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Agriculture, Ecosystems and Environment 149 (2012) 171– 180

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

jo ur n al homepage: www.elsev ier .com/ lo cate /agee

ffects of shade tree removal on birds in coffee agroecosystems in Chiapas,exico

tacy M. Philpott ∗, Peter Bichierepartment of Environmental Sciences, University of Toledo, 2801 W. Bancroft St., MS 604, Toledo, OH 43606, United States

r t i c l e i n f o

rticle history:eceived 30 September 2010eceived in revised form 27 January 2011ccepted 9 February 2011vailable online 5 March 2011

eywords:viangriculture

a b s t r a c t

Coffee agroecosystems with complex shade canopies provide refuges for biodiversity, and reductions incomplexity cause biodiversity loss. However, no studies directly compare farms before and after a man-agement shift. We surveyed birds before and after a drastic canopy reduction. We compared abundanceand richness of all birds, migrants and residents, and bird guilds, and examined impacts on functionalgroup richness for insect-feeding birds and abundance of two key insectivore groups. Finally, we usedconfidence inference trees to examine which vegetation variables best explained bird abundance andrichness for all birds and different groups. We observed 113 bird species from over 7700 individuals. Sur-prisingly, there were no changes in cumulative bird richness in the cut and uncut areas; however, bird

offea arabicaunctional diversitynsectivorehade management

abundance and mean richness was 3–6 times higher in uncut areas. Abundance and richness of all birds,migrants, residents, and individual guilds was higher in shaded areas, as was functional group richnessof insectivores and abundance of key insectivore species. Canopy cover and canopy depth best predictedbird abundance and richness. Birds prey on arthropods including coffee pests. Most coffee farmers elimi-nate shade trees to increase yields; however, management changes that negatively affect insect-feedingbirds may indirectly affect the coffee crop.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

Agricultural expansion and intensification are two major rea-ons for decline of biodiversity. However, over the past 15–20 years,he benefits of some agricultural systems as refuges for biodiver-ity have often been examined (Perfecto et al., 1996). In particular,haded coffee and cacao agroecosystems have become a majorocus for conservation work due to their vegetatively complexhade canopies that provide needed resources in landscapes devoidf natural forests (Perfecto et al., 1996; Moguel and Toledo, 1999).ne of the major reasons for the explosion in this field was theocumentation that shaded agroforests provide important win-ering habitat for migratory birds (Greenberg et al., 1997a). Sincehe initial work on birds, several have examined the potential forhaded coffee to support arthropods, bats, small mammals, andven amphibians (reviewed in Perfecto et al., 2007). Although thepecific details of loss of bird richness described in individual stud-

es vary, overall patterns strongly support the conclusion that coffeearms with diverse, dense, and thick shade canopies support highiversity of birds, and of forest specialists, and that as vegetation

∗ Corresponding author. Tel.: +1 419 530 2578; fax: +1 419 530 4421.E-mail address: stacy.philpott@utoledo.edu (S.M. Philpott).

167-8809/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.agee.2011.02.015

in the canopy is simplified, bird richness is lost (Philpott et al.,2008). In addition, landscape level characteristics of coffee land-scapes including nearness of forest fragments and abundance ofedge habitats may affect birds (e.g. Tejeda-Cruz and Sutherland,2004). Declines in bird richness in coffee agroecosystems are cor-related with declines in tree richness and density, canopy cover,canopy height and depth, coffee density, lower height of understoryplants, removal of epiphytes, increasing distance from forest frag-ments, and a lack of abundant fruit and nectar resources (Greenberget al., 1997a; Cruz-Angón and Greenberg, 2005; Philpott et al., 2008;Peters et al., 2010).

The effects of management intensification on bird abundanceand richness are important from a conservation standpoint, andalso because birds provide important ecosystem services to agri-cultural systems. Birds are important seed dispersers (Sekercioglu,2006) and also play critical roles as pest control agents within cof-fee agroecosystems (Greenberg et al., 2000; Perfecto et al., 2004;Borkhataria et al., 2006; Kellermann et al., 2008; Van Bael et al.,2008; Johnson et al., 2009; Philpott et al., 2009). Birds reducepopulations of arthropods in both the coffee plants and shade

trees (Greenberg et al., 2000; Philpott et al., 2004) and limit out-breaks of potential pests (Perfecto et al., 2004). In addition, birdsprey on specific coffee pests including the coffee berry borer,Hypothenemus hampei (Borkhataria et al., 2006; Kellermann et al.,

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72 S.M. Philpott, P. Bichier / Agriculture, Eco

008; Johnson et al., 2009), and the coffee leaf miner, Leucopoteraoffella (Borkhataria et al., 2006). Abundance of birds, or of par-icular species may be important for this predatory role (Perfectot al., 2004; Philpott et al., 2009). Likewise, increases in species orunctional richness are important to increasing bird effectivenesss predators within agroforestry systems (Van Bael et al., 2008;hilpott et al., 2009).

