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Basic and Applied Ecology 12 (2011) 97–106

ocal and landscape-scale forest attributes differ in their impact on birdssemblages across years in forest production landscapes

onstans Wellsa,c,∗, Stefan M. Böhma, Steffen Bochb, Markus Fischerb, Elisabeth K.V. Kalkoa,d

Institute of Experimental Ecology, University of Ulm, Albert-Einstein Allee 11, D-89081 Ulm, GermanyInstitute of Plant Sciences and Oeschger Centre for Climate Change Research, University of Bern, Altenbergrain 21,H-3013 Bern, Switzerland

Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, D-60325 Frankfurt (Main), GermanySmithsonian Tropical Research Institute, Balboa, Panama

eceived 10 May 2010; accepted 3 January 2011

bstract

Forest management practices and forest fragmentation are commonly recognised as factors influencing species distributionnd diversity, but it remains largely unknown how species turnover and fluctuations across years might affect generalisationsrom short-term predictions. There is also much uncertainty about how compositional heterogeneity within forests, throughheir partitioning into differently managed stands, influences species occurrence and diversity. We aimed to investigate birdpecies richness in different forest stands in a heterogeneous landscape and asked whether short-term studies commonly usedre sufficient to describe diversity–habitat relationships.

Breeding birds were monitored in 50 stands of differing forest types and management regimes in 2008 and 2009 in theeterogeneous forest production landscape of the Schwäbische Alb in SW Germany. Based on a database of 25 and 21 speciesbserved in 2008 and 2009, respectively, we estimated local species richness and species turnover with Bayesian hierarchicalulti-species occupancy models to account for imperfect detection. A suite of forest attributes at stand level (from local field

nventories) and landscape level, between 100 and 2000 m in extent, which were derived from forest management maps, werecreened for their explanatory power regarding bird species richness. The most meaningful variables were included in a finalet of models to account for effects of forest attributes on species occurrence and resulting gradients in species richness andurnover.

Bird species richness in 2008 was mostly explained by stand composition heterogeneity over a distance of 800 m aroundample locations, whereas in 2009 local stand features like stand succession type and stand age mostly explained species

ichness. Species turnover was significantly higher in young forest stands of thicket and pole-wood stages compared with oldorest stands of timber stages.

Variable multi-scale impacts of forest attributes on local species richness and turnover, together with year-to-year fluctuationsn the regional species pool, emphasise that the relative importance of local versus landscape-scale factors in determining bird

le-scale investigations that take structural and compositional forest

pecies richness may vary across years. Long-term and multip eterogeneity into account are necessary for improved prediction of species–habitat relationships.

∗Corresponding author at: Biodiversity and Climate Research Centre (BiK-F), Senckenberganalage 25, D-60325 Frankfurt a.M., Germany.el.: +49 69 79840216; fax: +49 69 79840170.

E-mail address: konstans.wells@uni-ulm.de (K. Wells).

439-1791/$ – see front matter © 2011 Gesellschaft für Ökologie. Published by Elsevier GmbH. All rights reserved.oi:10.1016/j.baae.2011.01.002

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8 K. Wells et al. / Basic and Applied Ecology 12 (2011) 97–106

usammenfassung

Die Vielfalt und Verbreitung von Arten in Wäldern sind oft in Zusammenhang von unterschiedlichen Bewirtschaftungsmaß-ahmen und Fragmentierung von Forstbeständen untersucht worden, wobei weitgehend offen ist, inwieweit Fluktuationen inopulationen über mehrere Jahre Rückschlüsse von Kurzzeitstudien beeinflussen. Weiterhin wurde die landschaftsbezogeneeterogenität von Waldarealen, die oft in unterschiedlich bewirtschaftete Bestände parzelliert sind, bisher wenig in Arten-abitat-Beziehungen berücksichtigt. Wir untersuchten die Artenvielfalt von Singvögeln in unterschiedlichen Forstbeständen

n einer heterogenen Kulturlandschaft mit besonderem Fokus auf der Frage, inwieweit Rückschlüsse auf Arten-Habitat-eziehungen aus Kurzeitstudien generalisierbar sind.Brutvögel wurden in 50 Forstbeständen mit unterschiedlichen Bestandstypen und Bewirtschaftungsweisen innerhalb der

