National-Scale Anaylses of Habitat Associations of Marsh Tits

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National-scale analyses of habitat associations of Marsh Tits Poecile palustris and Blue Tits Cyanistes caeruleus: two species with opposing population trends in Britain Jane Carpenter a; Jennifer Smart b; Arjun Amar b; Andrew Gosler a; Shelley Hinsley c; Elisabeth Charman b a Edward Grey Institute for Field Ornithology, Department of Zoology, University of Oxford, Oxford b Royal Society for the Protection of Birds, Sandy, Bedfordshire c Centre for Ecology and Hydrology, Huntingdon, Cambridgeshire

First published on: 11 November 2009

To cite this Article Carpenter, Jane, Smart, Jennifer, Amar, Arjun, Gosler, Andrew, Hinsley, Shelley and Charman, Elisabeth(2010) 'National-scale analyses of habitat associations of Marsh Tits Poecile palustris and Blue Tits Cyanistes caeruleus: two species with opposing population trends in Britain', Bird Study, 57: 1, 31 — 43, First published on: 11 November 2009 (iFirst) To link to this Article: DOI: 10.1080/00063650903026108 URL: http://dx.doi.org/10.1080/00063650903026108

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National-scale analyses of habitat associations of Marsh Tits Poecile palustris and Blue Tits Cyanistes caeruleus : two species with opposing population trends in Britain

JANE CARPENTER 1* , JENNIFER SMART2 , ARJUN AMAR2 , ANDREW GOSLER1 , SHELLEY HINSLEY3 and ELISABETH CHARMAN2 1 Edward Grey Institute for Field Ornithology, Department of Zoology , University of Oxford , South Parks Road , Oxford , OX1 3PS , 2 Royal Society for the Protection of Birds , The Lodge , Sandy , Bedfordshire , SG19 2DL and 3 Centre for Ecology and Hydrology , Monks Wood, Abbots Ripton , Huntingdon , Cambridgeshire , PE28 2LS

Capsule Marsh Tits were strongly associated with both the amount and species diversity of woodland understorey; Blue Tits were associated with large trees and deadwood. Aims To gather quantitative information on the habitat requirements of Marsh Tits, in comparison with those of Blue Tits, across a large number of sites in England and Wales, and secondly to evaluate the range of habitat conditions likely to encourage the presence, and increase the abundance of, each species. Methods Counts of birds were made at each of 181 woods across England and Wales, and habitat data were collected from the same locations in each woodland. Marsh Tit and Blue Tit presence and abundance were related to habitat characteristics, interspecific competition and deer impact. Results Shrub cover and species diversity were important for the presence and abundance of Marsh Tits, across their geographical range in Britain. Blue Tits were associated with large trees and deadwood. Conclusion Our results support the hypothesis that changes in woodland management, leading to canopy closure and a decline in the understorey available, could have had an impact on Marsh Tits, and may have led to the observed population decline. These same changes were also consistent with population increase in Blue Tits. Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 Since the 1920s, woodland habitat across Europe has techniques such as coppicing (Amar et al. 2006, Hopkins been changing rapidly (Tucker & Evans 1997). & Kirby 2007), although changing timber markets could Afforestation of open land, along with intensively man- also be relevant (Fuller et al. 2007). aged plantations of conifers or non-native species, have There has been increasing concern about the health of left only fragmented patches of semi-natural forest woodland bird populations in Britain (Fuller et al. 2005, (Tucker & Evans 1997). In Britain, the pattern is similar. Amar et al. 2006, Hewson et al. 2007). National bird Although the forested area increased from 5% at the turn population monitoring schemes have reported declines of the century (Richards 2003, Hopkins & Kirby 2007) in the populations of many woodland bird species over to 12% in 2007, over 60% of this increase was due to the last 40 years (Eaton et al. 2006, Gregory et al. 2002). conifer plantation (Mason 2007). Furthermore, the age The most recent revision of the list of Birds of structure of trees in British woodlands is biased towards Conservation Concern in the UK placed seven species maturity with at least 70% of both conifer and broad- on the Red List, and nine on the Amber List (Gregory leaved forest stands now entering a closed-canopy stage et al. 2002). However, compared with the wealth of (Mason 2007). This change in broadleaved forest is research into reasons for the decline of farmland birds in probably due to the cessation of woodland management Britain (Brickle et al. 2000, Chamberlain et al. 2000, Vickery et al. 2004), there has been limited research into *Correspondence author. Email: [email protected] factors affecting woodland bird populations.

