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Dryad

The proximity of rapeseed fields influences levels of forest damage by red deer

Cite this dataset

Jarnemo, Anders; Widén, Anna; Månsson, Johan; Felton, Annika M (2022). The proximity of rapeseed fields influences levels of forest damage by red deer [Dataset]. Dryad. https://doi.org/10.5061/dryad.fj6q573xg

Abstract

We investigated the relationship between the level of red deer Cervus elaphus bark stripping damage in 68 Norway spruce Picea abies stands, and the presence of rapeseed Brassica napus fields in the surroundings, hypothesising that damage increases with decreasing distance to rapeseed fields. We also considered other potentially influencing factors, such as supplemental feeding, alternative forage availability, and deer use of spruce stands as indexed by a pellet group count.

Bark stripping rates were measured in 68 planted stands of Norway spruce with a minimum size of 1 ha and an age interval of 20-40 years. We selected, a priori, stands in forestry plans with a minimum of 80 % spruce. However, all stands were planted even-aged monocultures where cleaning of deciduous species had occurred at younger stages, resulting in a spruce proportion generally close to 100 %.  In each stand, 10 circular 100 m2 survey plots were systematically and evenly distributed (with a random starting point). Occurrence of fresh bark-stripping damage (i.e., wounds from preceding winter) was noted for the 10 spruce stems closest to the plot centre (i.e., 100 spruce stems per stand). We measured the distance from the rapeseed fields and feeding stations to the spruce stands by using QGIS.

An index of relative forage availability was measured by estimating percent living vegetation cover of woody browse projected onto the horizontal plane in 20 m2 subplots within targeted stands (the same 10 plots per stand as for the damage survey) and in the surrounding landscape in plots distributed along 500 m transects, one in each of the cardinal directions from the targeted stand (plots distributed at 0, 100, 200, 300, 400, and 500 m from the stand edge (0 m) making a total of 24 transect plots per stand).

The number of red deer pellet groups were counted within targeted spruce stands and in the surrounding landscape to provide indices of relative deer stand usage and overall abundance respectively. Pellet groups were surveyed in 100 m2 circular plots within the stands (the same plots as for damage and forage survey) and in the transect plots used for forage survey described above. Only fresh (from preceding winter) pellet groups were counted.

Spruce stands closer to rapeseed had a significantly higher proportion of damaged stems. The increased level of bark stripping damage was not explained by a higher deer stand use closer to rapeseed fields. Spruce stands closer to supplemental feeding stations had significantly higher damage levels. Damage levels were negatively related to the amount of available browse in the forest.

Methods

Description of methods used for collection/generation of data: Bark stripping rates were measured in 68 planted stands of Norway spruce with a minimum size of 1 ha and an age interval of 20-40 years. We selected, a priori, stands in forestry plans with a minimum of 80 % spruce. However, all stands were planted even-aged monocultures where cleaning of deciduous species had occurred at younger stages, resulting in a spruce proportion generally close to 100 %.  In each stand, 10 circular 100 m2 survey plots were systematically and evenly distributed (with a random starting point). Occurrence of fresh bark-stripping damage (i.e., wounds from preceding winter) was noted for the 10 spruce stems closest to the plot centre (i.e., 100 spruce stems per stand). We measured the distance from the rapeseed fields and feeding stations to the spruce stands by using QGIS.

An index of relative forage availability was measured by estimating percent living vegetation cover of woody browse projected onto the horizontal plane in 20 m2 subplots within targeted stands (the same 10 plots per stand as for the damage survey) and in the surrounding landscape in plots distributed along 500 m transects, one in each of the cardinal directions from the targeted stand (plots distributed at 0, 100, 200, 300, 400, and 500 m from the stand edge (0 m) making a total of 24 transect plots per stand).

The number of red deer pellet groups were counted within targeted spruce stands and in the surrounding landscape to provide indices of relative deer stand usage and overall abundance respectively. Pellet groups were surveyed in 100 m2 circular plots within the stands (the same plots as for damage and forage survey) and in the transect plots used for forage survey described above. Only fresh (from preceding winter) pellet groups were counted.

Spruce stands closer to rapeseed had a significantly higher proportion of damaged stems. The increased level of bark stripping damage was not explained by a higher deer stand use closer to rapeseed fields. Spruce stands closer to supplemental feeding stations had significantly higher damage levels. Damage levels were negatively related to the amount of available browse in the forest. (Jarnemo A, Widén A, Månsson J, Felton A M. 2022. The proximity of rapeseed fields influences levels of forest damage by red deer. Ecological Solutions and Evidence.)

Methods for processing the data: All the data collected was put into excel after the inventory was done in the field. We used the number of undamaged and damaged tree stems in the stands as response variable, thus this variable was not processed or changed from the raw data collected. We took an average number of the red deer pellet groups that was found in the stands, thus an average of the 10 plots inventoried per stand. The same was done with the pellet groups measured along the transects surrounding the stands. Thus, we took an average number of fresh red deer pellet groups found along the 4 transects surrounding the stand (average of the 24 transect plots surrounding the spruce stand).

The forage availability measured in the spruce stands and along the transects were processed before using the data. We first pooled the field layer and shrub layer data into an forage layer variable. Then we took the average of the forage layer per stand, thus the average of the 10 plots in each stand. The same was done for the 4 transects surrounding the field, where we took the average forage layer of the 4 transects (average of the 24 transect plots surrounding the spruce stand).

The distance to closest rape seed field was not processed, it is simply just the distance from the spruce stand edge to the closest rape seed field in kilometers. The same is done with the distance to the closest feeding station.

Instrument- or software-specific information needed to interpret the data: We measured the distance from the rapeseed fields and feeding stations to the spruce stands by using QGIS,  otherwise no ecific software was used to generate the data. Later, all analyses were carried out in R (version 4.0.0). We used generalized mixed effects models (GLMMs, function glmer in lme4 package, (Bates et al. 2015)) with binomial distribution to model bark-stripping damage. We adopted a stepwise deletion approach and verified the removal of variables with likelihood ratio tests (function lrtest in package lmtest). All models were checked for basic assumptions of normality and heterogeneity of residuals using the Dharma package in R (Hartig, 2016).

Standards and calibration information, if appropriate:  not appropriate

Describe any quality-assurance procedures performed on the data: The data was collected by a team of members that were educated in how the data should be collected, we followed already established protocols used by earlier studies in the field. Thus the methods have been used before and are quality assured.

Before the data was used in any final statistical model it was checked for basic assumptions of normality and heterogeneity of residuals using the Dharma package in R (Hartig, 2016).

People involved with sample collection, processing, analysis and/or submission: Anders Jarnemo, Anna Widén, Johan Månsson, Annika M Felton.

Funding

Swedish Environmental Protection Agency

The Swedish Association for Hunting and Wildlife Management