leinräumigen Kulturlandschaft der Schwäbischen Alb in SW Deutschland in 2008 und 2009 erfasst.Basierend auf einen Datenbestand von 25 versus 21 beobachteten Arten in 2008 und 2009, wurde die Artenvielfalt und

luktuationen mit einem Bayesischen hierarchischen Gemeinschaftsmodell unter Berücksichtigung der Unvollständigkeit voneldbeobachtungen geschätzt.Mehrere Umweltparameter auf lokaler Bestandsebene (basierend auf Feldkartierungen) sowie auf Landschaftsebeneit Radien zwischen 100 und 2000 m um Beobachtungspunkte (basierend auf digitalen Forsteinrichtungsdaten) wurden

ezüglich deren korrelativen Zusammenhang mit der Artenvielfalt getestet. Die aussagekräftigsten Variablen wur-en dann in einem weiteren Modellierungsschritt auf deren relativen Einfluss auf Artenvielfalt und Fluktuationenntersucht.

Die Artenvielfalt in 2008 korrelierte am stärksten mit der Komposition unterschiedlicher Forstbestände in der Umgebung bis00 m um die Beobachtungspunkte, während die Artenvielfalt in 2009 deutlich stärker mit Variablen auf lokaler Bestandsebeneie Sukzessionsstadium bzw. Bestandsalter korrelierte. Die Fluktuationen im Vorkommen von Arten waren signifikant höher

n jungen Beständen wie Dickung und Stangenholz als in älteren Baumholzbeständen.Der variierende Einfluss von Variablen unterschiedlicher räumlicher Skalenebene auf lokale Artenvielfalt und Fluktuatio-

en im Vorkommen von Arten sowie die Variabilität im regionalen Artenpool von Singvögeln über Jahre unterstreichen,ass der relative Einfluss von lokalen und landschaftsbezogenen Umweltparametern auf Artenvielfalt über Jahre variierenann.

Langzeitstudien, die Umweltparameter auf unterschiedlichen Skalenebenen und auch Parameter wie strukturelle und kom-ositionelle Heterogenität bewirtschafteter Wälder berücksichtigen, sind daher notwendig, um den Zusammenhang zwischenrtenvielfalt und der zugrundeliegenden Umwelt vorherzusagen.2011 Gesellschaft für Ökologie. Published by Elsevier GmbH. All rights reserved.

ing; Hi

MarHtmiSfmm

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eywords: Birds; Forest fragmentation; Habitat-association modellandom forest classifier; Species turnover; Silviculture

ntroduction

Forest cultivation and management have manifold effectsn the structure, composition, and geometry of forest areas,hich in turn determine species distribution and biodiversityatterns of forest wildlife. In reasonably large and homoge-eous forest areas, forest stand characteristics such as treeomposition, stratification, or type and intensity of silvicul-ural practices are the main drivers of forest biodiversityNiemi et al. 1998). Increasing habitat degradation, frag-entation, and ultimately habitat loss as consequences of

orest harvest, however, often merge with stand-scale effects,ffecting species occurrence and richness in managed forestsnd landscapes (Andrén 1994). These two central aspectsf the landscape context, widely recognised as the amountnd type of habitats and their spatial arrangements, are often

onfounded with each other and therefore their effects are dif-cult to disentangle when trying to predict species occurrencend diversity patterns (Debinski & Holt 2000; McGarigal

Cushman 2002; Smith, Koper, Francis, & Fahrig 2009).

(Mfl

erarchical state-space; Landscape attributes; Multiple-scale effects;

oreover, the recognition that landscape-scale heterogeneitylso influences metapopulation dynamics and species occur-ence at a finer sub-landscape spatial scale (Wiens 1989;anski & Ovaskainen 2000) implies cross-scale interac-

ions of processes in space and time. Those interactionsake it difficult to predict species occurrence from a lim-

ted set of single-scale information (Peters et al. 2004;chooley & Branch 2007). An ongoing challenge is there-ore to find the proper scale at which ecological processes areost relevant for species–habitat relationships in landscapeosaics.Recent studies found that predictors of species occur-

ence vary across regions, suggesting that conclusions onpecies–habitat relationships drawn from one region areot necessarily transferable to another that differs in land-cape attributes such as habitat amount or fragmentation