The joint Royal Society for the Protection of Birds However, all of these early studies were focused on only (RSPB) and British Trust for Ornithology (BTO) Repeat one or two woodlands in Oxfordshire. Hinsley et al. Woodland Bird Survey (RWBS) was partially designed (2007) provide recent evidence of the continued to address this gap in knowledge, as well as to test importance of the understorey to this species. Carpenter whether population trends in woodland birds, as detected (2008) showed that although niche separation contin- by the Common Bird Census, were valid. It focused first ues between Blue and Marsh Tits to some extent, there on the changes in bird populations in woodland habitats is also considerable overlap in the foraging behaviour across 20 years and second on habitat change within of the two species, both in terms of actual foraging woods and the link between population decline, habitat behaviour, and vertical and horizontal location in the change and current conditions (Amar et al. 2006). Two woodland habitat. Furthermore, some evidence for hundred and fifty two woodland sites from an original competition between the two species was also found. census in the 1980s were revisited in the early 2000s. This raises questions about the possible role of, first, Nine out of 34 bird species showed large (<25%) habitat change, and, secondly, competition, in the spe- population declines, but a further 11 showed a large cies’ decline. British woodland habitat has changed in population increase (Amar et al. 2006, Hewson et al. recent years (Amar et al. 2006, Mason 2007), attributed 2007). There was evidence that changes in woodland to canopy closure (Fuller et al. 2005) and overgrazing by structure resulting from woodland maturation, a reduc- deer (Gill & Fuller 2007). However, Broughton et al. tion in woodland management and, possibly increased (2006) found little difference in understorey cover deer browsing in some areas, could be important factors between areas of a woodland occupied and unoccupied influencing the declines of some species. by Marsh Tits, and instead found evidence of the impor- Basic ecological data for many woodland bird species tance of canopy characteristics. They noted that under- are lacking (Amar et al. 2006), thus, the RWBS dataset storey cover was very homogenous at their site, perhaps was also used to determine the habitat requirements of explaining this result, but the study still highlights the several declining species, in parallel with those of some lack of knowledge of how Marsh Tits’ use of understorey whose populations are increasing, to draw comparisons is influenced by other factors. Secondly, recent evidence and gain insights into possible reasons for these contrast- of a localized effect of competition by Blue Tits ing trends (Smart et al. 2007). Here, we report on the (Carpenter 2008) suggests this also needs further investi- findings for two closely related species; the declining gation. This is particularly pertinent (Perrins 2003) Marsh Tit Poecile palustris and the increasing Blue Tit given the recent increase in the Blue Tit population Cyanistes caeruleus . The Marsh Tit was one of three low- (+33% in the last 25 years, Eaton et al. 2006) in contrast land woodland bird species to be ‘Red Listed’ during the to the Marsh Tits’ decline. last update of the Birds of Conservation Concern, due to Only one published study (Hinsley et al. 2007) has a long-term population decline of over 50% in the last examined habitat requirements of Marsh Tits in Britain 25 years, detected by the national monitoring schemes in more than two woodlands, although only certain Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 (Gregory et al. 2002). This decline was also demon- variables, again concentrated on the understorey, were strated by Perrins (2003) using long-term ringing data included in the large-scale analysis. Thus, analyses of showing that the decline could be as much as 70% since the extensive dataset collected for the RWBS (a total the 1960s. However, recent studies suggest that the of 252 woods, of which we could use 181) provided a population may have stabilized at a historically low level, valuable opportunity to investigate the current habitat and may even have increased slightly in recent years associations of Marsh Tits across the species’ range. Our (Amar et al. 2006, Eaton et al. 2006). study had two main aims: first, to gather quantitative There has been little research on Marsh Tits in information on the habitat requirements of Marsh Tits, Britain since the 1950s (Colquhoun & Morley 1943, in comparison with those of Blue Tits, across a large Hartley 1953, Morley 1953, Gibb 1954), and hence number of sites on a national scale, and, secondly, to there is little current understanding of the causes of evaluate the range of habitat conditions likely to decline. The early studies identified Marsh Tits as being encourage the presence, and increase the abundance, more specialized ecologically than their dominant of each species. This provided a foundation upon which counterparts, Blue Tits and Great Tits Parus major , due habitat management, and formal hypothesis testing of to their apparent reliance on the understorey layer its effectiveness, could be based. Direct analysis of within mature woodlands, year-round territoriality and changes in bird abundance in relation to habitat change habit of storing food (Gibb 1954, Perrins 1979). within the RWBS data set was carried out by Amar

