LETTER How pervasive is biotic homogenization in human-modified
tropical forest landscapes?
Ricardo Ribeiro de Castro
Solar,1,2* Jos Barlow,2,3 Joice
Ferreira,4 Erika Berenguer,2
Alexander C. Lees,3 James R.
Thomson,5,6 J�ulio Louzada,2,7
M�arcia Mau�es,4 N�argila G. Moura,3
Victor H. F. Oliveira,2,7 J�ulio C. M.
Chaul,1 Jos�e Henrique Schoereder,1
Ima C�elia Guimar~aes Vieira,3 Ralph
Mac Nally5 and Toby A. Gardner,8,9
Abstract
Land-cover change and ecosystem degradation may lead to biotic homogenization, yet our under-standing of this phenomenon over large spatial scales and different biotic groups remains weak.We used a multi-taxa dataset from 335 sites and 36 heterogeneous landscapes in the BrazilianAmazon to examine the potential for landscape-scale processes to modulate the cumulative effectsof local disturbances. Biotic homogenization was high in production areas but much less in dis-turbed and regenerating forests, where high levels of among-site and among-landscape b-diversityappeared to attenuate species loss at larger scales. We found consistently high levels of b-diversityamong landscapes for all land cover classes, providing support for landscape-scale divergence inspecies composition. Our findings support concerns that b-diversity has been underestimated as adriver of biodiversity change and underscore the importance of maintaining a distributed networkof reserves, including remaining areas of undisturbed primary forest, but also disturbed and regen-erating forests, to conserve regional biota.
Human activities have profoundly modified most ecosystemson Earth (Steffen et al. 2015), causing widespread loss of bio-diversity (Vellend et al. 2007; Arroyo-Rodriguez et al. 2013;Newbold et al. 2015), changes in community structure (Dor-nelas et al. 2014), and the loss of ecosystem functions and ser-vices (Mitchell et al. 2015). In many places, these changeslead to taxonomic and functional simplification and the con-vergence of biotas within regions (McKinney & Lockwood1999; Olden & Rooney 2006), a phenomenon known as biotichomogenization. Biotic mixing and homogenization have beenreported for both aquatic and terrestrial taxa and in most ofthe world’s ecosystems (Baiser et al. 2012), and representmajor signals of the start of the Anthropocene, the currenthuman-dominated geological epoch (Lewis & Maslin 2015).Biotic homogenization is manifested as species loss, species
introductions and range shifts, and changes in species abun-dance distributions. Such changes are often driven or exacer-bated by human activities that drive land-cover change,habitat loss, habitat fragmentation, and degradation (Karpet al. 2012; P€uttker et al. 2015; Thomson et al. 2015). Decadesof research on the ecological consequences of these distur-bances provide substantial evidence that land-use intensifica-
tion drives reductions in both local (a) diversity (Gibson et al.2011; Newbold et al. 2015) and b-diversity (i.e. differences inspecies assemblage composition among sites, Whittaker 1972;Karp et al. 2012). As a result, the most disturbed sites arecharacterized by an impoverished subset of species that typi-cally have relatively high dispersal abilities and generalisthabits (Vellend et al. 2007; Karp et al. 2012). However, vari-ability in disturbance regimes can drive divergence in the com-position of species assemblages and hence an increase inb-diversity, such as through differing successional pathwaysamong forest fragments (e.g. Arroyo-Rodriguez et al. 2013).Work on biotic homogenization has been almost exclusively
conducted at a single spatial scale on a single taxon in relativelyfew types of land-use. This means that the processes of biotichomogenization and divergence in assemblage composition forentire landscapes and at multiple spatial-scales are littleexplored (Tabarelli et al. 2012; Barton et al. 2013). There is agrowing body of theory (Tscharntke et al. 2012) and empiricalinformation (Pardini et al. 2010; P€uttker et al. 2015) suggestingthat landscape- and regional-scale processes play a critical rolein determining species distributions and the persistence of biodi-versity in human-modified systems. Tscharntke et al. (2012)predicted that local biodiversity responses might be influencedby landscape-scale differences in: (1) the spatial heterogeneity in
1Departamento de Biologia Geral. Av. PH Rolfs s/n. Vic�osa, Universidade Fed-