Changes in agricultural management and landscape composi-ion impact bird community composition, and may affect functionalichness. Some studies demonstrate significant declines for cer-ain bird guilds with agricultural conversion or intensification.or example, richness of insectivores, frugivores, and nectarivoreseclined in maize and cacao fields in Sulawesi compared withearby forests (Waltert et al., 2004). Landscape factors can also

nfluence particular guilds. For example, frugivores are more com-on in low-intensity agricultural sites near to forest fragments

Luck and Daily, 2003). Larger reviews have summarized that glob-lly, birds found in agricultural habitats are more often generalists,nd are disproportionately frugivores and nectarivores (Tscharntket al., 2008). Large frugivores are lost with conversion from foresto agriculture, but nectarivores, and small to medium sized insec-ivores and frugivores are sometimes more diverse and common ingricultural areas (Tscharntke et al., 2008). Insectivorous birds, inarticular are often negatively affected by forest disturbance, andan be most sensitive to human impacts (Canaday, 1996). Nonethe-ess, insect-feeding birds are among the most abundant in coffeearms (Komar, 2006). Although loss of species may be reflectedn services provided, the loss of functional groups of insectivores

ay more closely track pest control services, in particular. Broadlyefined, a functional group is a grouping of species based on behav-

oral, morphological, physiological, or resource use traits, and canften better predict ecosystem function, compared with speciesichness (Philpott et al., 2009). As species richness declines, ands habitat intensification increases, both the number of functionalroups and resilience within a community may decline (Fischert al., 2007). Moreover, in a meta-analysis of several bird data setsFlynn et al., 2009) documented that functional diversity of birdseclined with a change from natural systems to semi-natural orgricultural habitats, and demonstrated that functional diversityctually declined faster than species diversity.

Despite the large background literature on bird diversity inoffee agroforests, functional roles of birds in limiting potentialnd actual coffee pests, and the impacts of land use change onifferent bird guilds, there is relatively little information avail-ble about which specific characteristics of coffee agroecosystemsrive changes in functional richness of insectivores within coffeegroecosystems. Furthermore, most studies of bird communitiesn agroecosystems rely on static differences among farms to exam-ne the effects of the management on birds, rather than followingn actual intensification process in action. Here, we take advan-age of a large-scale manipulation of the shade tree canopy toxamine the changes in the bird community after a large man-gement shift and compare this to data available about the birdommunity before this management shift. Specifically, we inves-igated the effects of a dramatic shade tree thinning and pruningn the abundance, species richness, and composition of birds inoffee agroecosystems. We examined the impacts of the manage-ent shift on all birds, migrant and resident birds, and different

ird guilds (nectarivores, granivores, insectivores, omnivores, andrugivores). We also examined the impacts on functional rich-ess of insect-feeding birds (both insectivores and omnivores)nd abundance of potentially effective insectivores. We then used

ermutation trees to identify those vegetation and site charac-eristics that most strongly related to abundance and richness ofll birds, and for groups of birds potentially important to pest-ontrol.

s and Environment 149 (2012) 171– 180

2. Methods

2.1. Study site

We worked in an organic, shaded coffee farm in the Soconuscoregion of Chiapas, Mexico, Finca Irlanda (15◦20′N, 90◦20′W). FincaIrlanda was first certified organic (and biodynamic) coffee farmin the world (Giovannucci, 2001). For the past 50 years, the farmowners have promoted ecologically friendly farming techniques,and in the last decade received shade-certification from Rainfor-est Alliance and the Smithsonian’s Bird-Friendly program. Thereare >50 scientific publications from studies at the farm, thus thereis a great deal of background information on the flora and fauna.Finca Irlanda contains ∼270 ha of coffee plus two forest fragments,is located between 950 and 1150 m elevation, and receives ca.4500 mm of rain annually. Prior to the management shift therewere ∼250 shade trees ha−1 that provided between 69 and 90%canopy cover. Trees were spaced approximately 5–8 m apart andwere uniformly distributed, except along roads where they wereclumped (Vandermeer et al., 2008). There were at least 100 shadetree species on the farm, with a canopy dominated by Inga spp.

During April and May of 2007 and 2008, the farm owners carriedout a large management shift during which time a large fraction oftrees within certain areas of the farm were entirely cut or severelypruned. For example, during 2007, more than 3000 of 11,000 treeswithin a marked 45-hectare plot in the farm were removed (Per-fecto et al., unpublished); many more trees were pruned. The statedgoal of the farmers was to reduce the canopy cover from 75% to50% in affected areas of the farm. We took advantage of the dras-tic management shift, and ample data on the vegetation and birdcommunity before the management shift to examine the impactson the bird community. We examined changes in bird richness andabundance, as well as several vegetation characteristics before themanagement shift (2001–2002) and after the management shift(2007–2009) to examine the direct and immediate effects of shadetree thinning and pruning on the birds. After the management shift,the vegetation in shaded and cut areas was visibly different (Fig. 1).We acknowledge that we deviate from a traditional study designby working in a single farm; however, we did not want to passup the unique research opportunity provided by the farmer-drivenmanagement shift.

2.2. Bird and vegetation surveys

We established plots throughout the farm to characterize thevegetation and bird communities. Each plot consisted of a 25 mradius plot, separated from other plots by at least 100 m. We sam-pled birds during three dry seasons and three wet seasons, but thenumber of points sampled, and the status of each point (shadedor cut) differed during each season (Table 1). Before the manage-ment shift, we established 64 point locations; all were sampled inthe wet season, and some of these were sampled during the dryseason. After the management shift, the same 132 point locationswere sampled during each period; however, the number of shadedand cut points differed due to continued management changes.The 64 locations sampled in 2001–2002 were in the same generalarea of the farm, and close to 64 of the locations sampled during2007–2009, but were not exact matches. The additional 68 loca-tions sampled in 2007–2009 were in areas of the farm that were notsampled prior to the management shift. Thus, this study was nota completely controlled before–after experiment, and it is possiblethere could have been pre-existing differences among areas that

were later cut and left uncut For clarity, we refer to points sampledbefore the management shift as shade before, and to points sam-pled after the management shift as shade after or cut, dependingon their shade condition.

S.M. Philpott, P. Bichier / Agriculture, Ecosystem

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ig. 1. Photographs of the study site, Finca Irlanda, showing cut (a) and shaded (b)reas of the farm in February 2008, after the management shift.