et al. (2006) and indicated that, for the RWBS data, a south-east England group of sites. For Marsh Tits, such an approach was not appropriate for Marsh Tits. Gloucestershire and the Forest of Dean were grouped together, as were Suffolk and Northamptonshire, to increase locality-specific sample sizes or to remove METHODS zero marginals in occupancy analyses. Data collection Bird presence and abundance estimates were obtained using a point count method in 2003 and/or A detailed account of the methods used for field data 2004. Point counts lasted 5 minutes and were carried collection is given in Amar et al. (2006) and Smart out twice, first in April or the first week of May, then et al. (2007). However, a summary is included here. in the last 3 weeks of May or first half of June. There Although the RWBS dataset consisted of sites cen- were at least 10 points per wood. Points were at least sused by volunteers working either for the BTO (ter- 100 m apart, and at least 50 m from the edge of the ritory mapping, total number of sites available = 113) wood. The maximum count across the two visits (and or the RSPB (point counts, total number of sites across the 2 years if the site was surveyed in both available = 252), the different surveys had different years) gave the abundance estimate. methods and could not be merged. Therefore, only Volunteers recorded whether birds were nearer or the RSPB sites were used. Figure 1 shows the distri- further than 25 m from the point at first detection. bution and clustering of sites used. For the current This was done to enable a density to be derived for study, we only used woodlands in England and Wales, each species per site, using the software program to reflect the current distribution of Marsh Tits. This distance (Buckland et al. 2001). However, problems gave a total sample size of 181 woods. For the pur- were identified in analysing our data with distance . pose of these analyses, Kent and Hertfordshire sites This was due to the use of only two distance bands ( n = 6) were joined with Buckinghamshire to create when recording species, which were determined from Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010

Figure 1. The location of all study woodlands in England and Wales showing the localities within which woodlands were clustered. For both species, Kent and Hertfordshire sites were joined with Buckinghamshire to form ‘south-east England’ sites. For Marsh Tits, Gloucestershire and the Forest of Dean were grouped, as were Suffolk and Northamptonshire.

historical methodologies (see Amar et al. 2006). This later). First, the significance of each covariate was made the density estimates very sensitive to incorrect tested individually, and any which were not significant assignment of individual birds into either of the two at the 10% level were removed from further analysis. bands, and we therefore concluded it would be far more At this stage, we also tested for quadratic relationships robust to use the actual counts in subsequent analyses. in nine variables (eight associated with tree and under- Habitat data were collected at each point-count storey structure plus altitude) thought most likely to location in study sites, with this point forming the cen- show such relationships. tre of a 25 m radius circle. Some data were collected at In the second stage possible covariates were catego- this scale, and some from four 5 m radius sub-plots cen- rized into groups of similar variables (Table 1 ), these tred 12.5 m from the centre of the plot in each of the being: (a) those associated with large-scale variables; (b) four cardinal directions. For variables recorded at this field-layer characteristics; (c) understorey structure; (d) sub-plot level, means of the four datasets were taken. tree structure; (e) deadwood; (f) landscape; (g) deer Habitat data at the woodland scale were calculated as impact; and (h) interspecific competitors (the latter for the mean of each variable across all point-count loca- Marsh Tits only). For some covariates there were no tions per wood. A table outlining each habitat variable, similar variables, these, therefore, remained ungrouped the level and unit of measurement and a description of and did not enter Stage 2. If more than one of the how each habitat variable was collected is provided in variables in a given group was significant at the univari- Appendix 1 . Certain non-habitat variables were also ate stage, they were entered into a multivariate backward included in the analyses to test some suggested reasons stepwise model, and only those terms which remained for Marsh Tit decline (see Appendix 2 ). significant at the 10% level were entered into the final The composition of surrounding habitat within 3 km model stage. radius buffer circles centred on the central location of each In the third stage, those variables remaining from site was calculated. This was done using the Centre for Stage 2, and any from Stage 1 which were not in a Ecology and Hydrology’s (CEH) Land Cover Map (LCM) group, were entered into a final model. This model was 2000 within ArcGIS Version 9. The percentage composi- run in a backward stepwise fashion again, removing the tion of all habitat classes at LCM level 2 within these cir- least significant term, until only those terms significant cles was calculated. The 15 habitat variables with the at the 5% level remained. These then formed the final highest percentage around sites contributed 98% of the models for the two species. total area, and these variables were then grouped into eight We carried out this three-stage process to account for broad habitat categories. Principal components analyses intercorrelations between predictors. We did not want (PCA) were used to reduce the number of landscape to reduce the number of variables entering the model, as variables entering the analyses. Principal Component (PC) so little is known about the requirements of these bird 1 explained 28% of the variation, and described a gradient species, and to do so could have inadvertently removed from an agricultural landscape to a non-agricultural land- important variables. We are aware that this is something Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 scape, whereas PC2 (explaining 17% of the variation) was of a data-mining approach, but feel that as a first stage a gradient from a wooded landscape to a non-wooded, exploration of national habitat requirements this grassier landscape (Amar et al. 2006). approach is justified. The results we present are therefore Data on spring weather conditions for each site were meant as a first step, and further research testing our obtained from the UK Met Office Climate Impacts initial findings more stringently should follow. Programme (CIP). The 5-year average (1996–2000) was We were interested, first, in the woodland-scale cor- calculated for three weather variables for April and May: relates of species presence. However, as Blue Tits were temperature, rainfall and the number of days where rain- present in all of the woodlands studied, this analysis fall ≥ 1 mm. PCA was again used to reduce the number was only carried out for Marsh Tits. The probability of of climate variables entering the model. PC1 explained Marsh Tit presence was modelled using the binary 76% of the variation, and described a gradient from the logistic regression procedure (proc logistic ) in sas drier east to the wetter west (Amar et al. 2006). 9.1. Goodness-of-fit was tested using the Hosmer– Lemeshow statistic (Hosmer & Lemeshow 1989), and a range of model performance statistics, as presented in Statistical analysis the results, was examined. A three-stage model selection process was used with Secondly, in occupied woods only (for both Blue both species, and for both models for Marsh Tits (see Tits, n = 181 and Marsh Tits, n = 114), we examined

Table 1. A comparison of the results of modelling the habitat correlates of Marsh Tit presence and Marsh Tit and Blue Tit abundance in woodlands.

