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Data from: Deer-mediated ecosystem service vs. disservice depends on forest management intensity


Stokely, Thomas D.; Betts, Matthew G. (2020), Data from: Deer-mediated ecosystem service vs. disservice depends on forest management intensity, Dryad, Dataset,



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.


  1. Description of methods used for collection/generation of data: 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).
    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.
    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.
    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.
    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).
  2. Methods for processing the data:
    • 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.
  3. Environmental/experimental conditions:
    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.
    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.
    Camera traps were deployed from May-October for each year.

Usage Notes

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.


United States Department of Agriculture, Agriculture and Food Research Initiative grant, Award: AFRI-2009-04457 & AFRI-2015-67019-23178

National Council for Air and Stream Improvement, Inc.

Oregon Forest Industries Council

Oregon State University, College of Forestry