We collected vegetation data for all plots and sample periodsxcept for the wet season of 2002 and for one forgotten plot in thery season of 2009 (Table 1). Both before and after the managementhift, we collected data on canopy cover, tree species richness, treeeight, tree density, percent coffee cover and epiphyte density. Weuantified epiphyte abundance within plots on a scale from 0 to 4 ofercent of tree trunk surface area covered with epiphytes (0 = <1%,

= 1–25%, 2 = 26–50%, 3 = 51–75%, 4 = > 75% covered). Coffee coveras estimated as the fraction of the plot covered by coffee foliage.t five points per plot (at the circle center and 10 m to N, S, E, and W),e measured canopy cover with a convex spherical densitometer.

e estimated tree heights (maximum, minimum, and mean), and

ampled vegetation >18 m with a range finder. Canopy depth wasalculated by subtracting minimum from maximum tree height forach plot. Tree identifications were made in the field and unknown

able 1ird and vegetation sample points in a coffee agroecosystem before and after aanagement shift.

Sample date Season Timing Habitat No. birdpoints

No. vegetationpoints

December-01 Dry Before Shade 48 48June-02 Wet Before Shade 64 0July-07 Wet After Shade 66 66July-07 Wet After Cut 66 66February-08 Dry After Shade 66 66February-08 Dry After Cut 66 66July-08 Wet After Shade 53 53July-08 Wet After Cut 79 79February-09 Dry After Shade 48 48February-09 Dry After Cut 84 83

Total points 640 575

s and Environment 149 (2012) 171– 180 173

trees were given a unique morphospecies name. After the man-agement shift, we also collected data on coffee height, and groundcover provided by dead vegetation, herbs and leaf litter, and bareground. We estimated mean coffee height per plot. Ground cover(e.g. dead vegetation, bare ground, leaf litter, and herbaceous veg-etation) in 25 m radius circles was estimated with visual estimatesat 5% intervals. We created a vegetation complexity index (VCI) tosummarize farm management strategy (Philpott et al., 2009). TheVCI is an index that can be used to assess overall vegetation com-plexity of a site by equally weighting and taking mean values acrossseveral vegetation variables (Mas and Dietsch, 2003; Philpott et al.,2009). For the VCI computed here, we examined values for canopycover, epiphyte score, mean tree height, number of trees, tree rich-ness, coffee cover, bare ground cover, and percent of trees in thegenus Inga for each plot. Data values for variables measured duringeach field season were converted from a scale of 0 (representingsimple vegetation) to 1 (representing complex vegetation). To con-vert values of most variables (canopy cover, epiphyte score, treeheight, number of trees, tree richness) to a scale from 0 to 1, wedivided values recorded for a given plot by the highest recordedvalue for that variable in any plot. For coffee cover, bare groundcover, and percent of trees in the genus Inga (all variables associatedwith higher management intensity and less complex vegetation)we divided values by the highest recorded value in any plot andsubtracted the product from one. We then took the mean of alleight converted variables to yield the VCI.

We surveyed birds by sight and sound with 10-min point countsin each of the plots (Hutto et al., 1986; Petit et al., 1994). We sampledplots during both the wet and dry seasons in order to compare theeffects on all birds, migrants, and tropical resident birds.

2.3. Data analysis

We first compared vegetation characteristics in shade before,shade after, and cut areas. First, we calculated mean values fortree height and for canopy cover for each plot. We compared meanvalues for variables measured during each sample period (canopycover, coffee cover, bare ground, epiphytes, tree height, tree num-ber, tree richness, percent Inga) and for ground cover variables(dead vegetation, leaf litter, herbaceous vegetation) with two sep-arate multivariate analyses of variance (MANOVA) followed byANOVA and Tukey’s test to compare individual factors in shadebefore, shade after, and cut areas. We examined differences in cof-fee height and the vegetation complexity index (VCI) with ANVOA.Values for canopy cover, coffee cover, percent Inga, and groundcover variables were arcsine square root transformed, and coffeeheight, tree height, number of trees, and tree richness were naturallog (+1) transformed to meet conditions of normality.

We characterized the bird community according to migra-tory status (migrant and resident) and feeding guild (frugivore,granivore, insectivore, nectarivore, and omnivore) using standardsources (Ehrlich et al., 1988; Stiles and Skutch, 1990; Stotz et al.,1996). To compare bird richness in shade before, shade after, andcut areas, we generated sample-based rarefaction curves, scaledto the number of individuals (MaoTao estimates) with EstimateS8.0 (Colwell, 2005). We statistically compared cumulative, rarefiedrichness in different areas for all birds, migrant and resident birds,and for birds in different feeding guilds by comparing the overlapin 95% confidence intervals. We also examined the mean speciesrichness and abundance of all birds in individual points with anal-ysis of variance (ANOVA) and for mean richness and abundanceof migrants and residents with multivariate ANOVA. We followed

MANOVAs with individual ANOVAs and Tukey’s tests to examinedifferences between specific groups and management areas. Datafor bird abundance and richness were natural log (+1) transformedto meet conditions of normality. Repeated measures ANOVA was

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74 S.M. Philpott, P. Bichier / Agriculture, Eco

ot used for analysis because point locations before and after theanagement shift were not complete matches.We classified all insectivores and omnivores into functional

roups based on body size (tiny, small, medium, large, and extraarge), foraging strata (understory, canopy, both), diet (strict insec-ivores, insectivores, omnivores), and foraging strategy (foliageleaner, bark gleaner, ground gleaner, hover and glean, hawks, andultiple strategies). Insectivores are those birds that entirely or

eed mainly on insects, and were described in the texts as birds thata) obtain less than 25–30% of their diet from non-insect items, (b)eed mostly or mainly insects, or (c) feed on insects and few berriesr seeds; all other sometime insectivorous birds were classified asmnivores (Philpott et al., 2009). All other categories were classi-ed exactly as in Philpott et al. (2009), and where birds were not

ncluded in that database, we went to the original sources to com-lement the data. We summed the number of functional groupsresent per point, and compared mean values across managementypes with ANOVA. We examined the abundance of three birdpecies and functional groups previously highlighted as importantor pest control services (Philpott et al., 2009). We compared thebundance of small, strict insectivore, understory foraging, foliageleaning species, and of tiny, omnivore, both strata, foliage glean-ng birds, and of the Tennessee Warbler in shade before, shade afternd cut areas with ANOVA.