Marsh Tit Blue Tit

Presence Abundance Abundance

Logistic GLM GLM

Groups V ariables n = 181 n = 114 n = 181

Large-scale Locality 36.3 °°°° 3.16 °°° 10.51 °°°° Weather PCA 0.02 ++++ 0.00 + Field layer Bracken 0.52 −− 0.62 +++ Bramble 0.89 ++ Herb 0.19 + 0.75 −− 0.05 − Grass 0.53 −− 3.80 −−−− 1.65 −− Moss 0.23 −−− 2.04 −− Bare ground Leaf litter 0.34 ++ 0.01 +++ 1.12 + Understorey Cover 0.5–2 m 0.13 +++ 0.02 ++ 1.51 ++ Cover 2–4 m 6.4 ++ Cover 4–10 m 0.54 + 1.91, 1.74, ns, ∩ Horizontal visibility 1.86 −−−− 10.99 −−−− 4.80, 5.08, ns, ∪ Tree size Canopy cover 8.0 +++ 1.71 −− 0.55 − Basal area dbh 4.87 ++ Max height 1.03 + Deadwood Dead trees 0.61 ++ Dead limbs 0.36 − 12.64 ++++ Fallen wood 0.18 +++ Landscape GIS PC1 3 km 0.90 ++++ 0.94 −−−− PC2 3 km 5.05 −− 2.61 −−− Deer Deer PC1 0.09 ++ 0.43 ++ Deer PC2 0.46 ++ 0.02 −−−− Interspecific competition BT abundance na 0.07 +++ na GT abundance na 0.07 +++ na BT + GT abundance na 13.46 +++ na Ungrouped Dominant tree 1.96 °° 1.56 °°° Lichen 0.07 −−−− Ivy Shrub diversity 6.6 ++++ Water features Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 Altitude 0.49 −− 1.80 − 0.50, 0.42, ns, ∪ Size 0.52 + Tracks 2.59 + Drey density 0.05 +++ 0.00 ++

Note: Variable names in bold are those where the effect of the quadratic term was tested. Fully shaded grey cells are the variables retained in the final model stage, grey shaded values (not full cells) denote the variables retained after the within-group analysis and un-highlighted values the variables significant at a univariate stage. Effect sizes of the final model variables are shown; and of all other significant variables after being added into the final model one by one (presence analysis = Wald statistic, abundance analysis = F statistic). The number of symbols shows the level of significance (i.e. + P < 0.1; ++ P < 0.05; +++ P < 0.01; ++++ P < 0.001) and the direction of the relationship (i.e. + , − , ∩ or ∪); °, categorical variable, hence no directional effect; na, variable not appropriate for the species/test; ns, not significant; GLM, generalized linear model; PCA, principal components analysis; PC, principal component; GIS, geographic information system; GT, Great Tit; BT, Blue Tit.

the correlates of bird species abundance. A generalized surveyed in each wood as an offset, to account for the linear model ( glm: proc genmod ) in sas 9.1 was used. likelihood of higher species counts in woods where The total number of individuals of each species counted more points were surveyed. The use of this offset, in a wood was fitted as the response variable. A Poisson meant that we were effectively modelling the number error structure was specified, with a logarithmic link of birds per point. The proportion of deviance ( R2 ) and the natural logarithm of the number of points explained by locality, the final model covariates, and

