Cutting and burning ground vegetation for capercaillie Tetrao

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Burning and mowing ground vegetation for capercaillie
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Tetrao urogallus conservation: an experimental test.
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MARK H. HANCOCK*, RON W. SUMMERS*, ANDY AMPHLETT*, ROBERT
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PROCTOR*, PETER HARVEY+ and STIJN BIERMAN‡
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* Royal Society for the Protection of Birds Scotland, Etive House, Beechwood Park,
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Inverness, IV2 3BW, UK.
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‡
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UK
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32 Lodge Lane, Grays, Essex, RM16 2YP, UK.
Biomathematics & Statistics Scotland, The King's Buildings, Edinburgh EH9 3JZ,
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Correspondence: Mark Hancock, email [email protected], phone 01463
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715000, fax 01463 715315.
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Running title: Burning and mowing for capercaillie
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Word count: text (excluding Supplementary Material): 6604, tables: 372, total 6976.
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Summary
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1. Populations of the capercaillie Tetrao urogallus, a forest grouse of conservation
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and economic importance, have declined over much of its range, often due to poor
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breeding success. Burning or mowing of ground vegetation could increase breeding
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success, if followed by increases in bilberry Vaccinium myrtillus and associated
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arthropods, important to capercaillie chicks. We carried out an experiment testing
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these management techniques at Abernethy Forest, Scotland.
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2. Twenty-five experimental blocks were established within semi-natural pinewood
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with a heather Calluna vulgaris and Vaccinium spp. shrub layer. Each block held
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three 700 m2 plots, randomly assigned to control, mow and burn. Vegetation,
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arthropods and capercaillie usage were monitored for one year before, and three years
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after treatment.
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3. Bilberry cover increased in mown and burnt areas, but there were also increases in
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controls, linked to unusual natural heather die-back. Consequently, there was no
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treatment effect on bilberry cover. Modelling a hypothetical scenario without heather
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die-back, suggested that, had it not occurred, there would have been significant
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treatment differences. For this ‘no die-back’ scenario, bilberry cover in burnt and
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mown plots increased after three growing seasons to 23% (95% confidence intervals
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13-35%), compared to control estimates of 13% (7-23%).
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4. There were treatment effects on the biomass of some arthropod groups important
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in the diet of capercaillie chicks, but, when modelling the ‘no die-back’ scenario, most
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of these effects disappeared. Detection-corrected counts of summer grouse dung
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(mainly capercaillie) were 4.3-6.3 (confidence intervals 2.5-9.8) times higher in
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treated plots than controls.
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5. Synthesis and applications. In forests with a heather-Vaccinium shrub layer, in the
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absence of unusual natural heather die-back, burning or mowing ground vegetation is
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likely to improve capercaillie habitat quality, by increasing the cover of a key plant
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(bilberry) within three years. However, increases in arthropod food abundance for
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capercaillie chicks, and longer-term vegetation responses, are uncertain, and will need
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to be re-assessed after longer-term study of the experimental areas.
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Keywords: arthropods, bilberry Vaccinium myrtillus, disturbance, dung counts, grouse
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(Aves: Tetraonidae), heather Calluna vulgaris, pine forest, prescribed fire, Scotland.
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Introduction
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The capercaillie Tetrao urogallus L., a forest grouse highly valued by conservationists
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and hunters across much of temperate/boreal Eurasia, has suffered widespread
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population decline (Storch 2001), often linked to poor reproduction (Moss et al. 2000;
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Wegge et al. 2005). In Scotland, rapid decline led to fears of regional extinction
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(Moss 2001). Habitat management is considered key to capercaillie conservation
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(Storch 2001). An important habitat element is bilberry Vaccinium myrtillus L., with
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which capercaillie broods are strongly associated, probably because it supports
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abundant arthropod food (Storch 1994; Summers et al. 2004; Wegge et al. 2005).
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Capercaillie at more bilberry-rich sites, have higher breeding productivity (Baines,
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Moss & Dugan 2004).
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The ground vegetation at capercaillie sites commonly comprises bilberry and other
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ericaceous shrubs, like heather Calluna vulgaris L., and cowberry Vaccinium vitis-
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idaea L. (Storch 2001). In Scottish semi-natural pinewoods, heather often dominates
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(Steven & Carlisle 1959; Summers et al. 1999), particularly in the high light
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environment typical of these areas (Parlane et al. 2006), which is often linked to past
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human impacts (Summers et al. 1999).
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Post-fire succession on heather moorland sometimes includes a prolonged phase of
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increased Vaccinium abundance (Ritchie 1955; Hobbs & Gimingham 1984). We
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wished to test whether this might also occur in pinewoods. If so, it could improve
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capercaillie habitat quality, and support the introduction of prescribed fire into
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pinewood conservation management. This would support the contention that
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ecological boreal forest management should include the emulation of some forms of
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natural disturbance, such as fire (Angelstam 1998; Perera, Buse & Weber 2004).