Betts, Forbes, & Diamond 2007; Whittingham et al. 2007;

cAlpine et al. 2008). In addition, other factors such as dif-erences in regional species pools, which alter richness inocal assemblages, or abiotic factors like regional climate

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K. Wells et al. / Basic and A

radients, may influence species–environment relationshipsnd the output of habitat-association modelling approachesBoulinier et al. 2001). Recent developments in hierarchicalodelling and multivariate techniques allow a comprehen-

ive analysis of uncertainty in species-specific demographicraits and community measures, such as species richness andurnover, allowing us to disentangle stochasticity in observa-ion from biological patterns (Link & Sauer 2002; MacKenziet al. 2002; Clark 2003). As a consequence, the transcriptionf findings from habitat-association modelling into practi-al conservation strategies requires a critical assessment ofhether and how findings from short-term or regionally lim-

ted studies allow generalisations (Guisan & Zimmermann000).

Here, we investigated the influences of forest attributesn breeding bird assemblages in a heterogeneous landscape,ncluding fragmented forests that are partitioned into discrete

anagement units of stands of differing tree composition,ge, and management intensity. In the landscape context, upo now, ‘heterogeneity’ has been mostly considered by iso-ating suitable habitat from a surrounding matrix (Fischer &indenmayer 2007), whereas heterogeneity within a habitatas not been as rigorously tested for forests as for agricul-ural landscapes (Mitchell, Lancia, & Gerwin 2001; Benton,ickery, & Wilson 2003; Tscharntke, Klein, Kruess, Steffan-ewenter, & Thies 2005; Cushman, McKelvey, Flather, &cGarigal 2008). However, this aspect is crucial, as small-

cale partitioning of managed forests into distinct units isommon in landscapes with area-wide management andorest use in Central Europe, where timber harvest at thetand level is increasingly ‘optimised’ for economic reasonsBaskent & Keles 2005). Considering habitat heterogene-ty at the stand scale should therefore be of much interest,ince these units comprise the scale where managementctions take place and profoundly shape the overall forestandscape.

In heavily partitioned forests, such as the Swabian Alb“Schwäbische Alb”) in SW Germany, individual ranges ofird species may cover different forest stand types. Habi-at heterogeneity and selection of favourable forest stands

ay therefore influence all levels of animal movement pat-erns, from daily foraging to dispersal, and hence affect localpecies richness and turnover.

We combined bird censuses with forest management datao test how attributes of a heterogeneous forest habitat mightnfluence local species richness and turnover of breedingird assemblages. Furthermore, as species occurrences varyrom year to year, we tested whether the impact of forestttributes on local species richness and turnover was con-istent between years. As a first step, we took data from awo-year sampling period, representing a time frame typi-al of many community studies (Marsh & Trenham 2008).

nly if patterns are consistent between years, short sam-ling periods might be sufficient to understand how speciesccurrence and richness is influenced by environmentalradients.

tdSs

Ecology 12 (2011) 97–106 99

ethods

ird survey and environmental predictor layers

Breeding birds were observed during the breeding seasonsn 2008 and 2009 on 50 permanently marked plots in dif-erent forest stands in the Schwäbische Alb, SW GermanyE9.58024, W9.20362, N48.53435, S48.34996). This regionomprises a submontane Karst area (elevation 500–900 m.s.l.) with annual average temperatures of between 6 and◦C and a vegetation period of ca. 200 days (Fischer et al.010).

The land cover was originally dominated by commoneech (Fagus sylvatica), but has been substantially changeduring a long history of human land use by the clearancef parts of the forests and farming practices, resulting inheterogeneous landscape mosaic covered with ca. 41%

orest, 24% grassland, and 22% fields (unpublished data).orest stands range from remains of old-growth beech for-st, through continuous stands with low-intensive logging,o actively managed deciduous, mixed deciduous-coniferousnd coniferous age-class forests. Besides beech, common ashFraxinus excelsior) and sycamore (Acer pseudoplatanus)ere the most common deciduous tree species. Norway

pruce (Picea abies) monocultures have been largely plantednd comprise ca. 20% of forest stands. Nearly all forestsre comprised of discrete and differently managed patchesith a mean area of 3.6 ± 5.8 ha (min/max: 0.004/84.5 ha)

ach.As part of the interdisciplinary long-term project ‘Biodi-

ersity Exploratories’ (www.biodiversity-exploratories.de),total of 50 permanently marked plots were monitored five

imes during the breeding season between March and Juneach year. During each survey, one of us (SMB) carried outfive-minute point count and identified all breeding birds by

ong and visual sightings within a 50-m radius. The distanceetween sampling plots ranged from 324 to 17 783 m (mean369 ± 5052 m).