both locality and the covariates combined were exam- concordant = 88.6, R 2 = 0.54, Hosmer and Lemeshow ined, and are presented in the results. goodness-of-fit = 0.77; Table 1 ). Marsh Tits were more likely to inhabit woods in the south and east of England, and at higher values of shrub diversity, canopy cover and Conditions required for habitat management understorey cover at 2–4 m (mean ± se: shrub diversity, The second aim of this study was to evaluate the range of occupied = 0.27 ± 0.01, unoccupied = 0.20 ± 0.01; canopy habitat conditions which should encourage the presence, cover, occupied = 12.5 ± 0.17, unoccupied = 11.7 ± 0.23; and increase the abundance, of each species. Therefore, cover 2–4 m, occupied = 26.5 ± 1.37, unoccupied = 18.1 we used the parameter estimates from the model outputs ± 1.49; Fig. 2 ). to calculate the range of habitat conditions that were likely to lead to a greater than average probability of wood Abundance of Marsh Tits and Blue Tits in occupancy and greater abundance. occupied woods For each species, we completed four steps. First, we calculated the average occupancy and abundance of The abundance of Marsh Tits in woods ( n = 114) was each bird species, and, secondly, using each covariate associated with 17 of the covariates in the univariate retained in final models or univariately significant at P < analysis (Table 1 ). Eight of these were entered into the 0.01, we ran the univariate analysis to obtain the inter- final model stage. Locality and three covariates were cept and parameter estimates. Thirdly, we used these fig- retained in the final model; these were strongly associ- ures, along with the average occupancy and abundance, ated with Marsh Tit abundance (P < 0.01). This model to solve for the value of the covariate x1 at which aver- explained 30% of the variation in Marsh Tit abundance 2 2 age occupancy or abundance occurred. We repeated this (locality only, R = 0.18; other covariates only, R = 0.22, step but used the maximum occupancy or abundance to Table 1 ). Abundance increased with decreasing horizon- solve for x2 at which maximum occupancy or abundance tal visibility, increasing predominance of woodland in occurred (details of the equations used are given in the landscape and increasing numbers of interspecific Smart et al. 2007). Finally, we also calculated the maxi- competitors (Fig. 3 ). mum and minimum values measured for each covariate The abundance of Blue Tits in woods ( n = 181) was and constrained the predicted range of habitat condi- associated with 20 of the covariates in the univariate tions (x1–x2) between these values. analysis (Table 1 ). Twelve of these were entered into the Some of the units of measurement for the habitat final model stage. Locality and two covariates were covariates were not ideal for translation into man- retained in the final model, which explained 27% of the 2 agement prescriptions. We therefore converted these variation in Blue Tit wood-abundance (locality only, R 2 to more meaningful measures. For example, shrub = 0.18; habitat covariates only, R = 0.13; Table 1 ). diversity is calculated for the main analyses as the Abundance increased strongly with increasing diameter at breast height (dbh) of trees and increasing number of

Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 proportion of all shrub species present (n = 36); for the management prescriptions we converted this dead limbs (Fig. 4 ). Removing the two apparent outliers back to an actual number of shrub species, to give from the dead trees dataset (see Fig. 4 ) did not change woodland managers a target number, by multiplying the inclusion of this variable in the final model. the given value by 36. Full details of all conversions Therefore, we left these two data points in the dataset. are provided in Smart et al. (2007). Conditions required for habitat management RESULTS Our second aim was to provide woodland managers with criteria to manage woodlands for Marsh Tits. Occupancy of woods by Marsh Tits Table 2 shows the final model covariates for each One hundred and eighty one woods were included in the species, and the range of conditions required to achieve occupancy analysis, of which Marsh Tits occupied 114. mean to maximum occupancy and abundance. For the first stage of the analysis, 22 of the 34 covariates Table 2 shows that, for Marsh Tits, ensuring good tested had a univariate association with Marsh Tit occu- understorey cover (and hence low horizontal visibility) pancy (Table 1 ). Of these, 14 were entered into the final and high shrub species diversity are the most important model stage. Three habitat covariates plus locality were aims for habitat management. Conversely, for Blue Tits, retained in the final model (area under curve = 0.89, % there was no importance placed on managing the

Figure 2. The influence of (a) shrub diversity; (b) vegetation cover 2–4 m; and (c) canopy cover on the probability of Marsh Tits Figure 3. The effect of (a) horizontal visibility; (b) landscape PCA 2 χ2 occupying woods (final model: R = 0.54; locality, Wald 7 = 2; and (c) the abundance of interspecific competitors on Marsh Tit χ2 2 36.3, P < 0.0001; shrub diversity, Wald 1 = 6.6, P = 0.01; abundance within occupied woods (final model: R = 0.30; locality, χ2 cover 2–4 m, Wald 1 = 6.4, P = 0.01; canopy cover, Wald F7,114 = 3.16, P = 0.004; horizontal visibility, F1,114 = 10.99, P = χ2 1 = 8.0, P = 0.05). Bars represent the number of woods from 0.001; landscape PCA2, F1,114 = 5.05, P = 0.03; interspecific which Marsh Tits were absent (grey bars) or present (clear bars). competitor abundance, F1,114 = 13.46, P = 0.0004). Lines were Lines were fitted from the final model output (solid line) and from fitted from the final model output (solid line) and from the final model the final model minus the locality effect, if the habitat covariate was minus the locality effect, if the habitat covariate was still significant still significant (dashed line; P < 0.05). Each line was fitted after (dashed line; P < 0.05). Each line was fitted after accounting for the accounting for the parameter estimates of the other continuous parameter estimates of the other continuous explanatory variables in explanatory variables in the model, assuming a mean value for the model, assuming a mean value for each. For explanation of units each. For explanation of units see Appendix 1. see Appendix 1.