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Forest management with fire is novel in the UK (Bruce & Servant 2003). The
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Scottish semi-natural pinewood resource is severely depleted (Anon 1994), and there
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were concerns that prescribed fire could be unsafe or impractical in this habitat.
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Therefore, we also tested mowing, which can produce similar effects to fire (Cotton &
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Hale 1994; Schimmel & Granström 1996). Our aims were to determine whether
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burning and/or mowing, within a pinewood with Calluna-Vaccinium ground
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vegetation, would lead to (i) increased cover of bilberry; (ii) increased biomass of
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arthropods important to capercaillie chicks; and (iii) increased usage by capercaillie.
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Methods
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STUDY AREA AND EXPERIMENTAL DESIGN
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The study took place at Abernethy Forest nature reserve (3°37’W, 57°14’N), within
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the Cairngorms National Park in the Scottish Highlands (Fig. 1a). The reserve
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includes c 4000 ha of Scots pine Pinus sylvestris L. forest, on predominantly peaty
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soils (Steven & Carlisle 1959; Summers et al. 1997). The only large herbivores are
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roe deer Capreolus capreolus L. and red deer Cervus elaphus L., each at densities
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around 8 km-2 (unpubl. data). At the nearest weather station (12 km to west, 228 m
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altitude) during 1994-2003, mean annual rainfall was 1060 mm. January and July
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mean temperatures were 2.4 and 13.9°C respectively.
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Twenty-five experimental blocks (Fig. 1b) were selected at random within ‘old, open
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forest’ (‘Box 2-3’: Picozzi, Catt & Moss 1992), at altitudes of 250-400 m. These
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comprised mainly P. sylvestris-Hylocomium splendens-Vaccinium woodland (W18b:
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Rodwell 1991). Block-scale densities of mature trees (modal height 17 m) averaged
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174 ha-1 (range 62-378). Each block consisted of three, 20 m x 35 m plots, separated
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by 10 m (Fig. 1c). Within each plot, eight 5 m x 5 m quadrats were used for recording
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(Fig. 1d). One plot per block was assigned at random to control, burning, and
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mowing. Baseline measurements took place in 2002, treatments were applied in
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spring 2003, and further monitoring took place in 2003-5.
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EXPERIMENTAL TREATMENTS
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Plots were burnt in strips, with water used to protect plot boundaries and features such
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as pine saplings (Dugan 2004). Mowing was by handheld, metal-bladed strimmer.
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Because the availability of suitable prescribed burning weather was uncertain,
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mowing only took place at a block after the ‘burn’ plot had been burnt.
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Treatment characteristics were expected to vary in ways that might influence
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subsequent succession (Schimmel & Granström 1996). Some of this variation might
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be amenable to management control. Therefore, various treatment characteristics
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were measured. For fires, we estimated ‘fireline intensity’ (Byram 1959) from three
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flame-length estimates per quadrat. To aid estimation, ‘fire canes’ (graduated
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2.5 m x 2 cm steel tubes) were fixed vertically at quadrat centres before fires. ‘Depth
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of burn’ (Schimmel & Granström 1996) was measured by marking the moss/litter
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surface at four points per quadrat using small metal posts, and measuring any
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reduction in height after the fire. Soil heating can strongly affect vegetation
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succession (Schimmel & Granström 1996), with 10 minutes at c 55°C being lethal for
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bilberry rhizomes (Granström & Schimmel 1993). Therefore, two simple measures of
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heat duration were taken: firstly, time above 55°C was measured using timers linked
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to thermocouples fixed on the moss/litter surface, 20 cm from each fire cane; and
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secondly, by direct observation of the duration of flames touching fire canes. For
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mowing, in the summer after treatment, we measured the height above the moss/litter
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surface at which stems were cut, and the cover of severed material.
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VEGETATION SURVEYS
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Ground vegetation was surveyed in August-September 2002-5, by a single observer
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(MH), using a 2 m x 25 mm x 5 mm graduated stick, used in other fire studies (Davies
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et al. in press). At four sample points per quadrat, the stick was pushed vertically into
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the moss/litter, down to the soil/humus surface. The maximum height within 5 cm of
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the stick was measured for shrubs and moss/litter. Vegetation structure was
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characterised by standing with the vertical stick at arm’s length, and estimating the
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percentage of the stick visible in a series of 10 cm bands. This gave an index of
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vegetation openness at various levels above the soil/humus surface. The value of this
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index for a 10 cm band centred at the surface of the moss/litter, termed ‘ground-level
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openness’, was estimated by interpolation. The total cover (including cover below
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other species) was estimated for shrub species inside a 1 m radius circle centred at
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each point. Young pines (under 1.5 m) were counted within the same circle. Cover
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was scored separately for heather that was live (green), recently-dead (brown), long-
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dead (grey), or shoot or seedling regeneration. Bilberry defoliation, which affects
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cover estimates, was estimated as the proportion of the 10 bilberry shoots nearest the
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vertical stick that were completely without leaves.