eral de Vic�osa, Vicosa, Minas Gerais, CEP 36570-900, Brasil2Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK3MCTI/Museu Paraense Em�ılio Goeldi, CP 399, CEP 66040-170 Bel�em, PA, Brasil4Embrapa Amazonia Oriental. Bel�em, Par�a CEP 66095-100, Brasil5Institute for Applied Ecology, University of Canberra, Bruce ACT 2617, Australia6Department of Environment, Arthur Rylah Institute for Environmental
Research, Land, Water and Planning, Vic. 3084, Australia
7Universidade Federal de Lavras, Setor de Ecologia e Conservac�~ao. Lavras,Minas Gerais, CEP 37200-000, Brasil8Stockholm Environment Institute, Linn�egatan 87D, Box 24218, Stockholm
104 51, Sweden9International Institute for Sustainability, Estrada Dona Castorina, 124, Horto,
types and intensities of disturbance events and (2) the interac-tion between disturbances and the natural environmentalheterogeneity that predated human mediated modifications.Both (a) and (b) contribute to the potential for landscape-scaledivergence in species composition (e.g. Laurance et al. 2007).Divergence for instance, is driven by the combined effects ofspatially heterogeneous environmental conditions, local pres-sures, and dispersal limitation (Myers et al. 2013).To test the extent to which landscape-moderated patterns of
b-diversity determine landscape-wide biodiversity and modu-late the effects of local-scale disturbances, we need to decom-pose patterns of species diversity (for multiple taxa) at severalspatial scales and over broad gradients of land-use intensityand disturbance. We need to understand the extent to whichvariation in b-diversity at different spatial scales and inresponse to different levels of land-use intensity and distur-bance is driven by species replacement (turnover) compared tovariation arising from species richness (resulting in nested-ness), a distinction missing from the vast majority of studiesto date (Baselga 2010; Baselga & Leprieur 2015). If b-diversityis driven by nestedness rather than by turnover, then general-ist and highly dispersive species consistently should befavoured in areas of more intense land use, resulting in biotichomogenization. This understanding is urgently needed tosupport practical conservation action in the humid tropics,which house the vast majority of the world’s terrestrial biodi-versity (e.g. Slik et al. 2015) but that continue to be subjectedto high rates of land-use change (Hansen et al. 2013; Kimet al. 2015) and forest degradation (Asner et al. 2009).Here, we undertook the first assessment of how biotic
homogenization plays out at multiple scales and for multipletaxa based on data for five taxa (birds, dung beetles, plants,orchid bees, and ants) sampled in 335 sites in 36 landscapes intwo regions of the Brazilian Amazon. These regions includemost of the variation in land-cover classes that characterizehuman-modified tropical forest landscapes, including arablecrops, cattle pastures, secondary forests regenerating oncleared land, and a gradient of primary forests experiencingdiffering degrees of anthropogenic disturbance.We use this extensive data set to explore three hypotheses.
(1) b-diversity, both among-sites and among-landscapes,should decline along a gradient of forest disturbance andland-use intensification (i.e. more intense human activities leadto greater biotic homogenization; Vellend et al. 2007; Karpet al. 2012). The loss of biodiversity should be attenuated atlandscape scales due to the compensating effect of divergencein species composition arising from spatial heterogeneity indisturbances or from differences in initial environmental con-ditions (Laurance et al. 2007; Tscharntke et al. 2012). (2) Theimportance of nestedness in determining changes in b-diver-sity, and hence the relative importance of local extinctionscompared with species replacement, should increase along adisturbance gradient from undisturbed forest to disturbed andregenerating forest to production areas, and independently ofscale (Baiser et al. 2012). And, (3) species richness at site,landscape and regional scales should decline consistentlyalong a gradient of land-use intensification (from undisturbedto disturbed and regeneration forest, to non-forest areas; Dor-nelas et al. 2014; Newbold et al. 2015). However, we expected
that high levels of b-diversity in disturbed areas wouldmoderate this decline in richness at larger spatial scales(Tscharntke et al. 2012). Last, most work on the effects ofland-use intensification on biodiversity considers one, or atmost two, distinct taxa. This limits the extent to which deduc-tions can be extended to biodiversity generally. Our concur-rent analysis of five very different taxa provides a powerfulopportunity to assess the extent to which our observations arelikely to be general phenomena.