Our environmental data set included variables fromwo sources: detailed field inventories of the vegetationt the level of sampling plots, and a regional digitalorest management map with information on tree com-osition, stand age, and management type which wasbtained from the regional authorities (Regierungsprä-idium Tübingen, Fogis 2006). The digital maps accu-ately described at least 90% of forest stands, asonfirmed by our own field inventories (unpublishedata).

We categorised forest stand composition (based oneech and spruce as the dominant overstorey treepecies, with a threshold level of 70% of dominant

rees) into six categories: beech, beech-mixed, mixed-eciduous, spruce, spruce-mixed, and mixed-coniferous.tand age was defined as the oldest tree layer withintands.

100 K. Wells et al. / Basic and Applied Ecology 12 (2011) 97–106

Table 1. Forest attributes considered in the analysis of local- and landscape-scale influences on bird species richness and species turnover.

Forest attribute/scale Description

Plot level (100 m × 100 m)Stand composition Categorical description of stand composition based on dominant tree species with 3

levels: beech, beech-mixed, spruceStand succession stage Categorical description of stand succession stage with 4 levels: thicket, pole wood,

young timber, old timberStand age Age of oldest tree layer within standForest edge Distance to nearest forest edgeManagement type Categorical description of management type with two levels: age-class, continuous

Vegetation inventory (20 m × 20 m; 3 layers)Cover shrubs/trees 5–10 m/trees >10 m Percent coverage with shrubs, trees 5–10 m, and >10 m (three variables)Cover Fagus sylvatica trees >10 m Percent coverage with Fagus sylvatica >10 mCover Picea abies trees >10 m Percent coverage with Picea abies >10 mSpecies number shrubs/trees 5–10 m/trees >10 m Species number of shrubs, trees 5–10 m, and >10 m (three variables)Shannon index shrubs/trees 5–10 m/trees >10 m Shannon index of species numbers of shrubs, trees 5–10 m, and >10 m (three variables)

Landscape level (within 100–2000 m radii)Forest area Total area (%) covered with forest within landscapeForest patch number Number of forest patchesForest patch shape Perimeter/area ratio of forest patchesStand area mean Mean areas of standsStand age mean Mean age of oldest tree layer of standsStand number heterogeneity Stand heterogeneity based on the Shannon index of numbers of different stand

compositionStand area heterogeneity Stand heterogeneity based on the Shannon index of the area of different stand

compositionStand area–age heterogeneity Stand heterogeneity based on the Shannon index of numbers of different stand

istinguB ) cover

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composition deech area Total area (%

At the plot level, we further distinguished successionaltages (thicket, pole wood, young timber, old timber) andanagement type (age-class forest versus continuous less-

ntensively managed forest). All sampling plots were locatedn either beech, beech-mixed, or spruce forest out of the sixategories present in their surroundings. We estimated theroportional covers of shrubs and trees of 5–10 m height andrees >10 m height from vegetation inventories conducted in008 on 20 × 20 m areas within sampling plots. We furtheralculated the number of species and Shannon indices fromhese data, as well as the coverage with the dominant trees. sylvatica and P. abies to account for differences in localtand composition (Table 1).