Marsh Tits and Blue Tits across England and Wales. Although we would not expect wholly uniform bird- habitat effects across all localities, we were interested in detecting general national trends, which existed regardless of the locality concerned, to enable us further to understand the requirements of these species at the national scale. Therefore, although there are likely to be regional, or locality-specific, trends contained within the data, we have not focused on these here, and consider them to be beyond the scope of this study. Marsh Tit occupancy was best predicted by locality and three of the habitat covariates: cover at 2–4 m, canopy cover and shrub diversity. This final model explained 54% of the variation, suggesting that the model fitted the data well, and indicating that these understorey variables, and canopy cover, are important for the presence of Marsh Tits in woodland. Furthermore, three out of the four ‘understorey group’ variables tested were significant for Marsh Tit occupancy, two of which entered the final model stage. Only cover at 4–10 m in the understorey group showed no detectable relationship. This was strong evidence that a diverse understorey with good cover, up to 4 m, is important for Marsh Tit woodland occupancy. At occupied sites, Marsh Tit abundance was best predicted by locality, horizontal visibility, the amount of wooded habitat in the surrounding landscape, and the number of interspecific competitors. This model explained 30% of the variation in Marsh Tit abundance. There was a strong negative relationship between Marsh Tit abun- Figure 4. The effect of (a) maximum tree diameter at breast dance and horizontal visibility, i.e. high abundance height; and (b) the number of dead limbs on trees on Blue Tit occurring with low visibility, although the other under- 2 abundance within occupied woods (final model: R = 0.27; locality, storey group covariates showed only weak relationships. Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 F = 4.8, P < 0.0001; dbh, F = 5.8, P = 0.02, number of 9,181 1,181 Therefore, we conclude that the amount of understorey dead limbs, F 1,181 = 11.9, P = 0.0007). Lines were fitted from the final model output (solid line) and from the final model minus the cover is important in predicting both occupancy and locality effect, if the habitat covariate was still significant (dashed abundance of Marsh Tits in woods in England and line; P < 0.05). Each line was fitted after accounting for the Wales. parameter estimates of the other continuous explanatory variables in the model, assuming a mean value for each. For explanation of Given recent and past studies on Marsh Tits, rela- units see appendix 1 . tionships with the understorey layer (Cramp & Perrins 1993, Hinsley et al. 2007), deadwood, (Cramp & understorey layer, and a lack of management, increas- Perrins 1993) woodland size (Hinsley et al. 1996), tree ing the availability of large mature trees and dead wood height (Broughton et al. 2006) and a wooded landscape were found to favour high abundance of Blue Tits. (Cramp & Perrins 1993), could have been expected. We did find relationships with understorey cover and landscape PC2 (wooded landscape), and the relation- DISCUSSION ship with canopy cover perhaps supports the relationship with tree height found by Broughton et al. (2006); Understanding the habitat requirements only a weak negative relationship with dead wood was The first aim of this study was to provide quantitative recorded, however. As Marsh Tits are hole-nesting evidence of the habitat and other requirements of birds and, in the UK, do not readily take to nest boxes,

Table 2. Predicting the habitat conditions which are likely to provide average to maximum occupancy (Marsh Tit only) and abundance (Marsh Tit and Blue Tit) of the study species.

Species/test Group Variable Range Average Max

Marsh Tit presence Locality − Wales, + East Understorey Cover 2–4 m (%) 0–66 22 66 Tree size Canopy cover (%) 0–100 76 100 Other Shrub diversity (no. spp) 0–20 8 20 Marsh Tit abundance Locality − Wales, + South Understorey Horizontal visibility (%) 30–96 65 39 Landscape W ooded habitats (%) 0–66 29 66 Blue Tit abundance Locality + South and East Tree size dbh (cm) 24–127 59 127 Deadwood Dead limbs (No. ha−1 ) 0–528 56 528

Note: Only those habitat variables retained in the final model (see text) are presented. Range = the range of the habitat variable present in the data.