METHODS
Study sites
We conducted our study in two regions of Par�a state, in theBrazilian Amazon: the municipality of Paragominas (hereafterPGM) and in the municipalities of Santar�em, Belterra andMoju�ı dos Campos (hereafter STM; Fig. 1). These two studyregions, separated by c. 800 km encompass more than threemillion hectares of lowland forests and differ markedly intheir human colonization history (Gardner et al. 2013).Although in recent decades both regions have suffered signifi-cant deforestation and forest degradation, leading to severaldegrees of disturbance, they still retain more than half of theirnative forest cover.
Sampling design
We divided each region into third or fourth order drainagecatchments (c. 5.000 ha; hereafter called landscapes) using theSWAT (Soil and Water Assessment Tool) model for ARCGIS10. Eighteen landscapes were selected, covering a gradient offorest cover (from 6% to 100% forest cover) and the majorland-cover classes in each region (Table 1, Gardner et al.2013). Within each landscape, we allocated 8–12 transects(each 300 m long) at a density of 1 transect/400 ha and sepa-rated by ≥ 1.5 km. Sampling of all taxa was conducted alongeach transect, which formed the site-scale of our analyses.These sites were allocated in proportion to the area of forestand non-forest in a given landscape (e.g. if forest comprised40% of the land cover in a landscape, then c. 40% of the siteswere located randomly, with a minimum inter-site separationof 1500 m, in forest areas). Some 335 sites were sampled forplants, birds, dung beetles, ants and orchid-bees. Details ofsampling techniques for each taxonomic are in the SupportingInformation. Other details for methods including definitionsof land-cover classes and further information on the studyregions is in Gardner et al. (2013).
DATA ANALYSES
Species presence-absence data were used for the main analyses,and all diversity metrics were repeated using proxies of abun-dance for each taxon. Our measures of abundance were thenumber of recorded individuals for vegetation, beetles and bees,and the number of point-counts (birds) or traps (ants) in whichthe species was recorded. Apart from vegetation data, these areproxies rather than true measures of abundance because the lat-ter is very difficult to obtain for diverse tropical forest biota in
multiple sites. Nevertheless, such abundance data provides auseful test of the robustness of our results and the potential forany bias in accounting for rare species (Jost 2007).
Diversity partitioning
We defined asite-diversity as the average number of species persite in each land-cover class, and alandscape-diversity as the
total number of species per landscape for each land-coverclass. c-diversity (cregion) was the total number of species ineach region per land-cover class. We calculated multiplicativeb-diversity for each scale. Multiplicative b is a measure of theeffective number of distinct assemblages or samples in aregion (Jost 2007). Multiplicative partitioning of diversity(Whittaker 1960, 1972) uses the formula cregion = asite 9bamong-sites 9 bamong-landscapes, where bamong-sites is the effectivenumber of distinct sites in a landscape and bamong-landscapes isthe effective number of distinct landscapes in the entireregion. We calculated all values for each land-cover class andtaxonomic group separately, and used multiplicative partition-ing as a measure of the magnitude of differentiation, indepen-dent of a-diversity (and therefore of species loss), thusindicating the amount by which diversity (e.g. species richness)increased from local to regional scales. We computed diversityvalues using both species richness (Hill numbers of order 0)and the exponential of Shannon entropy (Hill numbers oforder 1). While species richness includes the effect on all spe-cies irrespective of their frequency, the exponential of Shan-non entropy weights species by their frequencies, reducing theinfluence of rare species (Chao et al. 2014).
Figure 1 Map of the sampling regions and sampling design. We stratified our sampling of all five sampled taxonomic groups within three spatial scales:
regional, landscape and site. See the Supporting Information for more information on the taxa-specific sampling protocols.
Table 1 List of sites sampled within each land-cover class in both regions
Sample sizes differed for different land-cover classes becausewe undertook proportional (relative to forest and non-forestcover) sampling in each landscape. This could lead to biasedresults for analyses of b-diversity that may be sensitive tosample size. Therefore, we resampled the data to obtain com-parable values of b-diversity (Baselga 2010). To calculatebamong-sites for each land-cover class, we randomly sampledwithout replacement three sites of the same land-cover classwithin each landscape 5000 times. We calculated bamong-sites bydividing alandscape (the cumulative species richness of the threesites) by asite (the average species richness per site). To calcu-late bamong-landscapes for each land-cover class, we randomlysampled without replacement the data selecting three land-scapes with three sites each 5000 times. Therefore, bamong-land-
scapes was cregion (total species richness of three landscapes)divided by alandscape.