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Before treatments were applied, we measured the positions and heights of all trees
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over 1.5 m, within plots and adjacent 5 m buffer zones. For pines, we recorded the
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proportion of foliage within three height bands: 0-2 m, 2-5 m and over 5 m. The
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proportion of foliage that was brown was recorded within the same height bands,
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before and after fires. Sub-canopy light level was calculated using tree densities and
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heights, and the regression equation of Parlane et al. (2006).
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ARTHROPOD SURVEYS
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Arthopods were surveyed in June, when insectivorous capercaillie chicks are present
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(Summers et al. 2004). Our main sampling technique was pitfall trapping, which
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despite its potential biases, remains a practical and commonly-used technique
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(Southwood & Henderson 2000; Saint-Germain et al. 2007), including for studies of
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capercaillie diet (Summers et al. 2004). To supplement pitfall data, we also carried
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out some direct counts of arthropods. Direct counts of caterpillars were found by
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Atlegrim & Sjöberg (1995), to be positively correlated with foraging success of tame
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capercaillie chicks.
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Pitfall traps were 150 ml polyethylene containers, with a 46 mm opening diameter,
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two-thirds filled with trapping solution (ethylene glycol diluted 1:3, with one drop of
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detergent). Traps were set at each block, at all quadrat centres, on randomly-selected
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dates during the first half of June, and collected 14 days later. Four arthropod groups
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of importance in capercaillie chick diet (Kastdalen & Wegge 1985; Spidsø & Stuen
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1988; Picozzi, Moss & Kortland 1999; Summers et al. 2004) were extracted and
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counted: spiders (Araneae), beetles (Coleoptera), caterpillars (Lepidoptera), and ants
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(Hymenoptera: Formicidae). Caterpillars were measured to the nearest mm. Adult
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ants, beetles, and spiders were identified to species. For traps that caught many
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Formica ants, a random sample of 20 was identified. Other Formica were counted
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and assumed to comprise similar species proportions.
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Direct arthropod counts were carried out at every quadrat during June in the last two
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years of the study. The observer crouched near the quadrat centre and, for one
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minute, recorded any arthropods observed within 50 cm of the centre marker.
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Arthropods were counted by group, and sized by eye to the nearest mm.
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Different arthropod groups may have different trapping bias (Southwood &
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Henderson 2000), attractiveness to capercaillie (Kastdalen & Wegge 1985), or effects
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on chick survival (Picozzi, Moss & Kortland 1999). Therefore, biomass was
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estimated separately by group, using the approach of Saint-Germain et al. (2007).
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Counts by species, or size class for caterpillars, were determined for each plot.
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Median adult body lengths were collated from published keys. The dry mass for an
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individual of each species (or caterpillar size class) was estimated using published
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log(length)-log(mass) regression coefficients (Rogers, Buschbom & Watson 1977;
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Gowing & Recher 1984; Sample et al. 1993; Hódar 1996; Ganihar 1997), with back-
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transformation correction (Sprugel 1983). The individual body mass of each species
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was estimated as the mean of values given by all available regressions. These were
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multiplied by count to give the biomass of each species in each plot. Species
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biomasses were then summed to give the biomass for the whole group. For sexually-
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dimorphic spider species, calculations were done separately by sex.
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MEASURING CAPERCAILLIE USAGE
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Dung counts were carried out in May and October each year, to provide a measure of
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capercaillie usage. Dung found in May was cleared, so that October counts primarily
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represented summer accumulation. This is the period when ground vegetation is most
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used by capercaillie (Storch 2001; Summers et al. 2004). Dung counts involved
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searching a 2 m radius area centred at the quadrat centre for five minutes. The
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number and diameter of pellets in each dung group were recorded. We could not
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assume that dung groups would be perfectly detected, and therefore quantified
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detection rate as a function of vegetation openness using a trial with dummy dung.
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Detection rate was then estimated for each plot in each year from vegetation openness
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data (Supplementary Material). Black grouse Tetrao tetrix L. were also present and
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their dung overlaps in size with that of capercaillie (Brown et al. 1987). Therefore,
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we collated incidental grouse sightings, to see how commonly they occurred.
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DATA ANALYSIS
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Statistical analyses were used to investigate treatment effects on changes in bilberry
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cover, arthropod biomass, and capercaillie usage, between the base-line year (2002:
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pre-treatment) and post-treatment years. In all cases, we used linear mixed models in
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SAS (SAS Inst., 2000), assuming that errors were normally distributed (after
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transformation of the response), except for capercaillie usage (dung count) data where
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a generalized linear mixed model with Poisson errors was used. The significance of
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explanatory variables was estimated using F-tests with the denominator degrees of
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freedom estimated by the Satterthwaite approximation. Model assumptions were
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assessed visually by examining probability plots and plots of residuals against
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predicted values. The normality assumption was checked for random effects and
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model residuals. In some cases the response variables were transformed in order to
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achieve normality (as indicated below).