At landscape scales, we used the number of distinct for-st stands, mean stand sizes, mean perimeter/area ratios,he number of different stand composition (patch richnessith/without distinguishing stand age) at 20 areas of between00 and 2000 m radius centred on each of the 50 sam-ling plots using ArcGIS 9.1 (ESRI, Redlands, California)s landscape metrics. We determined nine variables forach spatial area, which we considered as most informative

Table 1). Hence, overall selection of variables for modelonstruction was done from a pool of 17 plot-level and aotal of 180 landscape-level forest attributes from 20 spatialreas.

saaH

ished further by stand ageed with beech stands within landscape

ata analysis

Multi-species site occupancy models have been recentlydapted to account for imperfect detection during animalonitoring, and have proven useful tools to model species

ommunity variables, such as species richness and turnover,ithin a consistent and meaningful framework (MacKenzie

t al. 2002; Dorazio and Royle 2005; Kéry, Royle, Plattner,Dorazio 2009). Within such a hierarchical framework

ased on observations of bird species i (i = 1, 2, . . ., N) onhe various plots j (j = 1, 2, . . ., 50) during five annual sur-eys k (k = 1, 2, . . ., 5) in either sampling year a (a = 1, 2),e considered uncertainty in species’ occurrence as a binary

tate random variable z(i, j, a). Given the observed speciesresence/absence during a particular survey x(i, j, k, a), thebserved presence (x(i, j, k, a) = 1) defines the state as z(i, j,) = 1, whereas non-observation x(i, j, a) = 0 includes uncer-ainty of whether a species was present but not detected orruly absent. We assume that conditional on z(i, j, a), z(i, j, k, a)re Bernoulli trials determined by a detection probability p(i,, k, a) of species i at plot j during the kth survey in year a. With

uch a background we suppose x(i, j, k, a) ∼ Bernoulli(z(i, j,) × p(i, j, k, a)) for modelling species detection and z(i, j,) ∼ Bernoulli(ψ(i, j, a)) for modelling species occurrence.ere, ψ(i, j, a) is the probability of occurrence of species

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at plot j during year a. We implemented an extension ofhe model described by Kéry and Royle (2009) to allowor multiple species. Detection probability was assumed toary for each species with the date of the survey (linear andquared effects) such that logit(p(i, j, k, a) = νi,a +β1i,a × date+β2i,a × date 2.The baseline vi,a serves as a species-specific but annually

uctuating ‘average’ detection probability based on a nor-ally distributed random error, whereas the coefficients β1i,a

nd β2i,a account for effect of survey date on each species.ince abundant species are more likely to be present on differ-nt plots and are also more likely to be detected, we followedhe approach by Dorazio and Royle (2005) and Kéry andoyle (2009), assuming that vi,a were jointly normally dis-

ributed with the intercept μi,a of logit(ψ(i, j, a)).We accounted for unobserved species in the assem-

lage by including another latent state variable in theodel and augmented species data with 100 unobserved

all-zero’ species as described by Royle, Dorazio, andink (2007) and Kéry and Royle (2009). Given thisinary state with w(i, a)∼Bernoulli(Ω) and Ω(a) ∼ unif(0,) of having species observed or not, we assumed(i, j, a)∼Bernoulli(ψ(i, j, a) × w(i, a)).

Overall species richness was then calculated as the sumf w(i, a) and the estimated number of species to occur onplot j as the sum of w(i, a). Plot-specific species turnoveras calculated as 1 − J(j)1,2, where J(j)1,2 is Jaccard index of

ommunity similarity.The ratio of species present during both years

s calculated as (N(j)1, 2 =∑

z(i, j, a = 1) × z(i,, a = 2)) and species turnover is calculated as− J(j)1,2 = 1 − (N(j)1,2/(N(j)1 + N(j)2 − N(j)1,2)). Cal-ulation of species turnover included hence all speciesssumed to comprise the regional species pool and thendex ranges from 1 when no species are shared to 0 whenll species are shared. Hierarchical models were run inpenBUGS 3.1.2 (Lunn, Spiegelhalter, Thomas & Best009). The code is given in Appendix A in Supportingnformation.

We standardised all numerical explanatory variables so thathey each had a mean of zero and standard deviation (SD) ofne. Forest attributes typically involved much collinearitycross variables and scales. Because little is known of how topriori collect the biologically most relevant variables, we

pplied model averaging based on the random forest algo-ithm (Breiman 2001) to select forest attributes which mosteaningfully described landscape structure and relationshipsith bird species richness from the full set of variables. To this

nd, samples of randomly selected subsets of observationsestimated bird species richness) and explanatory variablesforest attributes) were used to compute the prediction accu-acy on the omitted observations. Mean square errors from

he regression trees were then averaged to estimate the rel-tive importance of explanatory variables. We built 10 000egression trees using the R package random Forest 4.5–30.hus we obtained a set of 11 meaningful forest attributes

cMst

Ecology 12 (2011) 97–106 101

ased on the random forest classification and Spearman’sank correlations of |r| < 0.5 among selected variables tovoid collinearity.

