Burning and mowing ground vegetation for capercaillie Tetrao urogallus conservation: an experimental test.

MARK H. HANCOCK*, RON W. SUMMERS*, ANDY AMPHLETT*, ROBERT PROCTOR*, PETER HARVEY+ and STIJN BIERMAN‡

*Royal Society for the Protection of Birds Scotland, Etive House, BeechwoodPark, Inverness, IV2 3BW, UK. +32 Lodge Lane, Grays, Essex, RM16 2YP, UK. ‡Biomathematics & Statistics Scotland, The King's Buildings, EdinburghEH9 3JZ, UK

Correspondence: Mark Hancock, email , phone 01463 715000, fax 01463 715315.

Running title: Burning and mowing for capercaillie

Word count: text (excluding Supplementary Material): 6604, tables: 372, total 6976.

Summary

1. Populations of the capercaillie Tetrao urogallus, a forest grouse of conservation and economic importance, have declined over much of its range, often due to poor breeding success. Burning or mowing of ground vegetation could increase breeding success, if followed by increases in bilberry Vaccinium myrtillus and associated arthropods, important to capercaillie chicks. We carried out an experiment testing these management techniques at Abernethy Forest, Scotland.

2. Twenty-five experimental blocks were established within semi-natural pinewood with a heather Calluna vulgaris and Vaccinium spp. shrub layer. Each block held three 700m2 plots, randomly assigned to control, mow and burn. Vegetation, arthropods and capercaillie usage were monitored for one year before, and three years after treatment.

3. Bilberry cover increased in mown and burnt areas, but there were also increases in controls, linked to unusual natural heather die-back. Consequently, there was no treatment effect on bilberry cover. Modelling a hypothetical scenario without heather die-back, suggested that, had it not occurred, there would have been significant treatment differences. For this ‘no die-back’ scenario, bilberry cover in burnt and mown plots increased after three growing seasons to 23% (95% confidence intervals 1335%), compared to control estimates of 13% (7-23%).

4. There were treatment effects on the biomass of some arthropod groups important in the diet of capercaillie chicks, but, when modelling the ‘no die-back’ scenario, most of these effects disappeared. Detection-corrected counts of summer grouse dung (mainly capercaillie) were 4.3-6.3 (confidence intervals 2.5-9.8) times higher in treated plots than controls.

5. Synthesis and applications. In forests with a heather-Vaccinium shrub layer, in the absence of unusual natural heather die-back, burning or mowing ground vegetation is likely to improve capercaillie habitat quality, by increasing the cover of a key plant (bilberry) within three years. However, increases in arthropod food abundance for capercaillie chicks, and longer-term vegetation responses, are uncertain, and will need to be re-assessed after longer-term study of the experimental areas.

Keywords: arthropods, bilberry Vaccinium myrtillus, disturbance, dung counts, grouse (Aves: Tetraonidae), heather Calluna vulgaris, pine forest, prescribed fire, Scotland.

Introduction

The capercaillie Tetrao urogallus L., a forest grouse highly valued by conservationists and hunters across much of temperate/boreal Eurasia, has suffered widespread population decline (Storch 2001), often linked to poor reproduction (Moss et al. 2000; Wegge et al. 2005). In Scotland, rapid decline led to fears of regional extinction (Moss 2001). Habitat management is considered key to capercaillie conservation (Storch 2001). An important habitat element is bilberry Vaccinium myrtillus L.,with which capercaillie broods are strongly associated, probably because it supports abundant arthropod food (Storch 1994; Summers et al. 2004; Wegge et al. 2005). Capercaillie at more bilberry-rich sites, have higher breeding productivity (Baines, Moss & Dugan 2004).

The ground vegetation at capercaillie sites commonly comprises bilberry and other ericaceous shrubs, like heather Calluna vulgaris L.,and cowberry Vaccinium vitis-idaea L. (Storch 2001). In Scottish semi-natural pinewoods, heather often dominates (Steven & Carlisle 1959; Summers et al. 1999), particularly in the high light environment typical of these areas (Parlane et al. 2006), which is often linked to past human impacts (Summers et al. 1999).