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Differences between treatments in the change in bilberry cover from the baseline year
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(2002) to three years after the application of treatments (2005), were estimated using
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the following linear mixed model:
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Yi,j = a0 + a1*Xi,j + a2*X2i,j + a3*Si,j + a4*Vi,j + a5*Li,j + Bi + Tj
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(1)
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with Yi,j and Xi,j the total bilberry cover (arc-sine fourth-root transformed to achieve
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normality of model residuals) in 2005 and 2002 respectively, as measured in the plot
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with treatment j (j=1(control), 2(mown), or 3(burnt)) in block i (i=1,2,…25). Si,j and
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Vi,j are the difference in mean defoliation score and visit date between these years
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(2005 minus 2002) and Li,j is the light index. These were included because these have
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been found to affect bilberry cover by Parlane et al. (2006) (Vi,j and Li,j) or were
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assumed to do so (Si,j). Bi is a random effect representing potential block effects (a
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spatial factor). The main parameters of interest are the treatment effects Tj. We
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included baseline bilberry cover squared as a covariate, as we expected, and observed,
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greater change in bilberry cover where initial cover was further from its minimum and
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maximum possible values of 0% and 100%.
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The experiment was affected by an unusual natural heather die-back event (Hancock
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in press), roughly synchronous with the application of experimental treatments in
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spring 2003. This was probably caused by exceptional weather conditions, combined
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with the maturity of heather at the site. We expected heather die-back to lead to
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increases in bilberry cover, due to evidence of competitive effects (Parlane et al.
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2006). Therefore, as well as the observed experimental results, we wished to estimate
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treatment effects for a hypothetical situation where this die-back event had not
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occurred, termed the ‘no die-back scenario’. Die-back was calculated as the
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proportion, in control plots, of heather cover in summer 2003, that was brown
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(recently dead). To model bilberry response under the hypothetical ‘no die-back
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scenario’, the heather die-back scores were added as covariates to eqn 1:
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Yi,j = a0 + a1*Xi,j + a2*X2i,j + a3*Si,j + a4*Vi,j + a5*Li,j + Bi + Tj + a6*Di + bj*Di
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(2)
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where the coefficients a6 and bj are the parameters for the slopes for Di, the arc-sine
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square-root transformed heather die-back score at the control area of block i in 2003.
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Other terms are as in eqn 1.
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Differences between treatments in change in arthropod biomass in pitfall traps, from
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the baseline year (2002) to each of the three post-treatment years, were estimated
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using the following linear mixed model:
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Zi,j,t = a0 + a1*Mi,j + Yt + Bi + Tj + TYj,t
(3)
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where Zi,j,t and Mi,j are respectively the post-treatment and pre-treatment biomass
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estimates for the invertebrate groups of interest in plots with treatment j in block i,
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and post-treatment year t (t=2003, 2004 or 2005). Yt is a categorical variable for the
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post-treatment year-effect, TYj,t is a nine-level categorical variable representing the
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interaction between treatment j and year t, and other variables are as eqn 1. Visual
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inspection of residual plots from these models suggested that errors could be assumed
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to be normally distributed after a loge transformation for the spider and ant biomass
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data, and a fourth-root transformation for beetle and caterpillar data. Therefore we
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used these transformations for the variables Zi,j,t and Mi,j.
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We also expected natural heather die-back (see above) to affect arthropod responses,
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due to consequent changes in vegetation structure, impacts on populations of
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herbivorous arthropods feeding on heather, and increases in dead plant material
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available to detritivores. Therefore, analogous to the bilberry cover analysis (eqn 2),
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we adapted eqn 3 to estimate differences between treatments in change in arthropod
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biomass under the ‘no die-back scenario’, by adding the heather die-back scores as
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covariates:
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Zi,j,t = a0 + a1*Mi,j + Yt + Bi + Tj + TYj,t + a2*Di + dj*Di + et*Di + fj,t*Di
(4)
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The coefficients dj, et and fj,t are the treatment-specific, year-specific, and the
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treatment-by-year-specific slopes for the heather die-back scores Di, respectively.
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Direct arthropod counts were analysed in the same way except that no baseline data
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were available.
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To investigate differences between treatments in capercaillie usage, we estimated
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differences in autumn grouse dung counts between plots with different treatments, in
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the three post-treatment years. Baseline (pre-treatment) data were not included as
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these showed an unexpected pattern of high counts in control plots (Results) which
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could have led to estimates of treatment effects that were unduly positive. As there
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were many zero counts, only blocks in years with at least one grouse dung group
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recorded were included in the analysis. We estimated relative differences in dung
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frequencies between treatments by fitting the following model:
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Loge(DGi,j,t) = Bi,t + Pi,j + Loge(Ri,j,t) + Tj
(5)
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where Bi,t are intercepts (fixed effects) for block i in year t (to account for clustering
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of counts of plots within the same block), and Pi,j random effects for plots with
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treatment j in block i (to account for clustering of counts within plots across years).
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Ri,j,t (included as an offset in the model) are detection rates of dung groups of median
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size in plot i in block j in year t, as estimated from a model which related dung-
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detection rates to visibility scores, parameterised using data from the dummy dung
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trial (Supplementary Material).