We finally constructed a set of alternative generalised lin-ar models, assuming Poisson distributions, from all linearombinations of selected forest attributes for examining birdpecies richness estimates for 2008, 2009 and the pooledpecies richness estimate for both years from our hierarchicalodel.We used Bayesian model averaging (BMA) for estimat-

ng the relative importance of selected forest attributes andstimating their mean coefficients with the R package BMS.2.5.

Bayesian model averaging calculates the posterior inclu-ion probabilities (PIP) of variables in a set of candidateariables from all possible combination of variables and aver-ges coefficients over all models.

We checked residuals of candidate models and observa-ions for possible spatial autocorrelation with Moran’s I test.hey were spatially weighted by k = 10 nearest neighboursnd a permutation test with 10 000 iterations (R packagepdep 0.4–36). We further tested for possible spatial cor-elation of species turnover across plots and geographicalistances between plots with Mantel tests based on 10 000ermutations. Since we found no pronounced spatial auto-orrelation (i.e., all P > 0.15 in a total of 484 Moran testsith permutations and Spearman’s r < 0.07 in both Mantel

ests), we assumed our analytical approach with no correc-ion for spatial dependence to be accurate despite the overlapf landscapes at larger extents. Analyses were conducted in2.9.2 (Ihaka & Gentleman 1996).

esults

atterns in local and regional bird speciesichness

We observed a total of 26 breeding bird species, of which5 were recorded in 2008 and 21 in 2009. Overall estimatesf species richness differed strongly. It was estimated as 28pecies (95% credible interval: 24–45 species) in 2008 and2 species (credible interval: 20–35 species) in 2009 (Fig. 1).

We detected 9 ± 2 species per plot in 2008 and 8 ± 3pecies per plot in 2009. The similar overall patterns andontrasts across plots suggest that our sampling with fiveeplicates per plot and year was accurate.

Mean probabilities of occurrences varied widely andanged from 15 to 96% in 2008 and from 11 to 88% in 2009mong species with detection probabilities of 6–66% in 2008nd 7–71% in 2009. The most common species were Eritha-

us rubecula, Turdus merula, Fringilla coelebs, in both years.ost of the commonly encountered species exhibited con-

iderable turnover in their presence on plots, with the highesturnover observed for Turdus philomelos and C. caeruleus.

102 K. Wells et al. / Basic and Applied Ecology 12 (2011) 97–106

F rarchii rs. Estih inter

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ig. 1. Overall species richness estimates from the multi-species hien 2008 (first plot), in 2009 (second plot), and in total for both yeaighest density probability indicated by solid vertical lines, and 95%

he lowest detection probabilities occurred in Phylloscopusonelli, Carduelis chloris, and Turdus pilaris.

Most species occupied fewer plots in 2009 comparedith 2008 (see Appendix A, Table 1). Species turnover onlots between 2008 and 2009 decreased significantly withtand age (Spearman’s r = −0.44, P = 0.001), and it was sig-ificantly larger in plots categorized as thickets and poleoods compared with old forest stands of timber succes-

ional stages (Kruskal–Wallis ANOVA H = 19.5, P < 0.001,.f. = 3) (Fig. 2).

andscape scale and collinearity

Due to the pronounced landscape heterogeneity in thetudy region, correlations between observations and land-cape variables showed considerable variation across scales

Fig. 3, exemplarily shown for two out of four landscapeariables considered in our models). Evaluating variabilityn importance of variables with the random forest algorithmevealed that 32 out of 180 landscape variables from all spa-

s(hl

cal model with augmented data (100 non-observed pseudo-species)mates are given as density distribution with the posterior mode ofvals given as dashed lines. *The number of observed species.