We used confidence inference trees to examine the relativemportance of different vegetation and site variables in predictinghe variation in bird richness and abundance (Strasser and Weber,999; Hothorn et al., 2006; Jha and Vandermeer, 2010). Permu-ation trees, such as confidence inference trees, are often used toxamine ecological patterns, and to do so, split data for responseariables into homogeneous groups using explanatory variablesDe’ath and Fabricius, 2000; Olden et al., 2008). Generally, permu-ation trees are advantageous over multivariate regression becausehey have the ability to handle missing data, are easy to inter-ret, and allow explaining variation in a single response variabley one or more explanatory variables that may interact in a hier-rchical fashion (De’ath and Fabricius, 2000; Olden et al., 2008).e created confidence inference trees using a binary recursive

ata-partitioning algorithm available in the package “party” in RR Development Core Team, 2008). In contrast to other packagese.g. “rpart” and “randomforest”) that are somewhat biased towardselecting variables with multiple splitting points, confidence infer-nce trees require significant differences in the data to partitiont, thereby lessening variable selection bias (Hothorn et al., 2006;trobl et al., 2009; Thompson and Spies, 2009). Additionally, con-dence inference trees allow the user to set the minimum p-valueecessary for data splitting, thereby eliminating the problems asso-iated with over fitting and tree pruning (Thompson and Spies,009). The ‘party’ algorithm first examines whether predictor vari-bles are independent of each other and of the response variable.he package then selects the predictor variable with the strongestelationship to the response variable, and assigns the relationship a-value. The data are then split into two groups of data or nodes thatach subsequently compared to the predictor variables. The pro-ram will continue to split or partition the data into nodes basedn significant relationships between the predictor and responseariables significant to the assigned p-value.

We included the following factors as potential explanatory vari-bles (canopy cover, coffee cover, coffee height, number of treendividuals, number of tree species, canopy depth, percent of treesn the genus Inga, and percent ground covered by dead vegetation,eaf litter, and herbaceous vegetation). As response variables, we

ncluded bird richness and abundance, richness and abundance ofmnivores and insectivores, functional richness of birds, and abun-ance of bird groups important to predatory function. We includedata for individual points as replicates, and used total bird richness

s and Environment 149 (2012) 171– 180

and abundance per point for analysis. All variables included weretransformed to meet conditions of normality as explained for veg-etation and bird analyses. We used univariate tests, and becauseof the high number of tests performed, we selected a conserva-tive critical value (p < 0.001) to reduce Type I error (Thompson andSpies, 2009). Although we could have investigated the relationshipsbetween the site characteristics and other bird groups, we limitedthe analysis to all birds, and for those birds associated with pest-control services within the coffee agroecosystem. Data from thewet season of 2002 were not included, as vegetation data were nottaken during that sample period.

3. Results

3.1. Vegetation

As expected, there were many significant changes in the vege-tation of the farm before and after the management shift (Table 2).There were several differences in vegetation characteristics of bothground cover (MANOVA, F4, 522 = 31.25, p < 0.001) and for othervegetation factors measured (MANOVA, F18, 1130 = 80.03, p < 0.001).There was less than half as much canopy cover, many fewer epi-phytes, twenty percent fewer trees, and fewer tree species in cutareas than in shade after area (Table 2). There was more dead veg-etation but less leaf litter in the cut areas compared with shadeafter areas (Table 2). Coffee plants were on average shorter andcoffee densities were lower in cut than in shade after areas, but itis unlikely that these changes were brought about by the manage-ment shift. Thus, the management shift resulted in lower vegetationcomplexity (measured as the VCI) in the cut area than in shadeafter area (Table 2). Interestingly, vegetation complexity in thecut area did not differ from vegetation in 2001–2001 (e.g. shadebefore). Between 2001 and 2007 (prior to cutting), farm vegetationgrew more complex resulting in higher canopy cover, taller trees,more tree species, less dominance by Inga spp. trees, and less bareground in shade after than in shade before (Table 2). The only majordecrease in vegetation complexity in shade after (compared withshade before) was a dramatic loss of epiphytes (Table 2). Thus, veg-etation in the farm before the management shift was only slightlymore complex than in cut areas after the management shift.

3.2. Bird abundance and bird richness

Overall, 7718 bird individuals from 113 species were seen orheard across all sample periods. We observed 67 bird species (1022individuals) sampling before the management shift, and observed101 species (4821 individuals) in shade after and 82 species (1875individuals) in cut areas. Before the management shift, the mostabundant bird species were the Tennessee Warbler (Vermivoraperegrina) (206 or 20.2% of individuals), the Red-legged Honey-creeper (Cyanerpes cyaneus) (124 or 12.1%), and the Yellow-greenVireo (Vireo flavoviridis) (78 or 7.6%). The most common species inthe shade after areas were the Red-legged Honeycreeper (1194 or24.7%), the Clay-colored Thrush (Turdus grayi) (433 or 8.9%), andthe Yellow-winged Tanager (Thraupis abbas) (183 or 3.8%) and themost common species in the cut areas were the Red-legged Hon-eycreeper (369 or 19.6%), the Rufous-Capped Warbler (Basileuterusrufifrons) (155 of 8.3%) and the Clay-colored Thrush (136 or 7.2%).