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Results
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TREATMENT CHARACTERISTICS AND IMPACTS ON TREES
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The method of treatment was similar at all blocks, but because of variations in
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vegetation, terrain, and weather, certain treatment characteristics also varied
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(Table 1). Mean mowing date was three weeks later than that for burning. Fires were
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generally low intensity, with mean flame heights of 0.6 m. On average, only 1 cm of
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the initial mean moss/litter depth of 15 cm was consumed by fires.
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There were some losses of young (under 1.5 m) pines due to treatments. Before and
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after treatment, the total number of young pines fell from 45 to 39 (179 to 155 ha-1) in
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burnt plots and from 27 to 22 (107 to 88 ha-1) in mown plots. However, newly-
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established pine seedlings were later found in both burnt and mown plots. Counts of
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young pines, three years after treatment, were 192 (764 ha-1) and 80 (318 ha-1) in
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burnt and mown plots respectively.
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There were localised impacts of fires on mature pines. Recently dead (brown) needles
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were rare before burning, averaging 0%, 0.4% and 0% of foliage in height classes
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0-2 m, 2-5 m and over 5 m. However, after burning, plot-scale values in these height
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classes rose to 43%, 17% and 1.2% (means) and 79%, 58% and 10% (maxima), with a
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weighted overall mean of 8.2%. Six of the 289 mature (over 1.5 m) pines on burnt
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plots were killed, the tallest being 3.5 m.
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EFFECTS ON GROUND VEGETATION
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Median bilberry cover was around 10% before treatment, and remained similar in the
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first summer after treatment (Fig. 2a). By three years after treatment, there were
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increases in cover, not only in burnt and mown areas (median 23-24%), but also in
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controls (median 19%). Median live heather cover was initially around 75%, then
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declined to less than 6% in mown and burnt areas after treatment (Fig. 2b). There was
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a smaller decline in controls, to around 47%, linked to unusual natural heather die-
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back (Hancock in press). Most surviving heather in treated areas was mature shoots
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that had avoided treatment, having been part-buried in moss, or too damp to burn
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during fires. Two years after treatment, when different types of heather regeneration
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were most clearly distinguishable, regeneration by vegetative sprouting and seedlings
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averaged only 0.03-0.04% and 0.1-0.3% cover respectively, while surviving mature
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heather averaged 5-8%. Heather frequency within 1 m radius sample points was 98%
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initially, then fell in burnt and mown plots to 60% after treatment, and recovered to
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88% by three years after treatment. The height of ground vegetation fell from a mean
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of 53 cm (s.e. 0.86) initially, to means of 38 and 28 cm (s.e. 1.9, 1.0) in burnt and
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mown plots respectively, the year after treatment.
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The effect of treatment on bilberry cover, three years after treatment, was not
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statistically significant (eqn 1, Table 2a, Fig. 3a). However, significant treatment
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differences were estimated under the ‘no heather die-back scenario’ (eqn 2, Table 2b,
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Fig. 3b). The influence of heather die-back on estimates of treatment effects is caused
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by the interaction between treatment and die-back (term bj in eqn 2; Table 2b). In
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particular, the positive values of the control vs. mown and control vs. burnt contrasts
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indicate how bilberry cover was affected by heather die-back relatively more
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positively in control plots, than in mown or burnt areas. Estimating treatment effects
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at a heather die-back value of 0%, gave the following ‘no die-back scenario’ results:
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from an initial mean bilberry cover of 10%, modelled cover in mown and burnt plots
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increased after three growing seasons to 23% (95% confidence intervals 13-35%),
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when that of control areas, had there been no heather die-back, was estimated at 13%
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(7-22%) (Fig. 3b). Although variance was high, the lower confidence limit of bilberry
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cover in treated areas exceeded the control mean, implying that a positive treatment
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effect was likely. This was also the case when treatment effects were estimated for
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hypothetical moderate heather die-back of 5%.
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Plotting the residuals of the ‘no die-back’ bilberry model, by treatment, against the
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treatment characteristic variables listed in Table 1, suggested there was little evidence
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that burning or mowing management with a particular subset of characteristics was
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linked to a more positive bilberry response.
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ARTHROPOD RESPONSES
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Arthropod biomass estimates, from pitfalls and direct counts, were significantly
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positively correlated for spiders, beetles and ants (rs=0.25, 0.16 and 0.76; P(one-
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tailed)=0.001, 0.026, and <0.0001 respectively; N=150), but not for caterpillars
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(rs=0.049; P(one-tailed)=0.28).