Decomposition of b-diversity
We decomposed bamong-sites and bamong-landscapes diversities intotwo components: nestedness (species gain/loss) and speciesreplacement (turnover) by calculating the multi-site Sørensen(bSOR) and Simpson (bSIM) indices (Baselga 2010, 2012). bSOR
measures total b-diversity, is positively related to multiplica-tive b (Pearson r = 0.98) and includes variation in speciescomposition from both replacement and nestedness. bSIM isindependent of variation in species richness so only measuresturnover. Therefore, differences between values are representa-tive of the nestedness component of b-diversity: bNES = bSOR–bSIM (Baselga 2010, 2012). Multi-site b-diversity calculationsbased on the Sørensen index are sensitive to sample size, sowe calculated b-values for all land-cover classes using aresampling procedure. We took 5000 random samples fromthe total number of sites of each land-cover class (Table 1) inthe same way that we did for each scale of b-diversity to havecomparable measures of bSOR and bSIM diversities. The per-centage importance of the nestedness component (bNES/bSOR)was used as a response variable for analyses. To assess therobustness of our results for the bSOR partition, we also calcu-lated Jaccard indices as proposed by Baselga (2012) and Car-valho et al. (2013). While a comparative review of thesemethods is beyond the scope of this article, both approachesyielded qualitatively very similar conclusions (see Legendre2014 and Baselga & Leprieur 2015).
Statistical analyses
We used generalized linear mixed models (GLMM, Bolkeret al. 2009) for all diversity comparisons between land-coverclasses. To investigate how asite and cregion diversities differacross land-cover classes, we first standardized species richnessper site for each taxon because the different taxa have very dis-parate levels of species richness. We divided the richness of eachtaxon in each individual site by the value of the richest site inthe entire sample, leading to values between 0 and 1 for eachtaxon (a-diversity). We performed the analysis using standard-ized values for all taxa jointly and for each taxonomic groupseparately. We used land-cover classes as the predictor variableand set taxonomic group, landscape identity, and region as ran-
dom effects. For c-diversity, we considered the total number ofspecies (also standardized to range between 0 and 1) in each tax-onomic group and land-cover class within each landscape as theresponse variable, and land-cover classes as the explanatoryvariable, with taxon and region set as random effects. We per-formed pairwise contrast analyses to evaluate specific differ-ences between land-cover classes combining the most similarclasses and comparing models (Crawley 2012).To assess how b-diversity was related to land-cover classes
at two scales (among-sites and among-landscapes), we used thevalues of b-diversity for each taxon within each land-cover asa response variable and land-cover class as the predictor vari-able. Landscape and region were included as random effectsfor the among-site b-diversity, with region as a random effectfor b-diversity among-landscapes. We performed contrast anal-yses in the same way as for analyses of asite and cregion.To analyse whether processes of nestedness and replacement
differed among land-cover classes and among taxa, we usedland-cover class as the predictor variable and used the per-centage contribution of nestedness as the response variable foreach taxon within each land-cover class. We did this for bothamong-sites and among-landscapes scales. Random effectswere landscape and region for among-site b-diversity andregion for among-landscapes b-diversity. We used binomialerror distributions, corrected for over-dispersion if necessaryby incorporating individual-level random effects in the model,and contrast analysis to discriminate among levels significance(Crawley 2012).We used R v3.2.0 (R Core Team 2015) for all analyses. We
performed residual analyses for all models and checked forthe distribution of errors and over-dispersion in the data. Weadjusted P-values following Benjamini & Yekutieli (2001),controlling for the probability of false discovery rate in multi-ple tests. Diversity partitioning and correlation analyses wereconducted using the vegan package v2.3-0. b-diversity decom-position was undertaken using the betapart package v1.3, andGLMMs using the lme4 package v1.1-8.