There were no differences in cumulative richness before or afterthe management shift, but abundance and mean richness of birdsand most bird groups per plot was higher in shaded than in cut

areas. Based on accumulation curves generated with EstimateS andassociated 95% confidence intervals, cumulative, rarefied speciesrichness did not differ for all birds, for migratory birds, or forresidents (Table 3). Similarly, there were no differences between

S.M. Philpott, P. Bichier / Agriculture, Ecosystems and Environment 149 (2012) 171– 180 175

Table 2Vegetation characteristics of the coffee farm before the management shift, and shaded and cut areas after the management shift.a

Shade before Shade after Cut df F p

Canopy cover (%) 36.67 ± 1.96b 43.44 ± 0.64a 13.47 ± 0.54c 2, 572 563.452 <0.001Coffee cover (%) 73.62 ± 2.85a 71.94 ± 0.79a,b 67.19 ± 0.85b 2, 572 10.286 <0.001Epiphyte rank 0.79 ± 0.06a 0.42 ± 0.33b 0.03 ± 0.01c 2, 572 76.389 <0.001Tree height (m) 8.23 ± 0.17b 9.51 ± 0.11a 6.85 ± 0.07c 2, 572 238.339 <0.001Canopy depth (m) 9.82 ± 0.51c 18.0 ± 0.33a 12.36 ± 0.23b 155.343No. trees 31.56 ± 1.07c 50.64 ± 0.89a 39.03 ± 0.61b 2, 572 99.311 <0.001No. tree species 8.27 ± 0.34c 15.27 ± 0.27a 12.11 ± 0.18b 2, 572 116.888 <0.001Inga spp. trees (%) 53.24 ± 2.24c 67.5 ± 0.87b 73.5 ± 0.75a 2, 572 21.244 0.001VCIb 0.37 ± 0.01b 0.48 ± 0.01a 0.37 ± 0.01b 2, 572 306.475 <0.001Ground coverDead vegetation (%) na 6.81 ± 0.55b 11.16 ± 0.32a 1, 525 26.22 <0.001Bare ground (%) 14.23 ± 3.79a 4.79 ± 0.40b 9.1 ± 0.51a 2, 572 19.652 <0.001Leaf litter (%) na 29.41 ± 1.34a 21.33 ± 0.79b 1, 525 23.44 <0.001Herbaceous veg. (%) na 59.29 ± 1.78 58.24 ± 1.49 1, 525 0.442 0.507Coffee height (m) na 2.13 ± 0.02 1.96 ± 0.01 1, 525 62.813 <0.001

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a Statistical results are from ANOVA followed by Tukey’s tests to compare differeor individual vegetation characteristics.

b Vegetation complexity index where 1 shows complex vegetation and 0 shows s

he cumulative, rarefied richness of birds during the wet and dry

easons (Table 3). However, at the plot level, there were very sig-ificant differences in bird abundance and richness (Fig. 2). Birdbundance was more than twice as high in shaded after (20.7 + 0.7ndividuals per plot) compared with shade before (9.1 + 0.5) and

ig. 2. Mean abundance (a and b) and species richness (c and d) per point for birds sogroecosystem before and shaded and cut areas after the management shift. Letters show

between treatments. Small letters show differences (p < 0.05) between treatments

vegetation.

cut areas (6.3 + 0.3) (F2, 638 = 267.8, p < 0.001; Tukey’s test for each

pair, p < 0.001). Likewise, bird richness was twice as high in shadeareas after (9.1 + 0.3 species per plot) than in shaded areas before(4.9 + 0.2) or in cut areas (3.9 + 0.1) (F2, 638 = 206.9, p < 0.001; Tukey’stest for each pair, p < 0.01). Both migrant and resident birds were

rted by migratory status (a and c) and feeding guild (b and d) in a shaded coffee significant differences (p < 0.05) for particular factors within a given bird group.

176 S.M. Philpott, P. Bichier / Agriculture, Ecosystems and Environment 149 (2012) 171– 180

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ig. 3. Mean insectivore functional richness per plot (a) and abundance of functionaefore and shaded and cut areas after the management shift. Letters show significa

uch more abundant (MANOVA, F2, 4 = 93.2, p < 0.001) and speciesich (F2, 4 = 76.8, p < 0.001) in shade after area compared with the cutrea. There were two times more resident species and three timesore resident individuals in shade after than in cut areas (Fig. 2a

nd c), and abundance and richness of migrants was twice as highn shade after than in cut areas (Fig. 2a and c).

.3. Abundance and richness of different guilds

According to species accumulation curves, and cumulative, rar-fied richness values, there were no differences in species richnessf any guilds across the habitats (Table 3). Richness for insectivoresnd granivores tended to be higher in shaded areas after the man-gement shift than shade before or in cut areas and richness forectarivores was slightly higher in cut areas than in the shadedreas before or after. But these differences were not significant. Athe plot level, however, there were large differences in richness ofach bird guild examined (MANOVA, F5, 10 = 36.5, p < 0.001). Meanichness of all groups (frugivores, granivores, insectivores, nectari-ores, and omnivores) was roughly twice as high in one or morehaded habitat compared with the cut area (F2, 638 > 4.5, p < 0.001)Fig. 2d). Similarly, bird abundance for different guilds depended on

anagement area (MANOVA, F = 44.8, p < 0.001). There were

5, 10arge differences in frugivore, granivore, insectivore, nectarivore,nd omnivore abundance per plot (F2, 638 > 7.5, p < 0.001). For allroups, abundance was two to three times greater in shaded areas

able 3arefied bird species richness for all birds and for different bird groups before andfter the management shift.a