Post-fire succession on heather moorland sometimes includes a prolonged phase of increased Vaccinium abundance (Ritchie 1955; Hobbs & Gimingham 1984). We wished to test whether this might also occur in pinewoods. If so, it could improve capercaillie habitat quality, and support the introduction of prescribed fire into pinewood conservation management. This would support the contention that ecological boreal forest management should include the emulation of some forms of natural disturbance, such as fire (Angelstam 1998; Perera, Buse & Weber 2004). Forest management with fire is novel in the UK (Bruce & Servant 2003). The Scottish semi-natural pinewood resource is severely depleted (Anon 1994), and there were concerns that prescribed fire could be unsafe or impractical in this habitat. Therefore, we also tested mowing, which can produce similar effects to fire (Cotton & Hale 1994; Schimmel & Granström 1996). Our aims were to determine whether burning and/or mowing, within a pinewood with Calluna-Vaccinium ground vegetation, would lead to (i) increased cover of bilberry; (ii) increased biomass of arthropods important to capercaillie chicks; and (iii) increased usage by capercaillie.

Methods

STUDY AREA AND EXPERIMENTAL DESIGN

The study took place at AbernethyForest nature reserve (3°37’W, 57°14’N), within the CairngormsNational Park in the Scottish Highlands (Fig. 1a). The reserve includes c 4000ha of Scots pine Pinus sylvestris L. forest, on predominantly peaty soils (Steven & Carlisle 1959; Summers et al. 1997). The only large herbivores are roe deer Capreolus capreolus L. and red deer Cervus elaphus L., each at densities around 8km2 (unpubl. data). At the nearest weather station (12km to west, 228m altitude) during 1994-2003, mean annual rainfall was 1060mm. January and July mean temperatures were 2.4 and 13.9°C respectively.

Twenty-five experimental blocks (Fig. 1b) were selected at random within ‘old, open forest’ (‘Box 2-3’: Picozzi, Catt & Moss1992), at altitudes of 250-400m. These comprised mainly P. sylvestris-Hylocomium splendens-Vaccinium woodland (W18b: Rodwell 1991). Blockscale densities of mature trees (modal height 17m) averaged 174ha1 (range 62-378). Each block consisted of three, 20m x 35m plots, separated by 10m (Fig. 1c). Within each plot, eight 5mx 5 m quadrats were used for recording (Fig. 1d). One plot per block was assigned at random to control, burning, and mowing. Baseline measurements took place in 2002, treatments were applied in spring 2003, and further monitoring took place in 20035.

EXPERIMENTAL TREATMENTS

Plots were burnt in strips, with water used to protect plot boundaries and features such as pine saplings (Dugan 2004). Mowing was by handheld, metal-bladed strimmer. Because the availability of suitable prescribed burning weather was uncertain, mowing only took place at a block after the ‘burn’ plot had been burnt.

Treatment characteristics were expected to vary in ways that might influence subsequent succession (Schimmel & Granström 1996). Some of this variation might be amenable to management control. Therefore, various treatment characteristics were measured. For fires, we estimated ‘fireline intensity’ (Byram 1959) from three flame-length estimates per quadrat. To aid estimation, ‘fire canes’ (graduated 2.5mx2cm steel tubes) were fixed vertically at quadrat centres before fires. ‘Depth of burn’ (Schimmel & Granström 1996) was measured by marking the moss/litter surface at four points per quadrat using small metal posts, and measuring any reduction in height after the fire. Soil heating can strongly affect vegetation succession (Schimmel & Granström 1996), with 10 minutes at c 55°C being lethal for bilberry rhizomes (Granström & Schimmel 1993). Therefore, two simple measures of heat duration were taken: firstly, time above 55°C was measured using timers linked to thermocouples fixed on the moss/litter surface, 20cm from each fire cane; and secondly, by direct observation of the duration of flames touching fire canes. For mowing, in the summer after treatment, we measured the height above the moss/litter surface at which stems were cut, and the cover of severed material.

VEGETATION SURVEYS

Ground vegetation was surveyed in August-September 2002-5, by a single observer (MH), using a 2m x 25mmx5mm graduated stick, used in other fire studies (Davies et al. in press). At four sample points per quadrat, the stick was pushed vertically into the moss/litter, down to the soil/humus surface. The maximum height within 5cm of the stick was measured for shrubs and moss/litter. Vegetation structure was characterised by standing with the vertical stick at arm’s length, and estimating the percentage of the stick visible in a series of 10cm bands. This gave an index of vegetation openness at various levels above the soil/humus surface. The value of this index for a 10cm band centred at the surface of the moss/litter, termed ‘ground-level openness’, was estimated by interpolation. The total cover (including cover below other species) was estimated for shrub species inside a 1m radius circle centred at each point. Young pines (under 1.5m) were counted within the same circle. Cover was scored separately for heather that was live (green), recently-dead (brown), long-dead (grey), or shoot or seedling regeneration. Bilberry defoliation, which affects cover estimates, was estimated as the proportion of the 10 bilberry shoots nearest the vertical stick that were completely without leaves.