RESULTS
Species richness in different land-cover classes at site and landscape
scales
Species richness at the site level (asite) declined steadily fromundisturbed forests to disturbed primary forests, secondaryforests and production areas (cattle pastures and mechanizedagriculture) with significant differences between all land-coverclasses (v2 = 398.92, d.f. = 185, P < 0.001, Fig. 2a). Speciesrichness at the landscape level (alandscape) followed a similarpattern, declining along the same gradient (v2 = 202.86,d.f. = 8, P < 0.001, Fig. 2b), with significant differencesbetween all land-cover classes apart from logged and burntand secondary forests (v2 = 1.21, d.f. = 8, P = 0.30, Fig. 2b).Species richness at the regional scale (i.e. cregion) differed onlywhen comparing forest areas (of any type) with productionareas (of any type) (v2 = 42.27, d.f. = 5, P < 0.001, Fig. 2c).We found similar patterns and statistical results when wecomputed diversity measures taking species abundances orfrequencies into account (exponential Shannon entropy)
(Fig. S1). These trends were broadly similar for each taxon,which despite individual idiosyncrasies, exhibit a generaldecline in species richness outside primary forests (Fig. 3).
b-diversity in different land-cover classes
Among-site b-diversity was consistently greater in forest habi-tats (of all types) than in production areas (of any type)(v21,8 = 12.37, d.f. = 10, P ~ 0.005, Fig. 4a). This pattern heldwhen based on measures of abundance (Fig. S2a). Conversely,we found little difference in landscape-scale b-diversity(bamong-landscapes) among all land-cover classes (v2 = 9.24,d.f. = 6, P ~ 0.09, Fig. 4b) based only on presence-absencedata. However, when proxies of abundance are accounted forthere was a significant drop in bamong-landscapes when moving
from forest to non-forest land (v2 = 15.07, d.f. = 6, P < 0.001,Fig. S2b). Patterns were essentially the same for each taxo-nomic group, although bamong-sites was somewhat greater inarable fields for birds and in secondary forests for dung bee-tles) (Fig. 3b and c).
Relative importance of nestedness and replacement contributing to
b-diversity
Species replacement accounted for the majority of b-diversityin all land-cover classes but the proportional contribution ofnestedness increased in non-forest areas (bSOR, Fig. 5). Thecontribution of nestedness to bamong-sites to total b-diversityshowed a three-fold increase in production areas comparedwith forest areas (v2 = 70.22, d.f. = 10, P < 0.001, Fig. 5a).
(a)
(b)
(c)
Figure 2 a and c components of diversity in different land-cover classes. Diversity is expressed as the standardized average species richness within each
land-cover class for all taxa, and separately for a-diversity-site – species richness at the site scale (a); a-diversity-landscape – species richness at the
landscape scale (b); and c-diversity – pooled species richness at the regional scale (c). Different colours illustrate forest (black and dark grey) and non-
forest land-cover classes (light grey). We used P < 0.05 to determine significance levels and error bars are standard errors (for gamma they represent only
maximum and minimum values, as n = 2). Codes for land-cover classes are as Table 1.
Moreover, the contribution of nestedness to bamong-sites in dis-turbed and secondary forests was also significantly greaterthan that observed in undisturbed sites (v2 = 4.1, d.f. = 10,P = 0.043, Fig. 5a). The overall pattern was broadly similarfor bamong-landscapes with b-diversity being dominated by spe-cies replacement, but with nestedness playing a more impor-tant role in non-forest compared to forest areas (v2 = 44.163,d.f. = 6, P < 0.001, Fig. 4b) but with a similar contributionfor undisturbed and disturbed forest sites. Results for individ-ual taxa broadly followed these patterns but were particularlymarked for dung beetles and orchid bees for which the contri-bution of nestedness in production areas accounted for up to60% of total b (Fig. S3).
DISCUSSION
Our assessment of patterns of diversity among multiple taxaand spatial scales in two human-modified regions of theBrazilian Amazon represents a major advance in our under-standing of biotic responses to land-cover change and human-
induced forest disturbance. While we found consistent changesin a-diversity in human-modified tropical landscapes, changesin b-diversity, and the process of biotic homogenization, weredependent on land cover and scale. Results were very similarwhether based on species occurrence or on abundance or inci-dence data. We assess the implications of these findings in thecontext of our initial hypotheses by examining the newinsights gained from our disturbance gradient of land-coverclasses, the multiple spatial scales of our biodiversity samplingand the multi-taxonomic analysis. We consider the practicalimplications of our results for the conservation of forest biotain the human-modified landscapes that increasingly dominatethe tropics.