Shade before Shade after Cut

All birds 67.0 ± 6.1 70.6 ± 10.1 71.1 ± 7.7Dry season 49.0 ± 6.1 56.1 ± 9.0 53.8 ± 5.1Wet season 43.0 ± 6.5 41.6 ± 8.4 42.2 ± 7.5Migrants 18.0 ± 2.7 24.0 ± 5.6 24.9 ± 4.2Residents 49.0 ± 5.6 46.5 ± 8.5 47.5 ± 6.7Insectivores 31.0 ± 3.5 38.5 ± 7.1 34.9 ± 4.7Omnivores 17.0 ± 2.0 15.3 ± 4.4 16.8 ± 3.3Granivores 4.0 ± 0 4.5 ± 1.4 4.6 ± 0.5Nectarivores 8.0 ± 3.2 4.9 ± 2.3 7.3 ± 3.7Frugivores 6.0 ± 3.6 6.7 ± 3.3 6.9 ± 1.8

a Numbers show cumulative species richness ± 95% confidence intervals (CI) forichness rarefied to the number of individuals in the area with the lowest abundance.here were no differences in rarefied richness between sites according to 95% CI.

ortant functional bird groups and bird species (b) in a shaded coffee agroecosystemerences (p < 0.05) within a given bird group.

after compared with cut areas (Fig. 2b). Insectivore and nectarivoreabundance was higher in shade before compared with cut areas;abundance of frugivores, granivores, and omnivores was similar inshaded areas before and cut areas (Fig. 2b).

3.4. Functional richness and abundance of functionally importantbird species and groups

The change in management had strong impacts on both thenumber of insectivore and omnivore functional groups present atthe plot level and on the abundance of bird species potentiallyimportant to predatory function. Rarefied functional group richnesswas not different among habitat types with 36 (±4.4, 95% CI) groupsin shade before, 39.3 (±4.6) groups in shade after and 37.2 (±4.3)groups in cut. However, there were more than twice as many func-tional groups per plot in the shaded areas after than in the shadebefore or cut areas, and significantly more functional groups in theshade before than in the cut area (Fig. 3a, F2, 637 = 163.7, p < 0.001).The two important functional groups were each represented byone or two species: the small, strict insectivore, understory forager,foliage gleaning species was the Spot-Breasted Wren (Thryothorusmaculipectus) and the tiny, omnivore, both strata, foliage gleanerspecies were the Northern Beardless-Tyrannulet (Camptostomaimberbe) and the Tennessee Warbler. Spot-Breasted Wren abun-dance was three times as high in the shade after than in the cut, butthere were no differences in the shade before and cut areas (Fig. 3b,F2, 637 = 9.8, p < 0.001). The tiny, omnivore, both strata, foliagegleaners, in contrast, were eight times more abundant before themanagement shift than in the cut areas, and twice as abundant inthe shade after than in the cut (Fig. 3b, F2, 637 = 43.7, p < 0.001). Simi-larly, the Tennessee Warbler was much more abundant in the shadebefore with more than five times as many individuals than in theshade after or cut areas (Fig. 3b, F2, 637 = 53.2, p < 0.001).

3.5. Vegetation drivers of richness and abundance patterns

Several vegetation and site characteristics were predictive ofbird abundance and richness and abundance and richness of certaingroups of birds. Higher canopy cover and canopy depth were pre-

dictive of higher bird abundance and richness, and higher canopieswere also associated with higher bird abundance (Fig. 4a andb). Back transformed values show cutoff points for canopy coveraround (26%), for canopy depth near (15 m) and for mean tree

S.M. Philpott, P. Bichier / Agriculture, Ecosystem

Fpa

haectctacidwfcwccBu1dspwc

ig. 4. Confidence inference tree showing the vegetation variables that significantlyredicted bird abundance (LN + 1) (a) and bird species richness (LN + 1) (b) in a coffeegroecosystem.

eight at 7.8 m for predicting the highest levels of bird abundance,nd 26% cover and 15 m and then 22.9 m for canopy depth for high-st bird richness. Insectivore abundance was best predicted by highanopy cover and high cover provided by leaf litter and dead vege-ation (Fig. 5a), whereas insectivore richness was predicted only byanopy depth (Fig. 5b). Canopy cover and canopy depth were thewo factors predictive of omnivore richness and abundance (Fig. 5cnd d). Insectivore abundance was highest with above 26% canopyover, 15% leaf litter cover, and 15% dead vegetation cover, andnsectivore richness was higher with greater than 13.8 m canopyepth and highest over 22.5 m. Omnivore abundance and richnessas highest over 34% canopy cover and 14.7 m canopy depth. The

unctional richness of insect-feeding birds was best explained byanopy depth and canopy cover. Functional richness was highesthere the canopy exceeded 14.7 m thick, and over 34% canopy

over. The abundance of the tiny, omnivorous, both understory andanopy foraging foliage gleaners (Tennessee Warbler and Northerneardless-Tyrannulet) was high under two combinations of val-es: either with canopy cover over 18% and tree richness under0 species, or for high canopy cover, richness over 10 species, andead vegetation cover great than 8% (Fig. 6b). Finally, the number ofmall, strict insectivore, understory foraging foliage gleaners was

redicted only by canopy depth and was higher where canopiesere more than 18 m thick (p < 0.001, low cover group, n = 471, high

over group, n = 104).