ial areas between 500 and 2000 m radii explained >5% ofean square errors in regression models. Due to collinear-

ty, however, it was only possible to include four of theseandscape-scale variables (additional to local-scale variables)n the models, namely forest area, stand age, stand area meannd stand area heterogeneity in our analysis. Most notably,e found that each landscape variable was strongly correlatedith measures from different/nearby landscape scale. This

uggests that conclusions on landscape-scale effects fromlimited set of two or three spatial scales are likely to be

nsufficient for a comprehensive landscape analysis.

ird species richness and turnover acrossnvironmental gradients

The influence of stand level and landscape variables on

pecies richness varied in 2008 and 2009 according to PIPFig. 4). In 2008, stand area heterogeneity over 800 m radiiad a strongly positive effect on bird species richness, fol-owed by an additional positive effect of coverage with P.

K. Wells et al. / Basic and Applied Ecology 12 (2011) 97–106 103

Fig. 2. Species turnover on plots classified by successional stagewith species turnover given as (1 – Jaccard index).

Fig. 3. Correlations (Spearman’s rank) of estimated species rich-ness in 2008 (black, solid lines) and 2009 (white, dashed lines)with landscape variables across landscape scales ranging from 100to 2000 m radii. (�/©) Stand area heterogeneity; (�/�) % forestarea. Significant correlations under P < 0.05 (rho > 0.3) are enlarged.

Fig. 4. Relative importance of forest attributes according to the posterior inclusion probability (PIP) from Bayesian model averaging testedover all combination of variables with species richness estimates for 2008, 2009, pooled species richness over both years and species turnoveras dependent variables.

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04 K. Wells et al. / Basic and A

bies trees >10 m. In contrast, stand succession stage at thelot level explained most of the variation in species richnessn 2009, when significantly more species were recorded inoung and old timber compared with thicket and pole woodtages.

The species richness estimate pooled over both sam-ling year exhibited strongest correlations with stand areaeterogeneity over 800 m radii and coverage with P.bies trees >10 m similar to species richness estimatesor 2008.

Since most variables occurred only in a small proportionf candidate models out of the full set of 2048 models perependent variable, there was considerable uncertainty inoefficient estimates (see Appendix A, Table 2).

iscussion

Understanding the relationships between environmentalariables and both species occurrence and richness in het-rogeneous and fragmented landscapes is most challenginghen the subjects are mobile organisms like birds, which

xhibit complex patch-occupancy dynamics through dailyovements, and at times through seasonal migration and

early recolonisation of breeding habitats. Inevitably, uncer-ainty in species detection, cross-scale interactions throughxtended forays, and patch-occupancy dynamics are issues ofoncern when searching for reliable judgements from moni-oring data.

Our results emphasise that forest attributes at multiplecales may influence bird species richness in heterogeneousandscapes, with clear differences in environment–specieselationships across years. Interestingly, forest stand hetero-eneity over a distance of 800 m around sampling locationsevealed the strongest relationship with species richness in008, in contrast to local stand attributes which were theore influential factors in 2009. This result is of particular

nterest since stand-level heterogeneity through spatial vari-bility in forest management schemes is crucial to bring theffects of compositional heterogeneity at the landscape levelnto the debate of how forest management and habitat frag-

entation might impact wildlife (Tews et al. 2004; Janssen,ortin, & Hébert 2009). While this result extends the commonndings that habitat heterogeneity, or intermediate distur-ance regimes, enhances species richness on a landscapecale (Chesson 2000), it requires additional investigationecause we found that landscape compositional heterogene-ty affected bird species richness only in 2008 but not in009. Furthermore, our results stem from a single, largelyragmented study region and therefore need to be expanded.

Relatively small forest patches may increase specificolonisation-extinction dynamics, demographic stochastic-

ty, and species turnover regarding species–area relationshipsnd metapopulation dynamics (Hanski et al. 2000; Bouliniert al. 2001). Moreover, such patterns are likely to changeue to gradients in forest management across patches and the

paaL

Ecology 12 (2011) 97–106

roportion of old-growth forest (Schmiegelow & Mönkkönen002; Virkkala & Rajasärkkä 2006), which is scarce in ourtudy region. Perhaps compositional heterogeneity might inome limited way compensate for the decrease in struc-ural components and resource availability in managed foresttands compared with the structure-rich and more hetero-eneous conditions in natural and more species-rich forestsSchulte, Mladenoff, Crow, Merrick, & Cleland 2007).