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Spider biomass in pitfalls (Fig. 4a) was higher in the burnt and/or mown plots, than
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controls, two and three years after treatment (Table 3a, Fig. 5a). Mown and burnt
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areas, after three years, had 2.3 (confidence intervals 1.8-2.6) and 1.8 (1.4-2.1) times,
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respectively, the spider biomass of controls. Conversely, pitfall beetle biomass in
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mown and burnt plots (Fig. 4b) was lower than controls, two years after treatment
11
(Table 3a, Fig 5b) by 49% (confidence intervals 2-73%) and 54% (12-76%),
12
respectively. Caterpillar biomass (Fig. 4c) did not differ significantly between
13
treatments in any post-treatment year. After the first post-treatment year, ant biomass
14
(Fig. 4d) in burnt plots was higher than that of control and/or mown plots (Table 3a,
15
Fig. 5c), exceeding that of control plots 3.2-fold (confidence intervals 1.9-5.2) and
16
2.0-fold (1.2-3.3) in the second and third post-treatment years respectively.
17
18
For the ‘no heather die-back’ scenario, most significant between-treatment differences
19
in arthropod biomass disappeared: only spider biomass in the second post-treatment
20
year remained significantly different between treatments (Table 3b, Fig. 5d), with 4.6
21
times (confidence intervals 1.8-12) greater biomass in mown plots than controls. This
22
implies that, to some extent, the treatment differences found in analyses of the real
23
results, reflected an avoidance of (spiders, ants) or preference for (beetles) control
24
areas affected by heather die-back.
25
17
1
Direct count data suggested that ant biomass in the second post-treatment year
2
differed between treatments (treatment x year interaction, treatment contrasts within
3
year two: F2,120=6.67, P=0.0018). In this year, ant biomass seen in mown and burnt
4
plots, per count, was 3.1 and 4.7 times, respectively, that seen in control plots (95%
5
confidence intervals: 1.2-6.7; 2.0-9.4). However, in the ‘no-dieback’ model, this
6
difference disappeared (F2,115=0.87, P=0.42). No other significant within-year
7
treatment differences were found in the direct count data.
8
9
CAPERCAILLIE USAGE
10
11
The detection rate of dummy dung groups was around 0.3 in control and cut
12
vegetation, and around 0.5 in burnt vegetation, three years after treatment (Fig. 6a).
13
The preferred model (Supplementary Material) of detection probability included
14
dummy-dung group size and pellet diameter, and their interaction, and vegetation
15
openness. This model gave estimates of the detection probability of a median dung
16
group (a single 9 mm diameter pellet), as around 0.11 in untreated vegetation, and
17
0.16 in burnt or mown vegetation (Fig. 6b). Real dung data showed a strong shift
18
from greater counts in controls, in the pre-treatment year, to the opposite pattern in
19
post-treatment years (Fig. 6c). Modelling the post-treatment data, with detection rate
20
included as a covariate, showed that counts were much higher in burnt and mown
21
plots in the post-treatment period, than controls (Fig. 6d), by factors of 4.3 and 6.3 in
22
burnt and mown plots respectively (95% confidence intervals: 2.5-7.3 and 4.0-9.8).
23
Residuals were higher in controls where heather die-back was greater; thus treatment
24
effects might have been even more pronounced in the absence of die-back.
25
18
1
In total, there were 28 recorded sightings of grouse within 50 m of experimental plots.
2
All but one of these were capercaillie. Ninety-two percent of grouse droppings found
3
were within the range of diameters of full-grown capercaillie (Gjerde 1990). Together
4
with anecdotal knowledge of the usual areas inhabited by grouse species at our study
5
site, this suggested that most recorded grouse dung was that of capercaillie.
6
7
Discussion
8
9
This study provides the first experimental evidence of the potential effectiveness of
10
burning and mowing heather-rich ground vegetation in woodland, as tools for
11
improving capercaillie habitat. Interpretation was complicated by a natural heather
12
die-back event, but when modelling a hypothetical scenario without die-back, we
13
estimated that burnt and mown areas would have bilberry cover scores that were
14
approximately double those of control means after three growing seasons. Although
15
wide confidence intervals highlight the variability in the response, an increase of some
16
degree is likely. The importance of bilberry to capercaillie (Storch 2001), particularly
17
for broods (Storch 1994; Summers et al. 2004), means that increases are likely to
18
benefit the species.
19
20
Our study supports the idea that conservation managers should consider introducing
21
some forms of disturbance, for biodiversity objectives (Angelstam 1998). However,
22
we found no evidence that the more natural of the two disturbance methods tested,
23
fire, had greater immediate advantages in terms of our objectives. It was not the
24
naturalness of a disturbance, but rather its impact, particularly in terms of ‘dominance
25
reduction’ (Wohlgemuth et al. 2002), that was more important.
19
1
2
Schimmel & Granström (1996) found that intense fires could eliminate Vaccinium
3
species. However, our fires never approached the peak intensities they tested, of 75
4
minutes heating, compared to a quadrat-scale maximum of 10 minutes in this study.
