantify the effects of silvicultural herbicide
 treatments on biodiversity and ecosystem functions. NA
 = Data were either missing (i.e. no variable to measure) or not measured
 for the specific row (i.e., Year and Plot). See
 Readme_Document.txt, which is included with this dataset, for more
 information.