s and Environment 149 (2012) 171– 180 177

4. Discussion

We found dramatic plot-level decreases in the richness andabundance of birds of different migratory status and guild in thisstudy, but no differences in cumulative richness across the farm.This was somewhat surprising given the large number of stud-ies that have documented declines in bird species richness withmanagement intensification (reviewed in Komar, 2006; Philpottet al., 2008). Yet, many studies have found only small differ-ences in cumulative richness of birds in coffee habitats varying incanopy characteristics (Greenberg et al., 1997b; Cruz-Angón andGreenberg, 2005) or have measured bird richness with species-sample curves (Thiollay, 1995; Tejeda-Cruz and Sutherland, 2004)or mean estimated richness (Waltert et al., 2004, 2005; Perfectoet al., 2003) making the results somewhat difficult to directlycompare. Our results finding higher bird abundance in habitatswith more complex shade were largely consistent with previousstudies (Greenberg et al., 1997b; Johnson, 2000; Cruz-Angón andGreenberg, 2005; Philpott et al., 2008). Further, many of the samefactors (e.g. canopy cover, canopy depth, tree richness) correlatedwith bird richness and abundance in our data set also predictedbird richness in other regions and studies (e.g. Parrish and Petit,1996; Greenberg et al., 1997b; Johnson, 2000; Philpott et al., 2008).Other factors, such as an Inga-dominated canopy or the changesin epiphyte presence or abundance, were not significant predic-tors of bird abundance and richness for our data, even thoughthese factors influence all birds and certain bird guilds elsewhere(Greenberg et al., 1997b; Cruz-Angón and Greenberg, 2005; butsee Jones et al., 2002). It is noteworthy, however, that epiphytesare much less abundant in the Soconusco region of Chiapas thanin other areas of Mexico and that in addition to trees, epiphytesprovide canopy cover within agroforests (Cruz-Angón et al., 2008).Although the epiphyte measure in this study did not influencebirds specifically, part of the influences of canopy cover may beattributable to changes in cover provided by epiphytes. One impor-tant caveat to our data is that we only examined the effects of sucha management shift in one coffee farm, thus there is no spatialreplication.

Several studies have also examined the impacts of coffeemanagement on different guilds of birds. Most studies focus onabundance of different guilds, or their relative abundance, andnot species richness within each guild. Generally, there are moreomnivores, frugivores, nectarivores, and fewer insectivores in cof-fee habitats compared with nearby forests (Komar, 2006). Butresults differ somewhat among studies and agroforests exam-ined. For example, in one study, abundance of both insectivoresand frugivores was lower in shaded monocultures than in rusticcoffee and forested habitats, but did not differ among the latter(Tejeda-Cruz and Sutherland, 2004). In contrast to our results, theyalso observed increases in abundance of granivores and omni-vores with increasing disturbance. Their study sites, however,were arranged along an elevation gradient with degree of distur-bance negatively correlated with elevation, and our sites were allwithin a very similar elevation range. More generally, agroforestsin Sumatra harbor fewer frugivores, specialists, and large insecti-vores, and more omnivores, nectarivores, and granivores comparedwith native forests (Thiollay, 1995). In agricultural landscapes nearthe Guinea–Congolian rainforest, frugivorous and omnivorous birdspecies richness did not differ between annual crop, agroforest, andforest habitats, granivore and nectarivore richness was higher inagricultural land-use types, and insectivore richness declined inagroecosystems compared with forests (Waltert et al., 2005). In

contrast, we found consistent differences in abundance and speciesrichness of all bird guilds with lower numbers in the cut areasthan in the shaded areas. Thus many of the specific details of howguild structure, richness, and abundance changes may depend on

178 S.M. Philpott, P. Bichier / Agriculture, Ecosystems and Environment 149 (2012) 171– 180

F predio

sw

safi2bwFhabtoSitelmcteraofpc

ig. 5. Confidence inference tree showing vegetation variables that significantlymnivore abundance (LN + 1) (c), and omnivore species richness (LN + 1) (d).

tructural factors of the agroecosystem, or the landscape context inhich the agroforests are embedded.

Certainly other local and regional characteristics of coffee land-capes that we did not measure may affect bird richness andbundance. At the local level, high fruit abundance within coffeearms may increase frugivore and omnivore abundance, and alsoncrease the foraging activity of fruit-feeding birds (Carlo et al.,004; Peters et al., 2010). Predation risk might be one reason thatirds are more abundant in more vegetatively complex agroforestsith a higher density of trees and coffee plants (Johnson, 2000).

urthermore, reduced mean tree height, presence of certain micro-abitats, lower variety of food resources, heavy hunting pressure,nd competition by other birds or mammals may be important toirds in agroforests (Thiollay, 1995). At the landscape level, distanceo nearest large or small fragments, habitat connectivity, amountf forest in the landscape, and thickness of edges may affect birds.pecifically within agricultural landscapes, species richness of birdsncreases with nearness to forests (Estrada et al., 1997), or is main-ained near to forest fragments or with thick habitat edges (Hughest al., 2002). Thus landscape context can strongly mediate localevel responses such that effects of a manipulation at the farm scale

ight be more (or less) severe in a less forested landscape. Habitatonnectivity may be important for increasing dispersal distanceshrough the agricultural matrix (Castellón and Sieving, 2006). How-ver, habitat connectivity may not always be important for birdichness in coffee landscapes, especially where most agroforestsre relatively well connected (Jones et al., 2002). One important

bservation that was not explicitly examined in our study was thator the same period during which Finca Irlanda intensified theirroduction, several nearby farms underwent intensification pro-esses in certain areas (Pers. obsv.). Thus the overall matrix quality

cted insectivore abundance (LN + 1) (a), insectivore species richness (LN + 1) (b),

may have decreased with potentially important impacts for birdforaging and survival in the region. In the Amazon forest fragmentsproject, bird richness declined with time since fragmentation, andmore so in small fragments; however, during the first few yearsafter fragmentation, a larger number of bird species were seen evenin small fragments, likely due to a temporary influx of birds fromrecently disturbed areas (Ferraz et al., 2003). As the entire valleywas experiencing coffee intensification the remaining shaded areasof Finca Irlanda, a less-disturbed habitat, may have been similarlyexperiencing higher bird abundance. Some data from other stud-ies support this hypothesis. For example, in Hispaniola, a coffeefarm of relatively poor vegetation quality supported a relativelyhigh abundance and richness of birds because it was surroundedby a poor-quality matrix (Wunderle and Latta, 1998). Likewise, inPuerto Rico, where coffee landscapes are somewhat dominated byabandoned plantations and second-growth forests, bird abundancein coffee farms, some rich in species of fruiting trees, was low.Thus patterns of bird abundance at the farm level may be betterexplained by landscape effects than by the local level vegetationcharacteristics of individual plots.