5
Similarly, our fires had lower fireline intensities than those of Bruce & Servant
6
(2003), whose peak values were over ten times ours, with correspondingly two to
7
three times more severe canopy browning of mature trees. Other studies have shown
8
that heather can recover rapidly after burning (Hobbs & Giminham 1984) or mowing
9
(Cotton & Hale 1994). However, we found little vegetative heather regeneration,
10
probably due to the advanced age of plants at the site. Despite high post-treatment
11
heather frequency, mainly due to surviving shoots of mature heather, we found that
12
heather cover, especially of young seedlings, remained low in the first few years after
13
treatment. These factors may have contributed to the positive bilberry response.
14
15
Bilberry-rich areas are usually richer in arthropods important to capercaillie, than
16
other vegetation (Kastdalen & Wegge 1985; Summers et al. 2004). However,
17
modelling a scenario with no natural heather die-back, we found few biomass
18
increases in key arthropod groups following treatment. Of four groups, counted in
19
two ways, on two treatments, there was only one increase.
20
21
Increases in capercaillie usage, as measured by dung deposition, were striking.
22
Summer dung accumulation was 4.3-6.3 times higher in treated areas than controls,
23
and might have been higher still in the absence of heather die-back. Storch (1993)
24
found that adult capercaillie were strongly associated with bilberry in summer, and
20
1
preferred vegetation heights of 30-40 cm, lower than that of our controls: either or
2
both factors may explain capercaillie usage of treated areas.
3
4
The results of this study are applicable to similar semi-natural and production forests.
5
However, it coincided with an unusual heather die-back event (Hancock in press).
6
While we have statistically modelled a ‘no die-back scenario’, this may not fully
7
reflect what would have happened had heather die-back not occurred, reducing the
8
certainty with which our findings can be applied to more typical years. Unexpectedly,
9
however, heather die-back highlighted the potential for major short-term vegetation
10
changes without management intervention, and illustrated the possibility that natural
11
disturbance events may periodically, but unpredictably, deliver vegetation changes of
12
similar magnitude to those obtained by management.
13
14
No other studies have evaluated these management techniques in similar habitats for
15
similar aims. Bruce & Servant (2003) showed the feasibility of using prescribed fire
16
within a similar Scottish pinewood, but replication was limited, with no detailed
17
measures of capercaillie habitat quality.
18
19
Our results show that land-managers can expect mowing or burning of the ground
20
vegetation, in open forests with heather-Vaccinium ground vegetation, to deliver
21
benefits for capercaillie conservation, via increased bilberry cover after three years.
22
These results help support the EU-funded mowing management already being carried
23
out in Scotland for capercaillie. However, the following caveats remain. Firstly, the
24
striking increase in capercaillie usage following burning and mowing does not imply a
25
population effect. Such an effect is more likely following improvements in brood
21
1
habitat and breeding success. While increased bilberry is likely to benefit broods,
2
general increases in key arthropod groups have not yet been demonstrated. Secondly,
3
results reported here apply to the first three growing seasons after treatment. Longer-
4
term vegetation development could lead to long-lasting areas valuable to capercaillie
5
broods, such as bilberry stands, or, potentially, quite different communities, such as
6
thickets of Scots pine seedlings, or rejuvenated heather stands. Any benefits could be
7
short-lived if heather rapidly recovers in treated areas. Finally, this study has
8
unexpectedly highlighted the potential for rapid vegetation changes, towards a
9
composition more favourable to capercaillie, to occur without management
10
intervention. Long-term monitoring of the experimental areas will be needed to
11
assess the importance of these caveats.
12
13
Acknowledgements
14
15
We are grateful to J. Wilson, C. Legg, N. Cowie, J. Roberts and K. Duncan for ideas
16
and support; D. Dugan, C. McClean, B. Moncrieff, A. McAskill and R. Watson for
17
performing the experimental treatments; J. Willi, S. Rao, G. Nisbet, H. Swift, A.
18
Macfie and I. Hutson for data collection; P. Hammond and P. Kirby for assistance
19
with arthropod species determinations; G. Lyons, G. Nisbet, and P. and A. Sinclair for
20
sorting pitfall catches; M. Telfer and I. Dawson for specialist arthropod advice; and K.
21
Kortland, S. Taylor, and J. Dunsmore for organising funding, which was provided by
22
RSPB, Scottish Natural Heritage and the EU LIFE fund.
23
22
1
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27
1
2
28
1
2
3
29
1
2
3
30
1
2
a
b
3
4
5
6
7
8
9
10
cc
11
Figure 1. Experimental location and design. (a) Part of Scotland, showing
12
Cairngorms National Park (black line) and Abernethy Forest reserve (black shading).
13
(b) Central part of Abernethy Forest reserve, showing reserve boundary (black line),
14
the forested area (grey), and study blocks (black circles). (c) Air photograph of one
15
experimental block in open forest, after treatment, showing the three plots. (d)
16
Schematic diagram of one plot, showing the quadrats used for recording (grey).
17
18
31
1
Figure 2. Vegetation changes during the experiment: (a) bilberry cover; (b) heather
2
cover. Boxplots show median (central line), quartiles (box), and 5th and 95th centiles
3
(whiskers). Black dots indicate means. White bars: control plots; grey bars: burnt
4
plots; striped bars: mown plots. An arrow indicates the timing of the experimental
5
treatments.