Land-cover, spatial scale and taxa-dependent patterns of biotic
homogenization
a-diversity declined consistently along a gradient of increasinganthropogenic disturbance, which was consistent with thefindings of earlier studies (e.g. Gibson et al. 2011; Moura
(a)
(b)
(d)
(e)
(c)
Figure 3 Components of diversity for all taxa across all land-cover classes based on species occurrence data. Row (a), shows asite-diversity (i.e. average
number of species per site), rows (b) and (d) show b-diversity among-sites and among-landscapes, row (c) shows alandscape-diversity (i.e. average number of
species per landscape) and row (e) shows c-diversity (for each region). Different colours illustrate forest (black and dark grey) and non-forest land-covers
(light grey), we used P < 0.05 to determine significance levels and error bars represent standard errors (bars are absent where we could only calculate a
single value). Codes for land-cover classes are as Table 1.
et al. 2013). However, our b-diversity results show how con-clusions about biotic homogenization depend on both theintensity of anthropogenic disturbance and the scale of analy-sis.We found strong evidence that the conversion of forests to
agriculture leads to biotic homogenization by reducing b-di-versity (c.f. Karp et al. 2012; P€uttker et al. 2015). Homoge-nization is likely to be driven by the loss of pre-disturbancebiota, followed by the colonization of generalist species withhigh dispersal capabilities (Bengtsson 2010). Homogenizationalso arises from increased homogeneity of environmentalresources, which favours similar sets of species (Olden et al.2004). Evidence of biotic homogenization is supported by theincreasingly important contribution of nestedness to total
b-diversity in non-forest areas, which indicates that species-poor sites are characterized by a subset of more generalizedand disturbance-tolerant species due to the loss of more eco-logically specialized, disturbance-intolerant and forest-depen-dent species (Baiser et al. 2012).There was less evidence for biotic homogenization within
forests, where b-diversity was consistently high within all dis-turbance classes, irrespective of taxon or the scale of analysis.This high level of community dissimilarity among forestdisturbance classes may be due to pre-existing differences inenvironmental conditions and biota and from variabilityin disturbance processes and resultant spatial heterogeneity inlocal extinction filters (Tscharntke et al. 2012). Differences intime-since-disturbance, and the frequency and intensity of
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Figure 4 b-diversity among sites and landscapes based on species occurrence data. Β-diversity was calculated as the multiplicative Whittaker’s b and
sampling effort is standardized by resampling all land-cover classes to the same sample size. Data are presented as average b-diversity per taxon and per
land-cover class for both among sites within landscapes (a) and among landscapes within regions (b). Different colours illustrate forest (black and dark
grey) and non-forest land-covers (light grey), we used P < 0.05 to determine significance and errors bars are standard errors. Codes for land-cover classes
disturbance events, may be important in maintaining b-diver-sity in all forests. For example, secondary forests maintaineda high level of b-diversity among sites despite the initial dis-turbance (usually conversion to pasture or agriculture) remov-ing the original biological communities, which reflects theimportance of variation introduced by different successionalpathways (e.g. Norden et al. 2015). Variation in the timing ofdisturbances may maintain b-diversity in forests affected bylogging or fires, with longer term studies indicating a slowrecovery of even the most mobile taxa (Mestre et al. 2013).High levels of b-diversity at larger spatial scales partially off-set the localized loss of diversity from specific forest distur-bances (Laurance et al. 2007), which was shown by theattenuated declines in species richness at landscape and regio-nal scales. However, the much-reduced levels of a-diversity in
disturbed and regenerating forests suggest only partial com-pensation. Moreover, the contribution of nestedness toamong-site b-diversity in disturbed and regenerating forests isabout twice that of undisturbed primary forests, suggesting asubtle shift towards biotic homogenization even within forests(Arroyo-Rodriguez et al. 2013).While we saw consistently high levels of b-diversity among
both sites and landscapes in remaining forest areas, we foundthat landscape-scale b-diversity remained consistently high innon-forest areas, even though such areas had much reduceda-diversity. Given that turnover (replacement) in species com-position accounted for most of the among-landscape b-diver-sity even in non-forest areas, this result supports thelandscape divergence hypothesis (Laurance et al. 2007). Thathypothesis asserts that disturbed areas are likely to diverge in
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Figure 5 Percentage contribution of the nestedness component to the total b-diversity observed among sites and among landscapes. Decomposition of b-diversity into nestedness and replacement components was computed following Baselga (2010) (bNES = bSOR- bSIM) and standardized by resampling all
land-cover classes to the same sample size. Data are presented as the average percentage contribution of the nestedness component per taxon and per land-
cover class for both the decomposition of b-diversity among sites in a landscape (a) and the decomposition of b-diversity among landscapes in a region (b).