A particularly novel aspect of this study was the analysis of theeffects of a canopy management shift on functional groups of insec-tivores, and on specific bird species or groups likely important forinsectivory. Further, we documented which aspects of the canopylikely influence functional groups richness. Very few studies haveexamined factors that influence abundance and richness of insecti-vores in coffee agroecosystems, or which vegetation characteristics

correlate with increases in functional richness, or abundance ofimportant insectivores. Foliage-gleaners are generally positivelycorrelated with abundance of arthropods and increases in shadetree richness and density of crop structure (Johnson, 2000), two

S.M. Philpott, P. Bichier / Agriculture, Ecosystem

Fig. 6. Confidence inference tree showing vegetation variables that significantlypredicted number of insectivore and omnivore functional groups (LN + 1) (a) andtgp

frmeevspTditnai2Ti

he abundance of the tiny, omnivorous, both canopy and understory foraging foliageleaners (LN + 1) (b). The latter is one of two functional groups singled out for aotential sampling effect with respect to arthropod removal.

actors that may enhance arthropod richness and reduce predationisk. Diversity and abundance of important predators of coffee pestsay decline with increasing distance to forest patches (Kellermann

t al., 2008), and such loses of diversity are correlated with lessffective arthropod removal (Van Bael et al., 2008). Clearly, indi-idual species can vary widely in their contribution to ecosystemervices such that the loss of only a single or few species can dis-roportionately affect the system and services derived from it.his so-called sampling effect states that the mechanism drivingiversity-ecosystem function impacts is via presence of such single

mportant species (Hooper et al., 2005). In coffee agroecosystems,iny, omnivorous, both strata, foliage gleaners, represented mostotably here by the Tennessee Warbler, have been singled outs important abundant migratory insectivores whose abundance

s correlated with higher arthropod removal (Greenberg et al.,000; Philpott et al., 2009). Analyses of stomach contents from 42ennessee Warblers captured in the coffee layer of Finca Irlandandicate these birds are indeed frequently preying on arthropods

s and Environment 149 (2012) 171– 180 179

(including herbivores such as lepidopteran larvae and seed preda-tors like scolytids and curculionids) (T. Dietsch, unpublished data).Furthermore, foraging data from the same farm show TennesseeWarblers forage in the coffee layer at least 10% of the time, andgiven their abundance thus could have important impacts on cof-fee arthropods (T. Dietsch, unpublished data). The abundance ofindividuals in this functional group dropped strongly after the man-agement shift (in both cut and shade after) where other insectivoreswere not largely affected. Thus understanding the specific factorsresulting in large changes of Tennessee Warbler abundance maybe particularly crucial for understanding provisioning of pest con-trol services in coffee agroecosystems. The vegetation variablesthat best predicted its abundance were higher canopy cover, lowertree species richness, and more ground covered by dead vegeta-tion. Other studies, however, have found that Tennessee Warblersare more abundant in Inga-dominated farms compared with farmsdominated by other canopy trees (e.g. Gliricidia), perhaps due toa higher abundance of fruit or nectar resources, or potentially anincrease in insects associated with fruits and flowers (Greenberget al., 1997a). Here, relative abundance of Inga spp. trees did not pre-dict the abundance of Tennessee Warblers, however other speciesof trees were likely flowering and fruiting within the plantations.We did not measure reproductive phenology of trees within thefarms, nor what flowering and fruiting resources were availableduring surveys. What seems more likely is differences in TennesseeWarbler abundance may reflect differences in tree reproductivephenology during those time periods. Observations before themanagement shift were conducted in December, and observationsduring the dry season after the shift were conducted in February.It is possible, and even likely that a greater number of trees areflowering early in the dry season, thereby dramatically increasingTennessee Warbler abundance.

In sum, we did not observe impacts of the management shifton the cumulative species richness of birds, of migrants, residents,or birds of different feeding guilds, nor were there differences infunctional richness of insectivores across larger scales. However,we did find highly significant decreases in abundance and richnessof all groups of birds, including of functional richness of insect-feeding birds, and of some birds likely important for pest controlfunctions at the plot level. Thus, our results show that bird speciesdensity was uniformly higher across the shaded areas of the farm;per plot richness was greater, but cumulative richness did not differin shaded and cut areas of the farm. Although there are a dramaticnumber of new studies examining relationships between predatordiversity and ecosystem services, few have examined the impor-tance of distribution of predators at the local or more regionalscales. The changes in abundance of insectivores and omnivoreswith management shifts at the local scale will likely be importantfor pest control services, as will changes in the number of functionalgroups observed with this drastic change in coffee management.Farmers generally intensify production to increase yields, and thiswas certainly the case for the owners of Finca Irlanda (Pers. comm.).However the decreases in insect-feeding birds within the farm maylikely interact in a complex manner with other changes resultingfrom the management shift.

Acknowledgements

We thank W. Peters and Finca Irlanda for allowing us to con-duct this research on their farm. G. Lopez Bautista, B. Chilel, G.Dominguez, and A. De la Mora provided field assistance. G. Ibarra-

Nunez and El Colegio de la Frontera Sur provided logical support.S. Jha, D. Jackson, and D. Allen helped with the R code. K. Ennis,R. Friedrich, D. Gonthier, L. Moorhead, C. Murnen, G. Pardee, I. Per-fecto, J. Vandermeer, and Z. Liu contributed to discussions about the

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anuscript. The Organic Farming Research Foundation, the Uni-ersity of Toledo, and NSF grant DEB-0349388 I. Perfecto and J.andermeer provided funding for this research.

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