6
32
1
2
3
Figure 3. Fitted bilberry cover, by treatment, three years after treatment (back-
4
transformed means and standard errors). (a) As recorded in the experiment (eqn 1),
5
including the effects of unusual, natural heather die-back in the control areas. (b) As
6
modelled under a ‘no die-back scenario’ (eqn. 2), estimating treatment means for a
7
hypothetical situation without natural heather die-back. Key as Fig. 2.
33
1
2
3
4
Figure 4. Estimated biomass caught in pitfall traps, of arthropods important in
5
capercaillie chick diet, by treatment, in June each year. (a) Spiders; (b) beetles; (c)
6
lepidopteran caterpillars; (d) ants. Note log scale for ants. Key as Fig. 2.
7
34
1
2
3
Figure 5. Significant treatment effects on post-treatment pitfall trapped biomass of
4
arthropods important to capercaillie chicks (back-transformed fitted means and
5
standard errors). (a)-(c): Results as recorded in the experiment (eqn 3); (d) results
6
from the ‘no heather die-back’ scenario (eqn 4). (a) and (d) spiders; (b) beetles; (c)
7
ants. Key as Fig. 2.
8
35
1
2
3
Figure 6. Grouse (mainly capercaillie) dung counts and dung detection estimates. (a)
4
Detection rate of dummy dung groups, three years after treatment. (b) Estimated
5
detection probability of a median grouse dung group (see text). (c) Grouse dung
6
counts per plot. (d) Detection-compensated modelled grouse dung counts, post-
7
treatment (eqn 5). (b) and (d) are for non-zero block-years only. Means and standard
8
errors. Key as Fig. 2.
9
10
36
1
Supplementary material: method for estimating dung-
2
detection rates
3
4
Here, we describe how dung detection rates Ri,j,t in plots with treatment j in block i in
5
year t (as used in eqn 5 in the main paper) were estimated. This was necessary
6
because we could not assume that dung groups were detected perfectly. Dung
7
detection rates could be expected to be a function of vegetation openness, which in
8
turn is influenced by the treatments. Thus, it was necessary to correct for differences
9
in detection probabilities between treatments in order to avoid biases in estimates of
10
treatment effects on dung densities.
11
12
We obtained independent estimates of detection rates, by carrying out a detection
13
trial. Dummy dung pellets, made of brown-stained pine dowel, were placed in
14
quadrats, and their detection rate measured. The number and diameter of pellets in
15
each dummy group were those of a randomly-selected real dung group, found on
16
previous surveys. Shortly before the May 2005 dung survey, 300 dummy groups
17
were placed at random locations within the search areas of randomly-selected
18
quadrats. The vegetation openness (see above) of each dummy group location was
19
measured. The details of any dummy dung groups located during subsequent standard
20
searches were recorded.
21
22
Let Yi be the response variable, indicating whether dung group i was detected (Yi=1)
23
or not detected (Yi=0). We modelled the probability that a dummy dung group i was
24
detected (P(Yi=1)), as a function of the covariates that we expected to influence
25
detection probabilities, namely: the size of dung group i (number of pellets in the
37
1
group, loge-transformed), the pellet-size of dung group i (diameter of a typical pellet
2
in the group, loge-transformed), and vegetation openness (see methods section of the
3
main paper), as measured at the location that dung group i was placed.
4
5
The relationship between detection probabilities and these covariates was estimated
6
using generalized linear models with a binomial distribution for the Yi, and a logistic
7
link function for the covariates. Nineteen models were fitted, including a null model,
8
and all possible combinations of the three covariates and their first and second order
9
interactions. The model with the lowest AICc (the ‘best approximating model’:
10
Burnham & Anderson 2002) was used to estimate detection probability, in all plots
11
and years, of a ‘median dung group’. This was defined as a dung group with the
12
median number and diameter of pellets of recorded real dung groups. The best model
13
was as follows:
14
15
Logit(P(Yi=1)) = a0 + a1*Ni + a2*Pi + a3*Vi + a4*NPi,
16
17
with Pi being the (loge-transformed) pellet-diameter of dung group i, Ni the (loge-
18
transformed) number of pellets in group i, Vi the vegetation openness score in the
19
neighbourhood of group i, and NPi the product of pellet number and diameter.
20
21
We used this model to estimate detection rates in each of the plots and years, using the
22
median observed dung pellet-diameter P_med (9 mm), the median group size N_med
23
(one pellet), and the measured vegetation openness at each of the plots in each year
24
Vi,j,t, as follows:
25
38
1
Ωi,j,t= a0 + a1*N_med + a2*P_med + a3*Vi,j,t + a4*N_med*P_med.
2
3
Ri,j,t =exp(Ωi,j,t) / (1 + exp(Ωi,j,t))
4
5
REFERENCES
6
7
8
Burnham, K. P. & Anderson, D. R. (2002) Model Selection and Multimodel
Inference. Springer, New York.
9
39
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