Different colours express forest (black and dark grey) and non-forest land-covers (light grey), we used P < 0.05 to determine significance and errors bars
are standard errors. Codes for land-cover classes are as Table 1.
species composition because of differences in the effects of dis-turbance, or in the ways in which disturbances processes inter-act with underlying differences in environmental heterogeneity(see also Arroyo-Rodriguez et al. 2013). However, it is alsothe case that the contribution of nestedness to both among-site and among-landscape b-diversity is much greater in non-forest areas than in forest areas. While increased nestedness isan indication of increased biotic homogenization, differencesin community reassembly processes (e.g. ‘payment of extinc-tion debt’ and lag effects in colonization) in non-forest areasmeans that homogenized communities are not all nested inthe same consistent fashion. This is to be expected for highlydynamic agricultural landscapes that are subject to frequentchanges in cropping and land-management regimes, includingfire, ploughing and cattle grazing.The broad consistency of outcomes among taxa (Fig. 3)
suggests that these general findings are likely to be typical oftropical forest biota in human-modified landscapes. However,there were some idiosyncratic differences in taxonomicresponses (e.g. Barlow et al. 2007) that may provide insightsinto the nature of the biotic homogenization process. Whilesome of the most obvious differences in diversity relate todirect consequences of land management (i.e. removal ofwoody vegetation from agricultural land), others results mayarise from spill-over effects and the presence of occasionalspecies (e.g. Barlow et al. 2010). For birds, even though thereare very few species that reside in arable fields (Moura et al.2013), occasional visitors from a pool of mobile species occu-pying adjacent habitats can contribute towards the mainte-nance of high apparent levels of b-diversity in open areas (e.g.periodic appearance of nomadic granivorous species in pas-tures; e.g. Lees et al. 2013). Similarly, invertebrate taxa sam-pled with baited traps may have more occasional species ifsome taxa are attracted from neighbouring habitats. Theimportance of rare and occasional species in driving high b-di-versity in open areas was supported by the lower levels of b-diversity when we considered species abundance data(Fig. S4).
Implications for biodiversity conservation in human-modified
tropical landscapes
In contrast with our observation of a consistent decline in a-di-versity along a gradient of increasing anthropogenic distur-bance, b-diversity and the process of biotic homogenizationdepended on both land-cover class and the spatial scale ofobservation. These findings were supported by relatively consis-tent responses among diverse taxa, providing a robust basis formaking recommendations for the conservation of forest biota.Environmental laws currently governing tropical forests,
such as the Brazilian Forest Code (Federal Law 12.651, 17October 2012), focus almost exclusively on the protection offorest cover. Forest cover change is relatively easy to measureby using remote-sensing techniques, both at the scale of indi-vidual countries (e.g. PRODES-INPE 2015) and globally(Hansen et al. 2013). Our results support the importance ofmaintaining forest cover (Gardner et al. 2009) because all for-est types were much more species rich and biologically distinctthan any production areas. However, undisturbed primary
forests were consistently more diverse than forests disturbedby fragmentation, logging and fire, which underscores theurgent need to prioritize the conservation of the remainingareas of undisturbed forest where they exist (Gibson et al.2011; Moura et al. 2013) and to minimize any further forestdegradation and to restore actively already degraded areas(Malhi et al. 2014).While the importance of conserving undisturbed forests is
well-supported by previous work, our multi-landscape analysisprovides strong additional support for the importance ofmaintaining a broad and distributed network of forestreserves that includes disturbed primary and secondary forests(Chazdon et al. 2009), especially in regions where there are noremaining undisturbed forests. This contention is supportedby the high levels of among-site and among-landscape b-diver-sity we observed in all forest types and across all taxa, whichare explained primarily by high levels of species replacement(sensu Baselga 2010). While many species may be lost fromindividual sites, regional biota in human-modified landscapescharacterized by a heterogeneous mosaic of conserved anddegraded areas of forest may be able to support much of thelocal biodiversity. The persistence of different taxa in dis-parate areas provides opportunities for both ecological recov-ery, through either natural processes or from strategicinterventions, and for adaptation to changes (Malhi et al.2014).Our work is timely because debates about the old conserva-