this result seemed surprising. However, it might have problems with the deer impact PCA analyses, suggest- arisen because Marsh Tits readily use cavities within ing that these results should be interpreted with living trees, rather than those in dead wood, probably caution. Furthermore, the presence of deer, squirrels to reduce the likelihood of nest predation (Wesołowski and other tit species could all be expected, in general, 2002). Furthermore, if dead wood is not limiting, and to be positively associated with mature woodland and hence there is no shortage of nest holes, then a rela- thus, given sufficient habitat quality, positive relation- tionship may not be detected. Marsh Tits have been ships with Marsh Tits in their prime habitat might be shown to be sensitive to woodland size (Hinsley et al. expected. Therefore, these results remain speculative 1996), but we did not detect a relationship in this anal- and experimental work is required to investigate these ysis. However, this could be because most of the wood- aspects in more detail. lands used here were relatively large (<10% were As Blue Tits occupied all of the study woodlands, it smaller than 20 ha, see Hinsley et al. [2007]). The rela- was not possible to carry out a wood-occupancy analysis tionship with shrub diversity was a novel finding in for this species. Blue Tit abundance in woodland was this study. best predicted by locality and two habitat covariates, Predation by grey squirrels, deer impact, and inter- tree dbh and the number of dead limbs. This model specific competition were included in the analyses, explained 27% of the variation in Blue Tit abundance. specifically because these factors have been proposed as Understorey group characteristics were not as important potential causes of Marsh Tit decline (Fuller et al. for Blue Tits as for Marsh Tits, with no or only weak Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 2005). However, the results obtained here provide little effects for three of the four covariates. Cover up to 2 m support for these hypotheses as major drivers of decline. entered into the final model stage, but was not retained. Marsh Tit occupancy showed a positive relationship All three dead wood covariates showed positive rela- with squirrel drey density and both measures of deer tionships with Blue Tit abundance, and two of these activity. Marsh Tit abundance was positively related to entered the final model stage. This is interesting, given drey density and interspecific competitor abundance. the unimportance of this dead wood group to Marsh Furthermore, the positive relationship with interspe- Tits, and the fact that, unlike Marsh Tits, Blue Tits cific competitors was retained into the final model, and readily take to nest boxes. However, even fallen wood was highly significant (P < 0.001). This result is consis- was positively related to Blue Tit abundance, suggesting tent with that of Siriwardena (2006), who found that dead wood may be important as more than just a significant positive relationships between Marsh Tits provider of nesting cavities for Blue Tits. It is also and their potential competitors. Lewis et al. (2007) also possible that abundance of dead wood acts as an indica- found that interspecific competitors did not appear to tor of mature woodland, but this would be expected to be a factor in the decline of the closely related Willow benefit Marsh Tits as well. Defining the habitat require- Tit Poecile monatana . These results may suggest that all ments of Blue Tits from the literature (Cramp & Perrins three of these factors are unlikely to be important in 1993) was more difficult than for Marsh Tits, because Marsh Tit decline. However, Amar et al. (2006) discuss Blue Tits were more generalized in their requirements.

The predicted positive relationship between Blue Tit our woods (Gill & Fuller 2007, Hopkins & Kirby abundance and dead wood (Cramp & Perrins 1993) was 2007). In such woodlands, management to encourage a borne out in our analysis, but many of the other rela- healthy and diverse understorey is unlikely to succeed tionships were not as predicted, or only weakly so. The without undertaking some level of deer control, and other habitat covariate retained in the final model, tree more detailed analyses of deer impacts are urgently dbh, was not predicted and suggests a possible impor- required. tance of tree age for Blue Tit abundance. This could The Marsh Tit population appears to have stabilized explain the observed result with dead wood, as older at a historically low level (Eaton et al. 2006). However, trees are more likely to produce dead wood. managing woodlands for their benefit, and hence encouraging a reversal of their population trend, is still to be encouraged, especially as other threats to their Conditions required for habitat management population, such as impacts of climate change, are still Our second aim was to provide woodland managers poorly understood. Two studies on the potential impact with criteria to manage woodlands for the benefit of of climate change have produced contrasting results. Marsh Tits. Conditions were also calculated for Blue Carpenter (2008) showed that by the 2080s, there will Tits to allow us further to understand the opposing be little climate space still available to Marsh Tits in population trends of the two species. Britain, whereas Huntley et al. (2007) showed little As expected, given its increasing population trend impact on climate space in Britain by the late 21st (Eaton et al. 2006), the values required for greater than century. average Blue Tit abundance are those which already Changes in woodland management have been impli- exist in much of British woodland today, with plenty of cated in the decline of other woodland bird species mature trees, and little need for understorey layer. (Amar et al. 2006). Experimental manipulations of Much of the historical management of woodlands, such woodland habitat, at a woodland scale, to encourage a as coppicing, has now ceased, and many of these wood- diverse understorey with good cover, are required to lands are no longer actively managed (Mason 2007), understand how woodland policy and management allowing Blue Tits to thrive. This same lack of manage- influence the relationships between woodland structure ment, allowing canopy closure, may have been detri- and the diversity and abundance of bird, and other mental to the Marsh Tit population, due to a reduction wildlife, populations. in available understorey vegetation. However, understorey cover from 2–10 m in the RSPB surveyed sites used in the RWBS, in fact, increased substantially ACKNOWLEDGEMENTS between 1980 and 2003 (Amar et al. 2006). We are indebted to the RSPB and BTO volunteers who Interestingly, at these sites, the Marsh Tit population carried out the survey work. Without them, the dataset had actually increased by 27%, compared with a 27% on which this study is based would not exist. We wish to Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 decline in the BTO surveyed sites (Amar et al. 2006). thank Dr Ken Smith for assisting in the setting up of this This suggests that the Marsh Tit population increased work, and the RWBS project steering group for providing at sites where understorey also increased, and provides assistance and advice. Dr Rob Fuller, Dr Joseph Tobias and Dr Tomasz Wesołowski provided valuable comments further evidence for the potential importance of the on an earlier draft, which greatly improved the manu- understorey to this species. script. The original RWBS survey was funded by Forestry We have provided woodland managers with the Commission England and Wales, Natural England, Defra, criteria to begin to assess whether their woodland is RSPB, BTO and the Woodland Trust. The current work suitable for management for the benefit of Marsh Tits. was funded by the RSPB, Natural England and by a However, before a woodland manager embarks on such Natural Environment Research Council postgraduate a programme, multiple factors need to be considered. training award to JEC. For example, the woodland location, whether Marsh Tits were present historically, the impact on the bird REFERENCES and plant species community currently present, the historical use of the woodland, and, importantly, the Amar, A., Hewson, C.M., Thewlis, R.M., Smith, K.W., Fuller, problem of deer numbers. Despite the apparent lack of R.J., Lindsell, J.A., Conway, G., Butler, S. & MacDonald, M. 2006. What’s Happening to our Woodland Birds? Long-Term impact of deer found in our analyses, there is no doubt Changes in the Populations of Woodland Birds. RSPB Research that deer have radically altered the nature of many of Report no. 19. RSPB, Sandy, UK.