Before treatments were applied, we measured the positions and heights of all trees over 1.5m, within plots and adjacent 5m buffer zones. For pines, we recorded the proportion of foliage within three height bands: 02m, 25m and over 5m. The proportion of foliage that was brown was recorded within the same height bands, before and after fires. Sub-canopy light level was calculated using tree densities and heights, and the regression equation of Parlane et al. (2006).

ARTHROPOD SURVEYS

Arthopods were surveyed in June, when insectivorous capercaillie chicks are present (Summers et al. 2004). Our main sampling technique was pitfall trapping, which despite its potential biases, remains a practical and commonly-used technique (Southwood & Henderson 2000; Saint-Germain et al. 2007), including for studies of capercaillie diet (Summers et al. 2004). To supplement pitfall data, we also carried out some direct counts of arthropods. Direct counts of caterpillars were found by Atlegrim & Sjöberg (1995), to be positively correlated with foraging success of tame capercaillie chicks.

Pitfall traps were 150ml polyethylene containers, with a 46mm opening diameter, two-thirds filled with trapping solution (ethylene glycol diluted 1:3, with one drop of detergent). Traps were set at each block, at all quadrat centres, on randomly-selected dates during the first half of June, and collected 14 days later. Four arthropod groups of importance in capercaillie chick diet (Kastdalen & Wegge 1985; Spidsø & Stuen 1988; Picozzi, Moss & Kortland1999; Summers et al. 2004) were extracted and counted: spiders (Araneae), beetles (Coleoptera), caterpillars (Lepidoptera), and ants (Hymenoptera: Formicidae). Caterpillars were measured to the nearest mm. Adult ants, beetles, and spiders were identified to species. For traps that caught many Formica ants, a random sample of 20 was identified. Other Formica were counted and assumed to comprise similar species proportions.

Direct arthropod counts were carried out at every quadrat during June in the last two years of the study. The observer crouched near the quadrat centre and, for one minute, recorded any arthropods observed within 50cm of the centre marker. Arthropods were counted by group, and sized by eye to the nearest mm.

Different arthropod groups may have different trapping bias (Southwood & Henderson 2000), attractiveness to capercaillie (Kastdalen & Wegge 1985), or effects on chick survival (Picozzi, Moss & Kortland1999). Therefore, biomass was estimated separately by group, using the approach of Saint-Germain et al. (2007). Counts by species, or size class for caterpillars, were determined for each plot. Median adult body lengths were collated from published keys. The dry mass for an individual of each species (or caterpillar size class) was estimated using published log(length)-log(mass) regression coefficients (Rogers, Buschbom & Watson 1977; Gowing & Recher 1984; Sample et al. 1993; Hódar 1996; Ganihar 1997), with back-transformation correction (Sprugel 1983). The individual body mass of each species was estimated as the mean of values given by all available regressions. These were multiplied by count to give the biomass of each species in each plot. Species biomasses were then summed to give the biomass for the whole group. For sexually-dimorphic spider species, calculations were done separately by sex.

MEASURING CAPERCAILLIE USAGE

Dung counts were carried out in May and October each year, to provide a measure of capercaillie usage. Dung found in May was cleared, so that October counts primarily represented summer accumulation. This is the period when ground vegetation is most used by capercaillie (Storch 2001; Summers et al. 2004). Dung counts involved searching a 2m radius area centred at the quadrat centre for five minutes. The number and diameter of pellets in each dung group were recorded. We could not assume that dung groups would be perfectly detected, and therefore quantified detection rate as a function of vegetation openness using a trial with dummy dung. Detection rate was then estimated for each plot in each year from vegetation openness data (Supplementary Material). Black grouse Tetrao tetrix L. were also present and their dung overlaps in size with that of capercaillie (Brown et al. 1987). Therefore, we collated incidental grouse sightings, to see how commonly they occurred.

DATA ANALYSIS

Statistical analyses were used to investigate treatment effects on changes in bilberry cover, arthropod biomass, and capercaillie usage, between the base-line year (2002: pre-treatment) and post-treatment years. In all cases, we used linear mixed models in SAS (SAS Inst., 2000), assuming that errors were normally distributed (after transformation of the response), except for capercaillie usage (dung count) data where a generalized linear mixed model with Poisson errors was used. The significance of explanatory variables was estimated using F-tests with the denominator degrees of freedom estimated by the Satterthwaite approximation. Model assumptions were assessed visually by examining probability plots and plots of residuals against predicted values. The normality assumption was checked for random effects and model residuals. In some cases the response variables were transformed in order to achieve normality (as indicated below).