tion planning contention of ‘single large or several small’ pro-tected areas remain highly relevant. Our results are germaneto decisions about conservation banking, offset schemes andthe design of land-sparing initiatives to support both agricul-tural development and biodiversity conservation. One exampleis Brazil’s legal reserve trading system (within the ForestCode) for compensation. The consistently high levels ofamong-landscape b-diversity that we report indicate thatreserves should not be concentrated in one part of a region(e.g. in the form of a compensation bank) and that offsetareas preferentially should be positioned within the sameregion for which the compensation is being made. If thesesuggestions are not followed, then there will be substantiallosses of biodiversity. Effectively balancing conservation andrural development objectives in complex multiple-use land-scapes such as those of the eastern Amazon remains a majorchallenge. However, our results suggest that the effectivenessof policies could be improved by considering the differenteffects of land-cover change and anthropogenic disturbanceon patterns of biological diversity at multiple scales.
CONCLUSION
The paucity of studies looking at multiple scales and taxa hasmeant that the processes of biotic homogenization and diver-gence in whole landscapes are not well-understood (Tabarelliet al. 2012; Barton et al. 2013). We have disentangled some ofthe adverse effects of human-induced disturbances on biodi-versity in tropical landscapes by exploring biotic homogeniza-tion over a broad disturbance and land-use intensity gradientand by concurrently considering multiple taxa. Our resultsoffer strong support to theoretical predictions that landscape
processes can have a strong effect on landscape-wide biodiver-sity patterns (Laurance et al. 2007; Arroyo-Rodriguez et al.2013; Barton et al. 2013), and that b-diversity has been under-estimated as an important process involved in biodiversitychange (Tscharntke et al. 2012). We show how landscape-scale differences in species assemblages for very different land-cover classes and taxa can drive landscape-wide patterns ofbiodiversity that may partially and temporarily offset site-scale impacts.
ACKNOWLEDGEMENTS
We are indebted to the invaluable support of our field assis-tants, farmers and community of all surveyed municipalities.We are also thankful to Frederico Neves, Tathiana Sobrinho,Fl�avia Carmo, Victor Arroyo-Rodr�ıguez and two anonymousreferees for their helpful insights on previous versions of thismanuscript. We are grateful for financial support from Insti-tuto Nacional de Ciencia e Tecnologia – Biodiversidade e Usoda Terra na Amazonia (CNPq 574008/2008-0), EmpresaBrasileira de Pesquisa Agropecu�aria – Embrapa(SEG:02.08.06.005.00), the UK government Darwin Initiative(17–023), The Nature Conservancy, and Natural EnvironmentResearch Council (NERC) (NE/F01614X/1 and NE/G000816/1). JB, JHS, NGM and RRCS were supported by supportedby CNPq grants (400640/2012-0; 200846/2012-4). EB and JBwere also supported by a NERC grant (NE/K016431/1). RMand JT were partially supported by an Australian ResearchCouncil DORA Grant (DP120100797). TAG is supported byFormas (Grant No. 2013-1571). This is the contribution num-ber 43 of the Sustainable Amazon Network (www.redeamazo-niasustentavel.org).
AUTHORSHIP
RRCS designed the work program, and wrote the first draft ofthe manuscript; JB, JF, ICGV, TAG coordinated the project;RRCS, JB, TAG, JF, JL, MM, JHS designed the experiment;RRCS, EB, ACL, MM, NGM, VHFO, JCMC collected thedata; RRCS, JRT, RMN analysed the data; and all authorscontributed substantially to preparing the final manuscript.
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SUPPORTING INFORMATION
Additional Supporting Information may be downloaded viathe online version of this article at Wiley Online Library(www.ecologyletters.com).
Editor, Howard CornellManuscript received 25 March 2015First decision made 27 April 2015Second decision made 14 July 2015Manuscript accepted 25 July 2015