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( MS received 12 December 2008 ; revised MS accepted 7 May 2009 )

APPENDIX 1. HABITAT VARIABLES

The location of different aspects of woodland habitat structure, the variable name, level and unit of measurement and description of how each variable was measured during habitat surveys of 252 UK woodlands in 2003 and 2004. Variable names in bold are those variables where we tested for a quadratic effect.

Location V ariable Level/unit Description

Field layer All variables Sub-plot/% cover The % cover of each variable below 0.5 m was estimated across the sub-plot. Understorey Cover 0.5–2 m Sub-plot/% cover The total % cover of vegetation of the 5 m sub-plot as if viewed Cover 2–4 m from above, considering the vegetation in each height band in Cover 4–10 m turn. Horizontal visibility Sub-plot/no. Number of orange sections (max 12) at least 50% visible of an orange and black pole recorded. Pole 2.4 m long, sections 10 cm long. Tree structure Canopy cover Sub-plot/no. The number of 2 cm squares (max 16) in a 4 × 4 wire grid in which at least 50% of the square was occupied by canopy-level vegetation (min 10 m high) when viewed directly from below. The grid was held horizontally 60 cm above the observer using a marked stick with a plumb line. Basal area Plot centre/no. tree stems Using a standardised relascope to count the number of stems of each tree species that scored accordingly (Smart et al. 2007). dbh Plot centre/cm Tree with the maximum diameter at breast height Max height Plot centre/m Tree with the maximum height. Deadwood Dead trees Plot centre/no. Number of dead trees. Dead limbs Sub-plot/no. Number of dead limbs attached to trees at any height in the sub-plot. Ground wood Sub-plot/no. Number of pieces of dead wood on the ground > 10 cm diameter and 1m in length. Other habitat Dom tree Plot centre/category: ash, Dominant tree species – proportion of oak, ash, beech and beech, birch or oak birch from the total number counted by the relascope. Species with the highest proportion equals the dominant species. Lichen/ivy Sub-plot/category Abundance scored as 0 = absent, 1 = present, 2 = frequent. Shrub diversity Plot centre/index T otal number of shrub species divided by 36 (total number of shrub species recorded across all RWBS sites). Water features Plot centre/presence Presence/absence wet features (bog, stream, flush or pond). Altitude Plot centre/m Recorded from a GPS unit. Size W ood-level only/ha Using the National Inventory of Woodland and Trees, the area of all polygons of contiguous (no gaps > 25m) non-coniferous

Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010 woodland was calculated. Tracks Plot centre/category Presence of tracks: 0 = none, 1 = single foot track, 2 = vehicle width track.

APPENDIX 2. NON-HABITAT COVARIATES

Additional non-habitat covariates used in analyses, with a brief description of how each variable was measured. See Amar et al. (2006) for further details of methods used for the predation and deer damage categories.

Category Covariate Description

Predation Squirrel density Counts of squirrel dreys along a 1000 m transect. Counts were analysed using DISTANCE software to provide an estimate of drey density per woodland. Deer damage Deer PC 1 Principal component axis 1 – deer activity. A high score indicates high levels of deer activity. Deer PC 2 Principal component axis 2 – deer activity. This separates sites with abundant browsed bramble from those with a high browse line. Deer PCA based on stool counts, deer slots, deer pellets, browse line, browse height, browsed bramble, browsed stems, frayed stems and trackways per 100m. Interspecific competitors Blue Tit, Great Tit and combined Blue Included in Marsh Tit wood-abundance analysis only. Abundance taken and Great Tit abundance from RWBS dataset, and summed for combined species abundance.

PCA, principle component analysis; RWBS, Repeat Woodland Bird Survey. Downloaded By: [Charman, Elisabeth] At: 11:35 10 February 2010