Mobile species might benefit from landscape composi-ional heterogeneity up to a certain level because they canccess various resources in different habitat types within theirange. Resources, such as the arthropods that serve as preyor the majority of bird species we observed (19 out of 26ere mainly insectivorous), vary in turn locally across for-

st stands and also across years (M. Gossner & W. Weisser,niversity of Jena, unpublished data), possibly impactingird occurrence and assembling patterns on spatio-temporalcales. Species-specific resource utilisation, combined withtolerance of different forest structures and forest strata,

s well as species mobility, influence the occurrence pat-erns of birds (Sekercioglu et al. 2002; Betts, Rodenhouse,illett, Doran, & Holmes 2008; Böhm & Kalko 2009; Zipkin,ewan, & Andrew Royle 2009). To achieve a comprehensive

nd process-oriented understanding of bird species richnessnd turnover patterns requires taking such effects at thepecies-level into account.

Hierarchical multi-species models allow estimates ofpecies richness and turnover in a consistent frameworkDorazio and Royle 2005). This is in contrast to the clas-ical analysis, where species richness is usually corrected fornobserved species but not so similarity and turnover indicesMagurran 2004). It is important to note, however, that varia-ion in species occurrence and detection across years includess much uncertainty as observations used for species richnessstimates from a single year, and hence need to be consideredn species turnover estimates.

We found the first evidence from our two-year samplehat overall species turnover is linked to forest attributes liketand age and succession. We regard our data analysis as pre-iminary and are aware that two confounding effects werehaping species turnovers, namely reduced species richnessnd lower plot occupancies for species present in both yearsn 2009. Our data complement other studies well, and pointo important future research topics since species turnover hasarely been considered in the context of environmental gra-ients at different scales (Buckley & Jetz 2008). Moreover,nly recently has our understanding advanced that composi-ional species turnover can be biased by changes in speciesichness (Baselga 2010). Sufficiently large time series andntegrative modelling approaches are pivotal for disentan-ling the complex dynamics we found in our study. It requiresetailed data on links between fluctuations in regional species

ools and the varying relationships of species with forestttributes, which in turn influence local occupancy dynamicsnd species turnover. As an example, the Common Crossbilloxia curvirostra exhibits highly variable occurrence pat-

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erns as it follows the erratic availability of conifer conesRoyle & Kéry 2007).

In conclusion, sustainable forest management aiming atrotecting bird species richness needs to take local as wells landscape-scale variables into account. Moreover, it needso further consider variable impacts of forest attributes overime. This is particularly important in the context of intensiveorest management on a landscape scale where conditionsor wildlife may vary in nearby and differently managed for-st stands and conditions favourable to species richness inne forest stand may buffer poor conditions in others. Hencegreat deal of scientific study needs to be carried out to

nderstand how and why species respond dynamically toocal- and landscape-scale forest attributes, and how suchynamic interactions can be integrated into consistent multi-pecies predictive models. Species–habitat relationshipsequire analysis of multiple-year timelines that disentangleatural fluctuation from habitat and management effects. Thisill improve conservation and management efforts for theaintenance of current biodiversity levels and at the same

ime encourage sustainable use of forest resources.

cknowledgements

We thank the local authorities ‘Regierungspräsidiumübingen’ and ‘Landratsamt Reutlingen’ for research per-its and the provision of digital forest management maps. We

hank Ernst-Detlef Schulze, Dominik Hessenmöller, Sonjaockel, Ulf Pommer and Andreas Hemp for stimulatingiscussions on defining forest management types and theirontributions to plot selection. We are indebted to Jörg Hailer,alf Lauterbach, and Martin Fellendorf among other staff of

he Biodiversity Exploratories for valuable help in the field.Marc Kéry, Carsten F. Dormann, and Elise F. Zipkin pro-

ided helpful comments on data analysis. This work has beenunded by the German Science Foundation (DFG) Priorityrogram 1374 ‘Exploratories for large-scale and long-termunctional biodiversity research’.

ppendix A. Supplementary data

Supplementary data associated with this arti-le can be found, in the online version, atoi:10.1016/j.baae.2011.01.002.

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