Differences between treatments in the change in bilberry cover from the baseline year (2002) to three years after the application of treatments (2005), were estimated using the following linear mixed model:

Yi,j = a0 + a1*Xi,j + a2*X2i,j + a3*Si,j + a4*Vi,j + a5*Li,j + Bi + Tj (1)

withYi,j and Xi,j the total bilberry cover (arc-sine fourth-root transformed to achieve normality of model residuals) in 2005 and 2002 respectively, as measured in the plot with treatment j (j=1(control), 2(mown), or 3(burnt)) in block i (i=1,2,…25). Si,j and Vi,j are the difference in mean defoliation score and visit date between these years (2005 minus 2002) and Li,j is the light index. These were included because these have been found to affect bilberry cover by Parlane et al. (2006) (Vi,j and Li,j) or were assumed to do so (Si,j). Bi is a random effect representing potential block effects (a spatial factor). The main parameters of interest are the treatment effects Tj. We included baseline bilberry cover squared as a covariate, as we expected, and observed, greater change in bilberry cover where initial cover was further from its minimum and maximum possible values of 0% and 100%.

The experiment was affected by an unusual natural heather die-back event (Hancock in press), roughly synchronous with the application of experimental treatments in spring 2003. This was probably caused by exceptional weather conditions, combined with the maturity of heather at the site. We expected heather die-back to lead to increases in bilberry cover, due to evidence of competitive effects (Parlane et al. 2006). Therefore, as well as the observed experimental results, we wished to estimate treatment effects for a hypothetical situation where this die-back event had not occurred, termed the ‘no die-back scenario’. Die-back was calculated as the proportion, in control plots, of heather cover in summer 2003, that was brown (recently dead). To model bilberry response under the hypothetical ‘no die-back scenario’, the heather die-back scores were added as covariates to eqn 1:

Yi,j = a0 + a1*Xi,j + a2*X2i,j + a3*Si,j + a4*Vi,j + a5*Li,j + Bi + Tj + a6*Di + bj*Di (2)

where the coefficients a6 and bj are the parameters for the slopes for Di, the arc-sine square-root transformed heather die-back score at the control area of block i in 2003. Other terms are as in eqn 1.

Differences between treatments in change in arthropod biomass in pitfall traps, from the baseline year (2002) to each of the three post-treatment years, were estimated using the following linear mixed model:

Zi,j,t = a0 + a1*Mi,j + Yt + Bi + Tj + TYj,t(3)

where Zi,j,t and Mi,j are respectively the post-treatment and pre-treatment biomass estimates for the invertebrate groups of interest in plots with treatment j in block i, and post-treatment year t (t=2003, 2004 or 2005). Yt is a categorical variable for the post-treatment year-effect, TYj,tis a nine-level categorical variable representing the interaction between treatment j and year t, and other variables are as eqn 1. Visual inspection of residual plots from these models suggested that errors could be assumed to be normally distributed after a loge transformation for the spider and ant biomass data, and a fourth-root transformation for beetle and caterpillar data. Therefore we used these transformations for the variables Zi,j,t and Mi,j.

We also expected natural heather die-back (see above) to affect arthropod responses, due to consequent changes in vegetation structure, impacts on populations of herbivorous arthropods feeding on heather, and increases in dead plant material available to detritivores. Therefore, analogous to the bilberry cover analysis (eqn 2), we adapted eqn 3 to estimate differences between treatments in change in arthropod biomass under the ‘no die-back scenario’, by adding the heather die-back scores as covariates:

Zi,j,t = a0 + a1*Mi,j + Yt + Bi + Tj + TYj,t + a2*Di + dj*Di + et*Di + fj,t*Di (4)

The coefficients dj, et and fj,t are the treatment-specific, year-specific, and the treatment-by-year-specific slopes for the heather die-back scores Di, respectively.

Direct arthropod counts were analysed in the same way except that no baseline data were available.

To investigate differences between treatments in capercaillie usage, we estimated differences in autumn grouse dung counts between plots with different treatments, in the three post-treatment years. Baseline (pre-treatment) data were not included as these showed an unexpected pattern of high counts in control plots (Results) which could have led to estimates of treatment effects that were unduly positive. As there were many zero counts, only blocks in years with at least one grouse dung group recorded were included in the analysis. We estimated relative differences in dung frequencies between treatments by fitting the following model: