1 Title: The biodiversity benefit of native forest over

2 Grain-for-Green plantations

3 1,2 3,4 1 5,6 4 Authors: Xiaoyang Wang , Fangyuan Hua , Lin Wang , David S. Wilcove , 1,7,8* 5 Douglas W Yu 6 7 Author affiliations: 8 1 State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy 9 of Sciences, Kunming, Yunnan 650223, China 10 2 Kunming College of Life Sciences, University of Chinese Academy of Sciences, Kunming, Yunnan 650223, 11 China 12 3 Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge CB2 3QZ, U.K. 13 4 Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese 14 Academy of Sciences, Kunming, Yunnan 650201, China 15 5 Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, United States 16 6 Program in Science, Technology, and Environmental Policy, Woodrow Wilson School of Public and 17 International Affairs, Princeton University, Princeton, NJ 08544, United States 18 7 Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming Yunnan, 19 650223 China 20 8 School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk NR4 21 7TJ, UK 22 23 *Correspondence to: Email: [email protected]

24 Author declaration: XW, FH, and DY conceived the research and designed the experiments. XW 25 and FH carried out the fieldwork, WL created Figure 1, and XW carried out the lab work. XW and 26 DY carried out the bioinformatic and statistical analyses. XW, DY, and FH wrote the manuscript, 27 with comments from all other authors.

28

29 Fangyuan Hua: [email protected]

30 Lin Wang: [email protected]

31 David S. Wilcove: [email protected]

32 33 Biosketch: Prof. Douglas Yu is a Principal Investigator at the Kunming Institute of Zoology, China, 34 and Professor of Ecology at the University of East Anglia, UK. His group is a leading developer of 35 methods for rapid assessment of eukaryotic biodiversity using high-throughput DNA sequencing 36 (metabarcoding, mitogenomics, metagenomics) and participates in multiple collaborations to provide 37 biodiversity information for ecosystem-scale management. Yu is also a co-founder of NatureMetrics 38 (www.naturemetrics.co.uk), which supplies DNA-based services to governments, NGOs, and

1 39 businesses so that they can improve policies and processes for protecting and growing biodiversity 40 and natural capital through adaptive management. 41 42 ACKNOWLEDGMENTS 43 We thank Y. Liu, Q. Wei, S.K. Robinson, S. Francis, R. Zeidler, W. Hua, P. Li, M. Xu and Y. Yao for 44 helpful discussions and logistical support. Special thanks go to our field assistants: S. Huang, M. 45 Tong, and K. Du. We thank Ding Zhaoli for sequencing support. We are grateful for the support of Ji 46 Yinqiu, Wu Chunying, Yang Chunyan, and Wang Yunyu. D.W. Yu and X. Y. Wang were supported 47 by the National Natural Science Foundation of China (41661144002, 31670536, 31400470, 48 31500305), the Key Research Program of Frontier Sciences, CAS (QYZDY-SSW-SMC024), the 49 Bureau of International Cooperation project (GJHZ1754), the Strategic Priority Research Program of 50 the Chinese Academy of Sciences (XDA20050202, XDB31000000), the Ministry of Science and 51 Technology of China (2012FY110800), the University of East Anglia, and the State Key Laboratory 52 of Genetic Resources and Evolution at the Kunming Institute of Zoology. F. Hua and D. S. Wilcove 53 were supported by the High Meadows Foundation and Princeton University. L. Wang was supported 54 by the National Nature Science Foundation of China (31872963) and the Informatization Program of 55 the Chinese Academy of Sciences (XXH13505-03-102-2).

2 1 Title: The biodiversity benefit of native forests and

2 mixed-species plantations over monoculture plantations

3

4 Word count: Abstract = 339; Main text = 5605

5 Number of references: 92

6 Number of Figures, Tables: 6 and 0 respectively

7 Data accessibility statement: Sequence data have been submitted to GenBank under accession 8 number SAMN09981891. Bioinformatic scripts are in Supplementary Information. R scripts and data 9 tables are available at https://github.com/dougwyu/Sichuan2014.

10

1 11 ABSTRACT 12 Aim: China’s Grain for Green Program (GFGP) is the largest reforestation program in the 13 world and has been operating since 1999. The GFGP has promoted the establishment of tree 14 plantations over the restoration of diverse native forests. In a previous study, we showed that 15 native forests support a higher species richness and abundance of birds and bees than do 16 GFGP plantations and that mixed-species GFGP plantations support a higher level of bird 17 (but not bee) diversity than do any individual GFGP monocultures (although still below that 18 of native forests). Here, we use metabarcoding of arthropod diversity to test the generality of 19 these results. 20 21 Location: Sichuan, China 22 23 Methods: We sampled arthropod communities using pan traps in the land-cover types 24 concerned under the GFGP. These land-use types include croplands (the land cover being 25 reforested under the GFGP), native forests (the reference ecosystem as the benchmark for the 26 GFGP’s biodiversity effects), and the dominant GFGP reforestation outcomes: monoculture 27 and mixed-species plantations. We used COI-amplicon sequencing (‘metabarcoding’) of the 28 arthropod samples to quantify and assess the arthropod community profiles associated with 29 each land-cover type. 30 31 Results: Native forests support the highest overall levels of arthropod species diversity, 32 followed by mixed-species plantations, followed by bamboo and other monocultures. Also, 33 the arthropod community in native forests shares more species with mixed-species 34 plantations than it does with any of the monocultures. Together, these results broadly 35 corroborate our previous conclusions on birds and bees but show a higher arthropod 36 biodiversity value of mixed-species plantations than previously indicated by bees alone. 37 38 Main conclusion: In our previous study, we recommended that GFGP should prioritize the 39 conservation and restoration of native forests. Also, where plantations are to be used, we 40 recommended that the GFGP should promote mixed-species arrangements over 41 monocultures. Both these recommendations should result in more effective protection of 42 terrestrial biodiversity, which is an important objective of China’s land-sustainability 43 spending. The results of this study strengthen these recommendations because our policy 44 prescriptions are now also based on a dataset that includes over 500 species-resolution taxa, 45 ranging across the Arthropoda. 46 47 KEYWORDS: Arthropoda, biodiversity, China, forest management, Grain for Green 48 Program, metabarcoding, reforestation

2 49 1 INTRODUCTION 50 51 An important challenge for conservation science is to quantify the biodiversity impacts of 52 major policy initiatives, especially in regions undergoing large shifts in land-use change. 53 Nowhere is this more true than in China, which combines a high level of native biodiversity 54 (Tao, Huang, Jin, & Guo, 2010) with a large human population that is increasing its 55 ecological footprint (Liu & Diamond, 2005; Pyne, 2013; Sayer & Sun, 2003; Xie et al., 56 2012). Moreover, for decades, China has had the managerial, political, and financial capacity 57 to implement the largest land-sustainability programs ever seen, from nature-reserve 58 protection to reforestation to de-desertification (Bryan et al., 2018; Liu et al., 2003; Wu et al., 59 2019; Xu, Wang, & Xue, 1999). These programs have caused major land-use changes and 60 successfully slowed land degradation caused by economic activities (Liu, Li, Ouyang, Tam & 61 Chen, 2008; Ouyang et al., 2016; Ren et al., 2015). For example, China established its first 62 nature reserve in 1956 and reached 2740 reserves at the end of 2015 (Ma, Shen, Grumbine, & 63 Corlett, 2017). Nearly two-thirds of the area of those nature reserves have national-level 64 status, meaning that they receive the highest level of protection and funding, and analysis of 65 Landsat imagery has shown that national-level reserves successfully deter deforestation (Ren 66 et al., 2015). 67 68 Two other major land-sustainability programs are the Natural Forest Protection Program 69 (NFPP, also known as Natural Forest Conservation Program) and the Grain for Green 70 Program (GFGP, also known as the Sloping Land Conservation Program and the Farm to 71 Forest Program), which were implemented after widespread flooding in 1998 (Liu et al., 72 2008; Xu, Yin, Li, & Liu, 2006; Yin, Yin, & Li, 2009). The NFPP aims to reduce soil erosion 73 and flooding by protecting native forests in the upstream watersheds of the Yangtze and 74 Yellow Rivers (Liu et al., 2008; Ren et al., 2015). The GFGP complements the NFPP by 75 controlling soil erosion on sloping land. The government pays cash and grain to farmers in 76 exchange for tree planting on sloping farmland (Delang &Yuan, 2015; Liu et al., 2008; Ma et 77 al., 2017; Xu et al., 2006; Zhai, Xu, Dai, Cannon, & Grumbine, 2014). Having reforested 78 9.06 million ha of cropland over 16 years (~2014) since its inception in 1999, the GFGP is 79 the world’s largest reforestation program. 80 81 However, relative to their scale and budgets, little is known about the biodiversity 82 consequences of China’s land-sustainability programs, even though an important and 83 expected co-benefit is biodiversity conservation (Wu et al., 2019). In a recent, massive 84 review, Bryan et al. (2018) were able to cite only one study on the consequences of China’s 85 large-scale reforestation programs for biodiversity, Hua et al. (2016). This paucity of 86 understanding contrasts starkly with the large volume of information on other consequences 87 of these programs: water and soil maintenance (Deng, Shangguan, & Li, 2012; Long et al., 88 2006; Wang, Peng, Zhao, Liu, & Chen, 2017; Wang, Jiao, Rayburg, Wang, & Su, 2016), 89 carbon storage (Deng, Liu, & Shangguan, 2014; Wei et al., 2014), vegetation cover (Hua et 90 al., 2018; Zhai et al., 2014; Zhou, Van Rompaey, & Wang, 2009), and socioeconomic

3 91 outcomes (Liu & Lan, 2015; Yin, Liu, Zhao, Yao, & Liu, 2014; Yin et al., 2009). A better 92 understanding of the biodiversity implications of reforestation programs is needed to guide 93 these programs for China and the rest of the world (Turner, Lambin, & Reenberg, 2007; 94 United Nations, 2015). 95 96 Guided by the goal of soil erosion control, and operating under the implicit assumption that 97 any type of tree cover should achieve this goal, the GFGP has predominantly established tree 98 plantations (‘plantations’ hereafter) on retired croplands, rather than restoring native forests 99 (Hua et al., 2016; Hua et al., 2018; Zhai et al., 2014). However, compared with native forest 100 ecosystems, plantations are known to support lower levels of biodiversity across the world’s 101 forest biomes and across taxa (Barlow, Overal, Araujo, Gardner, & Peres, 2007; Bremer & 102 Farley, 2010; Brockerhoff, Jactel, Parrotta, Quine, & Sayer, 2008; Gardner, Hernandez, 103 Barlow, & Peres, 2008; Lindenmayer & Hobbs, 2004), although certain management 104 regimes, such as maintaining understory structure and mixed cropping, can somewhat 105 increase biodiversity (Hartley, 2002). On the other hand, compared with croplands, 106 plantations are known to support different species assemblages, with potentially higher levels 107 of biodiversity, although there are indications that croplands in low-intensity agricultural 108 systems – which the croplands retired under GFGP tend to be (Hu, Fu, Chen, & Gulinck, 109 2006) – may support considerable biodiversity which potentially exceeds that associated with 110 plantations (Allan, Harrison, Navarro, Wilgen, & Thompson, 1997; Buscardo et al., 2008; 111 Elsen, Ramesh, & Wilcove, 2018). Together, these insights suggest that plantations should 112 have been expected to support low levels of biodiversity and that the GFGP could support 113 more biodiversity if it restored native forests. 114 115 Indeed, this is what Hua et al. (2016) found. They surveyed bird and bee communities in 116 GFGP-related tree covers in south-central Sichuan, comparing native-forest remnants to 117 GFGP-financed tree-cover types, which include monoculture stands of bamboo, Eucalyptus, 118 and Japanese cedar, as well as ‘mixed plantations,’ which are mostly patchworks 119 (checkerboards) of two to five different monocultures and, to a lesser extent, bona fide tree- 120 level mixtures (Hua et al., 2018). Most importantly, this study documented that bird and bee 121 species diversities were higher in native forests than in any of the monocultures. In addition, 122 they found that in mixed plantations, bird diversity for non-breeding species was higher than 123 in any of the individual monocultures, albeit lower than in native forests. In contrast, bee 124 diversity was equally low in mixed plantations and monocultures. The lack of a boost to bee 125 diversity in mixed plantations was not surprising, since as with monocultures, the understory 126 vegetation in mixed plantations was notably lacking in flowering plants (Hua et al., 2016). 127 128 The above findings, however, raise the question of why bird diversity was increased just by 129 planting monocultures of different tree species next to each other. One possibility that could 130 not be investigated in Hua et al. (2016) is that general arthropod diversity might also have 131 been boosted in the mixed plantations, since, unlike bees, other arthropods can exploit a 132 range of food resources available even in plantations, via direct consumption of plants and

4 133 fungi, and via decomposition, parasitism, and predation of other animals, including other 134 arthropods (Jactel & Brockerhoff, 2007). Increased arthropod diversity might in turn support 135 more bird diversity. In addition, as a large component of biodiversity, how arthropods 136 themselves (and subgroups thereof) are affected by the GFGP is an important part of 137 understanding the GFGP’s biodiversity effects. For instance, Barlow et al. (2007) compared 138 primary forest and Eucalyptus plantations in Brazil and found that birds achieve highest 139 diversity in primary forest, while bees have similar levels of species richness in primary 140 forest and Eucalyptus plantations. They also found that butterflies and dung beetles achieve 141 low diversity but that fruit flies and moths achieve high diversity in Eucalyptus plantations. 142 143 The purpose of this study is to test the generality of Hua et al.’s (2016) results by 144 interrogating the ‘rest of the biodiversity’ that was captured in the same sites analyzed by Hua 145 et al. (2016). We employ the technique of metabarcoding, which combines traditional DNA 146 barcoding with high-throughput DNA sequencing to characterize the biodiversity of mixed 147 samples of eukaryotes (Cristescu, 2014; Deiner et al., 2017; Yu et al., 2012), and which has 148 been shown to be a reliable and efficient method for biodiversity characterization (Ji et al., 149 2013). Through metabarcoding the non-bee arthropods caught in the same pan traps 150 previously used to trap bees in Hua et al. 2016, we hope to answer the following questions: 151 (1) Do native forests support higher levels of arthropod species richness and diversity than all 152 four GFGP plantations? (2) For all GFGP plantations, do mixed plantations support higher 153 levels of arthropod species richness and diversity than do the three individual monocultures? 154 (3) How does community composition compare among these tree covers and what underlies 155 the potential differences? 156 157 2 METHODS 158 2.1 Study location 159 The study region and locations are as in Hua et al. (2016). In short, our study region was a 2 160 7,949 km area in south-central Sichuan province (Figure 1) spanning 315–1,715 m above sea 161 level, historically forested and then deforested starting in the 1950s. The GFGP established 162 ~54,800 ha of new tree cover between 1999 and 2014, dominated by short-rotation (6-20 163 years) monocultures of bamboo (BB), Eucalyptus (EC), and Japanese cedar (JC), and short- 164 rotation mixed plantations (MP) of two to five tree species (including the three monoculture 165 species). Monocultures are created by households planting the same tree species in 166 neighboring landholdings. Correspondingly, mixed plantations are, in most cases, created by 167 planting different species, resulting in a checkerboard, although about a quarter of mixed 168 plantations consist of tree-level mixtures. In Hua et al. (2016), we used the term ‘mixed 169 forests’, but in Hua et al. (2018), we switched to ‘mixed plantations.’ 170 171 The two other surveyed land covers were croplands (CL) and native forests (NF). Croplands 172 mostly consist of low-intensity plantings of rice, corn, and vegetables and is the land-cover 173 type that has been reforested by GFGP. Native forests are broadleaf, subtropical, evergreen 174 forest that have been subject to decades of selective logging and other forms of extraction.

5 175 Because this region of China has been inhabited for millennia, there are no undisturbed native 176 forests. Croplands are typically located on flatter land than are the tree covers, since GFGP 177 reforestation targeted sloped land, and the native forests are concentrated toward the more 178 hilly, southern end of the study region. For sampling, we chose larger expanses (> 60 ha) of 179 these six land-cover types: BB, EC, JC, MP, NF, and CL. 180 181 2.2 Sampling design 182 Each land-cover type was represented by at least two locations set ≥15 km apart. All tree- 183 cover stands sampled had closed canopy. For each land-cover type, we sampled with at least 184 10 one-ha quadrats, within each of which we operated 40 fluorescent pan traps for 24 hrs 185 (Bartholomew & Prowell, 2005) (Fig. S1). In total, we sampled 74 quadrats (BB: 10, EC: 10, 186 JC: 12, MP: 10, NF: 16, CL: 16). Different quadrats were separated by 300 m if placed in 187 the same tree-cover stand. Samples were stored in 100% ethanol at ambient temperature until 188 shipment to the lab, where they were stored at -20 before DNA extraction. The original 189 reason for using pan traps had been to trap bees, which we individually DNA-barcoded in 190 Hua et al. (2016). Here we analyze the bycatch. 191 192 2.2 Amplicon preparation 193 For each of the 74 quadrats, we pooled all 40 pan traps into a single sample. Three quadrats 194 had very few individuals, and we pooled them with their nearest neighbor of the same land- 195 cover type (EC01+EC02+EC03; NF02+NF03), leaving us with 71 samples. Storage ethanol 196 was removed by air drying on single-use filter papers. Our samples were dominated by 197 Diptera and Hymenoptera, as expected. We equalized input DNA across species by using one 198 leg of every individual larger than a mosquito (~5 mm long) and the whole body if smaller 199 (e.g. midges). This was to reduce the effect of large-biomass individuals outcompeting small- 200 biomass individuals during PCR, which improves taxon detection (Elbrecht, Peinert, & 201 Leese, 2017). DNA extraction followed the protocols of Qiagen DNeasy Blood&Tissue Kits 202 (Hilden, Germany), followed by quantification via Nanodrop 2000 (Thermo Fisher Scientific, 203 Wilmington, DE). 204 We amplified a 319-bp fragment of COI using forward primer LCO1490 (5’- 205 GGTCAACAAATCATAAAGATATTGG-3’) and reverse primer mlCOIintR (5’- 206 GGNGGRTANANNGTYCANCCNGYNCC-3’) (Leray et al., 2013). All samples were 207 carried out with two rounds of PCR. In the first round, both forward and reverse primers were 208 tailed with tags (12-17 bp) for sample identification. In the second round, we added Illumina 209 adapters to the amplicons from the first PCR, thus avoiding the tag jumping that can arise 210 during library preparation of amplicon mixtures (Schnell, Bohmann, & Gilbert, 2015). A table 211 of tags and primers is in Supplementary Information (Table S1). All PCRs were performed on 212 a Mastercycler Pro (Eppendorf, Germany) in 20-µl reaction volumes, each containing 2 µl 2+ 213 10x buffer (Mg plus), 0.2 mM dNTPs, 0.4 µM of each primer, 1 µl DMSO, 0.4 µl BSA 214 (bovine serum albumin) (TaKaRa Biotechnology Co. Ltd, Dalian, China), 0.6 U exTaq DNA 215 polymerase (TaKaRa Biotechnology), and approximately 60 ng genomic DNA. Both rounds 216 of PCR started with an initial denaturation at 94 ℃ for 4 mins, followed by 35 cycles of

6 217 94 ℃ for 45s, 45 ℃ for 45s, 72 ℃ for 90s, and finishing at 72 ℃ for 10 mins. PCR products 218 were gel-purified with QIAquick PCR Purification Kit (Qiagen). One sample failed to 219 amplify. We pooled the 70 PCR products into two libraries and sequenced on the Illumina 220 MiSeq (Reagent Kit V3, 300PE) at the Southwest Biodiversity Institute Regional Instrument 221 Center in Kunming. The total number of paired-end reads returned was 13,601,908. 222 223 2.3 Data analyses 224 The bioinformatic script, including parameters, for the analyses below is in Supplementary 225 Information and will be archived in datadryad.org, along with sequence data and metadata. 226 The R scripts and data tables are on https://github.com/dougwyu/Sichuan2014. Below, R 227 packages are indicated with single quotes, and other software is italicized. 228 229 2.3.1 Bioinformatic processing 230 Initial processing. – We removed remnant Illumina adapter sequences with AdapterRemoval 231 2.2.0 (Schubert, Lindgreen, & Orlando, 2016), followed by Schirmer et al.’s (2015) pipeline 232 to filter, trim, denoise, and merge read pairs. Specifically, we trimmed low-quality ends using 233 sickle 1.33 (Joshi & Fass, 2011), corrected sequence errors using BayesHammer in SPAdes 234 3.10.1 (Nikolenko, Korobeynikov, & Alekseyev, 2013), and merged reads using PandaSeq 235 2.11 (Masella, Bartram, Truszkowski, Brown, & Neufeld, 2012), all with default parameters. 236 237 Demultiplexing and Clustering. – We then used QIIME 1.9.1’s split_libraries.py (Caporaso et 238 al., 2010) to demultiplex reads by sample and used usearch 9.2.64 (Edgar, 2010) to retain 239 reads between 300 and 330 bp, inclusive, since our amplicon is 319 bp. We used vsearch 240 2.4.3 (Rognes, Flouri, Nichols, Quince, & Mahé, 2016) for de-novo chimera removal and 241 used CROP 1.33 (Hao, Jiang, & Chen, 2011) to cluster the remaining reads at 97%-similarity. 242 This step produced 3,507 OTUs. We also tried swarm 2.2.2 (Mahé, Rognes, Quince, Vargas, 243 & Dunthorn, 2015), but it returned huge numbers of OTUs that could not be reduced even 244 after running through ‘lulu’ (see below). 245 246 OTU filtration and taxonomic assignment. – From the resulting sample X OTU table, we 247 used ‘lulu’ 0.1.0 (Frøslev et al., 2017) to combine OTUs that were likely from the same 248 species but which had failed to be clustered by CROP. ‘lulu’ identifies such ‘parent-child’ 249 sets by calculating pairwise similarities of all OTUs (using vsearch) to identify sets of high- 250 similarity OTUs and then combining OTUs within such sets that show nested sample 251 distributions. For example, four OTUs might be highly similar, and within this set of four, 252 one OTU contains the most reads and is observed in ten samples. This OTU is the parent, and 253 daughters are inferred if they are present in a subset of the parent’s samples. We ended with 254 1,506 OTUs. 255 256 A common filtering step is to remove OTUs made up of few reads (e.g. 1-read OTUs), as 257 these are more likely to be artefactual (e.g. Yu et al., 2012, Zepeda-Mendoza et al., 2016). For 258 instance, PCR errors can generate clusters of sequences that are sufficiently different from the

7 259 parent that they cannot be identified as daughters. Such OTUs are more likely to be small 260 because novel haplotypes typically arise in a later PCR cycle. However, the definition of 261 small is subjective and differs with the size of the sequence dataset. We therefore used 262 ‘phyloseq’ 1.19.1 (McMurdie & Holmes, 2013) to plot the number of OTUs that would be 263 filtered out at different minimum OTU sizes (see http://evomics.org/wp- 264 content/uploads/2016/01/phyloseq-Lab-01-Answers.html, accessed 19 July 2018), and we 265 chose a minimum OTU size of 44 reads, which was roughly the graph’s inflection point and 266 thus filtered out the most OTUs for the lowest minimum size. We ended with 594 OTUs. 267 268 We then used PyNAST 1.2.2 to align the 594 OTU sequences to a reference alignment of 269 Arthropoda COI sequences (Yu et al., 2012) at a minimum similarity of 60%; one sequence 270 failed to align and was deleted. The remaining sequences were translated to amino acids 271 using the invertebrate mitochondrial codon table, and we removed 32 OTUs with sequences 272 that contained stop codons. We carried out taxonomic assignment of the OTUs using a Naïve 273 Bayesian Classifier (Wang, Garrity, Tiedje, & Cole, 2007) trained on the Midori UNIQUE 274 COI dataset (Machida, Leray, Ho, & Knowlton, 2017). Sixteen OTUs assigned to non- 275 Arthropoda taxa and two OTUs assigned to Collembola were removed. We ended with 543 276 OTUs. 277 278 Finally, we inspected the OTU table and set to zero those cells that had <5 reads representing 279 that OTU in that sample, since these were more likely to be the result of sequencing error (Yu 280 et al., 2012). In addition, we removed two samples (rows) that contained ≤100 reads total (i.e. 281 samples with little data) and removed seven samples (rows) with <5 OTUs because these 282 samples were potentially overly influential in analyses of species richness. These seven 283 samples included two from native forests and five from monocultures (3 BB, 1 EC, 1 JC), 284 meaning that we disproportionately removed monocultures, making our species diversity 285 analyses below more conservative. After these sample removals, seven OTUs were removed 286 because they were left with few (<20) reads. Because we do not consider OTU size to be 287 reliable measures of biomass or abundance (Nichols et al., 2018; Piñol, Mir, Gomez-Polo, & 288 Agustí, 2015; Yu et al., 2012), we converted the OTU table into a presence/absence (0/1) 289 dataset. Throughout, our bias was to remove false-positive detections even at the expense of 290 losing true-positive detections, thereby resulting in a dataset with less, but more reliable (and 291 thus more replicable), data. We ended with 536 OTUs and 61 samples. 292 293 2.3.2 Community analysis 294 OTU richness and diversities. – All community analyses were performed in R 3.3.3 (R Core 295 Team, 2017). We estimated species richness and Shannon and Simpson diversities using two 296 sample-based estimators: function specpool in ‘vegan’ 2.4-5 (Chiu, Wang, Walther, & Chao, 297 2014) and ‘iNEXT’ 2.0.12 (Hsieh, Ma, & Chao, 2016). 298 299 OTU phylogenetic diversities. – Because we used a combination of CROP+‘lulu’ and 300 ‘phyloseq’ to combine and remove small OTUs that were likely to be artefactual, the

8 301 remaining OTUs were more likely to represent true presences. Nonetheless, it remained 302 possible that we had over-split some biological species into multiple OTUs, since there is no 303 single correct similarity threshold for species delimitation, and this oversplitting might have 304 occurred more often for some taxa in some land-cover types, leading to artifactual differences 305 in species richness. However, oversplit OTUs should cluster together in a phylogenetic tree 306 and thus contribute less to estimates of phylogenetic diversity than would OTUs from 307 different biological species. Phylogenetic diversity should thus be a robust estimator of alpha 308 diversity (Yu et al., 2012). To estimate sample phylogenetic diversities, we used ‘iNextPD’ 309 0.3.2 (Hsieh & Chao, 2017). We built a maximum-likelihood (ML) tree in RaxML 8.0.0 310 (Stamatakis, 2014) with an alignment of the OTU-representative sequences, using a General 311 Time Reversible (GTR) model of nucleotide substitution and a gamma model of rate 312 heterogeneity estimating the proportion of invariable sites (-m GTRGAMMAI). The 313 algorithm used a rapid bootstrap analysis and searched for the best-scoring ML tree (-f a), 314 with -N 1000 times bootstrap and -p 12345 as the parsimony random seed. Three OTU 315 sequences produced very long branches in the ML tree, which would skew estimates of 316 phylogenetic diversity, and we removed them. Two of these OTUs were found in all land- 317 cover types (and thus would not have been informative), and one was only found in some 318 cropland samples (and thus would not have informed analyses of the tree-cover sites). 319 320 Beta diversity. – To visualize changes in community composition across land-cover types, we 321 ran a Bayesian ordination with ‘boral’ 1.6.1 (Hui, 2016), which is more statistically robust 322 than non-metric multidimensional scaling (NMDS) analysis because ‘boral’ is model-based 323 and thus allows us to apply a suitable error distribution so that fitted-model residuals are 324 properly distributed. We used a binomial error distribution and no row effect since we were 325 using presence/absence data (Figure S5). For the same reasons, we used ‘mvabund’ 3.12.3 326 (Wang, Naumann, Wright, & Warton, 2012) to test the hypotheses that native forests and 327 mixed plantations differ compositionally from each other and differ from the monocultures 328 and croplands. 329 330 We also visualized changes in community composition with an ‘UpSetR’ 1.3.3 intersection 331 diagram, an alternative to Venn diagrams (Conway, Lex, & Gehlenborg, 2017), with a 332 heatmap using the tabasco function in ‘vegan’, and with a ‘betapart’ 1.4-1 (Baselga & Orme, 333 2012) analysis, which partitions beta diversity into turnover and nestedness components using 334 binary Jaccard dissimilarities, which we visualized with NMDS using the metaMDS function 335 in ‘vegan’. Finally, we used ‘metacoder’ 0.2.0 (Foster, Sharpton, & Grunwald, 2017) to 336 generate taxonomic ‘heat trees’ to pairwise-compare the six land-cover types and identify the 337 taxa most strongly driving compositional differences. 338

9 339 3 RESULTS 340 341 3.1 Alpha diversity 342 Species richness and diversity are highest in native forests and croplands, followed by mixed 343 plantations, which are in turn richer and more diverse than the monoculture plantations, with 344 the possible exception of bamboo. 345 346 OTU richness and diversities. – The Chao2 estimator indicates that native forests, mixed 347 plantations, and croplands have the highest estimated species richnesses and do not differ 348 significantly from each other (Figure 2a). Importantly, all three monocultures (bamboo, 349 Eucalyptus, and Japanese cedar) exhibit less than half the species richness of native forests 350 and around half the species richness of mixed plantations (Figure 2a). The pairwise 351 differences between native forests and monocultures are all statistically significant (Table 352 S2), and the pairwise differences between mixed plantations and the three monocultures are 353 marginally or significantly different (Figure 2a, Table S2), all after table-wide correction. 354 355 The iNEXT analysis reveals even clearer contrasts: native forests have the highest estimated 356 asymptotic species richnesses and Shannon diversities, followed by croplands and mixed 357 plantations, followed by the three monocultures (Figures 2b, S3). The iNEXT-estimated 358 richness and diversity of mixed plantations are significantly higher than all the monocultures, 359 with the possible exception of bamboo, because the MP and BB confidence intervals touch. 360 361 Phylogenetic diversities. – The iNextPD analysis mirrors the iNEXT results (Figures 2b, S4). 362 Using ‘iNextPD’ to visualize phylogenetic coverage by land-cover type (Figure 3) reveals 363 that native forests and croplands exhibit almost complete coverage of the OTU tree, whereas 364 mixed plantations and bamboo exhibit some coverage deficits, followed by larger coverage 365 deficits in the other two monocultures. 366 367 3.2 Beta diversity 368 Native forests are compositionally most similar to mixed plantations and most dissimilar to 369 croplands. The differences in community composition are driven primarily by species 370 turnover. 371 372 Differences in community compositions. Ordination with ‘boral’ (Figure 4a) shows that the 373 primary separation is between the tree cover types and croplands, with a significantly positive 374 correlation between latent variable 1 and elevation (r = -0.457, df = 59, p = 0.0002). The 375 cropland sites themselves cluster into two groups by elevation. Latent variable 2 largely 376 separates Eucalyptus monoculture from the other tree-cover types, which might reflect its 377 distinct phytochemistry. Importantly, the mixed-plantation and (most of) the native-forest 378 sites overlap and are encircled by the monocultures, indicating that native forests and mixed 379 plantations are compositionally most similar. 380

10 381 The ‘UpSetR’ intersection diagram (Figure 4b) is consistent with the diversity analyses 382 (Figures 2, S3, S4): native forests (110 OTUs) and croplands (130 OTUs) support more than 383 2.5 times the number of ‘unique species’ (species detected in only one land-cover type) than 384 any of the plantations, and secondly, of the plantations, mixed plantations support the highest 385 number of unique species (44 OTUs). The greater compositional similarity that native forests 386 have with mixed plantations (Figure 4a) is displayed by native forests uniquely sharing more 387 OTUs with mixed plantations (22 OTUs) than with any of the monocultures (13, 9, and 5). 388 However, despite their overlap, ‘mvabund’ analysis shows that the arthropod communities of 389 mixed plantations and native forests are still significantly distinct from each other, and from 390 the three monocultures and croplands (Table S3). 391 392 Turnover versus nestedness. – Consistent with the UpSetR result that the mode in each land- 393 cover type is unique species, we found that turnover, not nestedness, dominates 394 compositional differences (Figure 5; see Figure S7 for a heatmap visualisation). In other 395 words, the arthropod communities in the monocultures are not simply subsets of native 396 forests or mixed plantations but contain distinct sets of species. 397 398 Taxonomic compositions of and differences between land-cover types. – The 536 arthropod 399 species in our metabarcoding dataset represent a wide range of arachnid and insect orders and 400 thus, represent a wide range of ecological functions (Figure 6), including generalist predators 401 (Araneae, Formicidae) and more specialized parasites and parasitoids (Tachinidae, Phoridae, 402 Braconidae) of other arthropods. We also observe taxa that are noted for pollination 403 (Thysanoptera, Syrphidae), xylophagy (Isoptera), and various modes of detritivory, 404 fungivory, frugivory, herbivory, and animal parasitism (Lepidoptera, Hemiptera, Diptera, 405 Orthoptera, Formicidae, Thysanoptera). 406 407 Although the ‘boral’ ordination (Figure 4a) reveals compositional similarity between mixed 408 plantations and native forests, it does not reveal the taxa that are most responsible for this 409 similarity, and for the differences with the other tree-cover types. With ‘metacoder’ heat trees 410 (Figure 6 inset), we can identify the taxa that are driving this similarity and the differences, 411 and what we see is that mixed plantations and native forests ‘differ in the same ways’ from 412 the monocultures. (1) Relative to bamboo, mixed plantations and native forests both have 413 slightly more Lepidoptera OTUs. (2) Relative to Eucalyptus, mixed plantations and native 414 forests both have more Diptera OTUs and fewer of the three OTUs assigned to genera 415 Mycetophila, Sonema, and Homaloxestis, which can be taken as Eucalyptus indicator species. 416 (3) Finally, relative to Japanese cedar, mixed plantations and native forests both have more 417 Araneae and Lepidoptera OTUs, fewer Hemiptera OTUs, and fewer of the OTU assigned to 418 Mycetophila. Heat-tree differences at higher taxonomic ranks (e.g. more Araneae-assigned 419 OTUs) mean that the species which separate the two land-cover types differ across samples 420 but nonetheless are in the same higher taxon (e.g. Araneae). Finally, when we include 421 croplands in the heat-tree comparisons (Figure S8), we observe the largest number of heat- 422 tree-tip differences between any two land-cover types. In other words, there are multiple

11 423 species-level indicators of croplands (or in the case of the Mycetophila OTU, an indicator of 424 Japanese cedar and Eucalyptus). 425 426 4 DISCUSSION 427 428 Improving biodiversity conservation under the GFGP 429 430 Our study found that native forests support the highest levels of arthropod species richness, 431 Shannon and Simpson diversity, and Faith’s and phylogenetic diversity (Figures 2, 3, S3, S4) 432 and that most of those species are unique to native forests (Figure 4b), consistent with the 433 patterns of bird diversity that were reported in Hua et al. (2016) and other biodiversity studies 434 in plantations (Barlow et al., 2007; Gardner et al., 2008). In addition, our findings pertaining 435 to the higher level of alpha diversity in mixed plantations over monocultures (Figures 2, S3, 436 S4), and their greater degree of compositional similarity to native forests relative to 437 monocultures (Figures 4, 5, 6, S6, S7, S8), corroborate those reported for birds (but not bees) 438 in Hua et al. (2016) and are consistent with other studies of biodiversity in tree plantations. 439 Butterfield and Malvido (1992) showed that mixtures of broadleafs and conifers resulted in a 440 higher species richness of carabid beetles than in conifer monocultures, and Recher et al. 441 (1987) showed that some bird species are present when in Eucalyptus-pine mixtures but 442 absent from pine monocultures. In short, mixed plantations not only support a higher 443 diversity of non-breeding birds but also provide a small but detectable biodiversity boost for 444 arthropods. Finally, we found that compositional differences amongst tree-cover types are 445 almost entirely dominated by species turnover, not nestedness, meaning that some species 446 were only detected in the monocultures. This result is consistent with the pattern of moth 447 communities in primary, secondary and plantation forests studied by Hawes et al. (2009). In 448 their findings, all three of their tree-cover types (primary and secondary forest, Eucalyptus 449 plantation) contained large numbers of unique species in three moth families (Arctiidae, 450 Saturniidae, Sphingidae). 451 452 Given the balance of evidence, we re-affirm our previous policy recommendations that the 453 GFGP should prioritize the retention and restoration of native forests, and when restoring 454 native forests is not possible, we secondarily encourage mixed-species plantings over 455 extensive monocultures, at least in western China where we conducted this study. The 456 foundation of these recommendations is now broadened to include 536 species-resolution 457 taxa ranging across the Arthropoda. Given the growing understanding of the biodiversity 458 implications of plantations compared with native forests in different forest biomes across the 459 world (Bremer & Farley, 2010; Fierro, Grez, Vergara, Ramírez-Hernández, & Micó, 2017), 460 these recommendations likely apply to other regions in China where GFGP is relevant, but 461 their applicability will benefit from additional field studies and from anticipated technical 462 advances in DNA-based biodiversity assessment. In the future, it will likely be insightful to 463 carry out time-series biodiversity surveys, since our dataset represents only a single time 464 point, but the temporal turnover of forest arthropod communities is high (Barsoum et al.,

12 465 2019). It is possible that the differences in biodiversity levels that we have detected are even 466 stronger when integrated over time. Another important variable that we did not measure is 467 sample biomass, given recent evidence that insect biomass has been dropping around the 468 world (e.g. Hallmann et al., 2017). Because we observed high species richness and diversity 469 in our cropland sampling sites (Figures 2, 3, S3, S4), where agriculture is small-scale in 470 nature, our a priori expectation is that biomass has probably not declined here as rapidly as 471 elsewhere, but this clearly needs testing and should of course now be a standard metric in 472 biodiversity surveys. 473 474 Greater levels of arthropod biodiversity in native forest is not a surprise, given their more 475 diverse vegetation structures and species compositions, which are well known to be 476 positively correlated with arthropod diversity (Castagneyrol & Jactel, 2012; Haddad et al., 477 2009; Stork, Mcbroom, Gely, & Hamilton, 2015; Zhang et al., 2016), but the greater diversity 478 and similarity of mixed plantations to native forests is somewhat surprising, especially since 479 they mostly just comprise small-scale monocultures, planted in checkerboard pattern. 480 However, planting different tree species near each other not only provides more diverse 481 vegetation per se but also, because the species vary in height and three-dimensional structure, 482 almost certainly allow greater sunlight penetration to the understory, which in turn should 483 result in greater availability of food and other resources. This mechanism is consistent with 484 our finding that bamboo, which does not create closed canopies, exhibits the highest richness 485 and diversity of the monocultures (Figures 2, S3, S4). We note that 95% confidence-interval 486 overlap is considered an overly conservative test for statistical significance at the p=0.05 487 level (MacGregor-Fors & Payton, 2013). A more diverse, and presumably higher-biomass, 488 arthropod community in turn could also support a richer bird community, at least for the 489 insectivorous subset of the community. Our results thus point to a plausible mechanism for 490 why bird diversity is boosted in mixed plantations. 491 492 In this study, we report evidence for a biodiversity benefit of native forests over GFGP 493 plantations, which we might think trades off against a greater value of timber sales from 494 plantations. However, even excluding biodiversity, which they did not study, Cao et al. (2019) 495 have recently shown that plantations in China also return a lower net value of other 496 ecosystem services relative to native forests, even after counting income from timber sales. 497 Plantations require a high initial outlay for tree planting, some non-native tree species like 498 Eucalyptus require more water input than do native tree species, and more management effort 499 is required to protect plantations from pest attack. In contrast, timber sale values are low. Cao 500 et al.’s findings complement and strengthen our recommendation (Hua et al., 2016) to 501 prioritize native forest recovery and expansion over creating plantations. 502 503 Methodological comments on metabarcoding and studies of biodiversity patterns. 504 505 Metabarcoding provides an efficient method for interrogating biodiversity samples, but 506 because of its reliance on PCR, metabarcoding datasets tend to contain a non-trivial amount

13 507 of noise. This noise manifests as a large number of false-positive OTUs, which are filtered 508 out heuristically. Such false OTUs especially complicate efforts to estimate alpha diversity. 509 Here, we applied several filtering steps to remove false OTUs, and we also used ‘iNextPD’ to 510 generate robust comparisons of alpha diversity by estimating phylogenetic diversity instead 511 of species richness. This approach has been previously shown to be reliable (Yu et al., 2012). 512 Another approach, which became available only after we had completed the wet-lab portion 513 of our study, is to subject each sample to multiple, independently tagged PCRs (typically 514 three) and to bioinformatically filter out sequences that fail to appear in at least two of the 515 PCRs above some minimum number of reads; such sequences are more likely to be PCR or 516 sequencing errors. This is implemented in the DAMe protocol of Zepeda-Mendoza et al. 517 (2016, also see Alberdi, Aizpurua, Gilbert, & Bohmann, 2018). 518 519 With regard to studies of biodiversity patterns, we follow Magurran et al. (2015; Magurran, 520 2016) in recommending that we should focus less on explaining change in species richness 521 and more on explaining change in species composition as a function of natural and 522 anthropogenic causes. The argument is that anthropogenically disturbed communities can 523 maintain species richness and even phylogenetic diversity, even as local, or worse still, 524 endemic, species go extinct and are replaced by cosmopolitan species. In our study, croplands 525 support an arthropod community similar in richness and diversity to that of mixed plantations 526 and just below that of native forests (Figures 2, 3, 4b, S3, S4), but the species composition of 527 croplands is distinct from those in native forests (Figures 4, 5, S6, S7, S8). Croplands 528 therefore cannot compensate for the loss of the biodiversity dependent on native forests. 529 530 REFERENCES 531 Alberdi, A., Aizpurua, O., Gilbert, M. T. P., & Bohmann, K. (2018). Scrutinizing key steps for reliable 532 metabarcoding of environmental samples. Methods in Ecology and Evolution, 9, 134–147. 533 https://doi.org/10.1111/2041-210X.12849 534 Allan, D. G., Harrison, J. A., Navarro, R. A., Wilgen, B. W. Van, & Thompson, M. W. (1997). The impact of 535 commercial afforestation on bird populations in Mpumalanga Province, South Africa—insights from bird- 536 atlas data. Biological Conservation, 79, 173–185. 537 Barlow, J., Gardner, T. A., Araujo, I. S., Avila-Pires, T. C., Bonaldo, A. B., Costa, J. E., … Peres, C. A. (2007). 538 Quantifying the biodiversity value of tropical primary, secondary, and plantation forests. PNAS, 104, 539 18555–18560. 540 Barlow, J., Overal, W. L., Araujo, I. S., Gardner, T. A., & Peres, C. A. (2007). The value of primary, secondary 541 and plantation forests for fruit-feeding butterflies in the Brazilian Amazon. Journal of Applied Ecology, 542 44, 1001–1012. 543 Barsoum, N., Bruce, C., Forster, J., Ji, Y., Yu, D. W., Lodge, A. H., … Kingdom, U. (2019). The devil is in the 544 detail: Metabarcoding of arthropods provides a sensitive measure of biodiversity response to forest stand 545 composition compared with surrogate measures of biodiversity. Ecological Indicators, 101, 313–323. 546 https://doi.org/10.1016/j.ecolind.2019.01.023 547 Bartholomew, C. S., & Prowell, D. (2005). Pan compared to malaise trapping for bees (Hymenoptera: Apoidea) 548 in a longleaf pine savanna. Journal of the Kansas Entomological Society, 78, 390–392.

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19 759 Metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods in Ecology 760 and Evolution, 3, 613–623. https://doi.org/10.1111/j.2041-210X.2012.00198.x 761 Zepeda-Mendoza, M. L., Bohmann, K., Carmona Baez, A., & Gilbert, M. T. P. (2016). DAMe: A toolkit for the 762 initial processing of datasets with PCR replicates of double-tagged amplicons for DNA metabarcoding 763 analyses. BMC Research Notes, 9, 1–13. https://doi.org/10.1186/s13104-016-2064-9 764 Zhai, D. L., Xu, J. C., Dai, Z. C., Cannon, C. H., & Grumbine, R. E. (2014). Increasing tree cover while losing 765 diverse natural forests in tropical Hainan, China. Regional Environmental Change, 14, 611–621. 766 https://doi.org/10.1007/s10113-013-0512-9 767 Zhang, K., Lin, S., Ji, Y., Yang, C., Wang, X., Yang, C., … Yu, D. W. (2016). Plant diversity accurately 768 predicts insect diversity in two tropical landscapes. Molecular Ecology, 25, 4407–4419. 769 https://doi.org/10.1111/mec.13770 770 Zhou, H., Van Rompaey, A., & Wang, J. (2009). Detecting the impact of the “Grain for Green” program on the 771 mean annual vegetation cover in the Shaanxi province, China using SPOT-VGT NDVI data. Land Use 772 Policy, 26, 954–960. https://doi.org/10.1016/j.landusepol.2008.11.006 773

20 774 Data Accessibility 775 Sequence data have been submitted to GenBank under accession number SAMN09981891. 776 Bioinformatic scripts are in Supplementary Information. R scripts and data tables are 777 available at https://github.com/dougwyu/Sichuan2014. 778

21 779 Figure legends 780 781 Figure 1. Study area in south-central Sichuan province, subdivided into counties and shaded 782 by elevation. Each cross represents a pan-trap sampling location, color-coded by land-cover 783 type: BB = bamboo monoculture, blue; EC = Eucalyptus monoculture, light green; CL = 784 croplands, orange; JC = Japanese cedar monoculture, red; MP = mixed plantations, purple; 785 NF = native forests, dark green. 786 787 Figure 2. Species richness estimates across land-cover type. (a) Comparisons of Chao2 788 species richness estimates. Land-cover types sharing the same superscript are not 789 significantly different at the p=0.05 level (Welch’s t-test) after table-wide correction for 790 multiple tests (Bonferroni). (b) ‘iNEXT’ estimates of species richness, Shannon diversity, and 791 ‘iNextPD’ estimates of phylogenetic diversity by land-cover type, using sample-based 792 rarefaction and extrapolation. Native forests (NF) have the highest species richness and 793 diversities, followed by croplands (CL) and mixed plantations (MP), followed by the three 794 monoculture plantations (BB, EC, and JC). Codes for land-cover types as in Figure 1. 795 Symbols on each curve indicate the number of sampled locations per land-cover type, solid 796 lines represent interpolations, and dashed lines represent extrapolations, with 95% confidence 797 intervals. Statistically significant pairwise differences are detected visually by non- 798 overlapping confidence intervals and are considered conservative (MacGregor-Fors & 799 Payton, 2013). Full iNEXT and iNextPD figures are in S3 and S4. 800 801 Figure 3. Phylogenetic distribution of OTUs by land-cover type, created using ‘iNextPD’. 802 Terminal nodes are black and represent the OTUs. Internal nodes are white. Sizes of the 803 squares on the right indicate each OTU’s incidence frequency (number of samples in which 804 the OTU is observed). Phylogenetic coverage is most complete in native forests (NF) and 805 croplands (CL), followed by mixed plantations (MP), followed by the three monocultures 806 (BB, EC, JC). Codes for land-cover types as in Figure 1. 807 808 Figure 4. Community composition differences in all land-cover types. (a) ‘Boral’ ordination. 809 Colors represent land-cover types, and numbers represent individual samples. Cropland (CL) 810 sites separate into two clusters by elevation. Overlap of native forests (NF) and mixed- 811 plantations (MP) points indicates greater compositional similarity between these two land- 812 cover types. Ovals manually added to visualize community groupings. Residuals of the 813 ‘boral’ fit in Fig. S5. (b) UpSetR intersection map of OTUs unique to and shared between and 814 among land-cover types. Croplands and native forests support the highest numbers of unique 815 OTUs (CL=130, NF=110), followed by the four plantations (MP=44, BB=37, EC=31, 816 JC=27). Native forests uniquely share almost as many OTUs with mixed plantations (22 817 OTUs) as native forests share with the three monocultures combined (27 OTUs, =13+9+5). 818 Horizontal bars on the left indicate the total number of OTUs in each land-cover class. Codes 819 for land-cover types as in Figure 1. For clarity, only pairwise comparisons are shown. A non- 820 truncated version is presented in Fig. S6.

22 821 822 Figure 5. NMDS (non-metric multidimensional scaling) ordination of beta diversity by land- 823 cover type (binary Jaccard dissimilarities), partitioned with ‘betapart’. (a) Total beta diversity. 824 (b) Beta diversity based on species turnover only. (c) Beta diversity based on species 825 nestedness only. Turnover accounts for most the observed beta diversity across land-cover 826 types, which is visualized as greater distances between points in the turnover figure (b) and 827 almost no distances between points in the nestedness figure (c). Codes for land-cover types as 828 in Figure 1. 829 830 Figure 6. Pairwise taxonomic comparisons of all land-cover types. Upper right triangle: 831 greener branches indicate taxa that are relatively more abundant (in numbers of OTUs) in the 832 land-cover types along the right column, and browner branches indicate taxa that are 833 relatively more abundant in the land-cover types along the top row. Lower left: taxonomic 834 identities of the branches. Note that this is a taxonomic tree, not a phylogenetic tree. Legend: 835 width indicates number of OTUs at a given taxonomic rank, and color indicates relative

836 differences in log2(number of OTUs). Codes for land-cover types as in Figure 1. A figure 837 including croplands and a zoomable taxonomic tree is in supplementary information (Figure 838 S8, S9). 839

23 840 Figure 1.

841

842 Figure 1. Study area in south-central Sichuan province, subdivided into counties and shaded 843 by elevation. Each cross represents a pan-trap sampling location, color-coded by land-cover 844 type: BB = bamboo monoculture, blue; EC = Eucalyptus monoculture, light green; CL = 845 croplands, orange; JC = Japanese cedar monoculture, red; MP = mixed plantations, purple; 846 NF = native forests, dark green.

24 847 Figure 2

600 b (a) (b)

Species0 richness Shannon1 diversity Simpson2 diversityFaith’s0 PD Phylogenetic1 entropy Rao’s quadratic2 entropy Faith’s0 PD Phylogenetic1 entropy Rao’s quadratic2 entropy

600 b,cb 80 80

500 NF NF b,c NF 300 500

60 MP 60 MP 400 Guides b,d NF Guides BB Guides CL BB 400 b,d CL BB CL CL CL EC CL NF EC NF JC EC 200 BB JC MP MP JC BB MP MP NF NF MP

300 40 300 40 NF JC NF MP a,c,d a,c,d JC MP CL Method BB Method a,c Species diversity MP interpolationMethod CL a,c CL EC NF interpolation

BB diversity Phylogenetic Species richness estimates EC NF extrapolationinterpolation BB Phylogenetic diversity Phylogenetic BB extrapolation 200 NA extrapolation Species richness estimates JC JC CL EC 100 EC

200 CL a 20 BB CL 20 EC MP MP JC EC EC JC JC EC a EC BB BB 100 JC JC 100 0 0 0 0 0 10 20 30 0 10 20 30 0 0 10 10 20 2030 30 0 10 20 30 0 10 20 30 BB EC JC MP CL NF 0 10 20 30 0 10 20 30 0 10 20 30 NumberNumber of sampling of sampling units units Number of sampling units 848 0

849 Figure 2. SpeciesBB richnessEC JC estimatesMP acrossCL landNF-cover type. (a) Comparisons of Chao2 species richness estimates. Land-cover types sharing the 850 same superscript are not significantly different at the p=0.05 level (Welch’s t-test) after table-wide correction for multiple tests (Bonferroni). (b) 851 ‘iNEXT’ estimates of species richness, Shannon diversity, and ‘iNextPD’ estimates of phylogenetic diversity by land-cover type, using sample- 852 based rarefaction and extrapolation. Native forests (NF) have the highest species richness and diversities, followed by croplands (CL) and mixed 853 plantations (MP), followed by the three monoculture plantations (BB, EC, and JC). Codes for land-cover types as in Figure 1. Symbols on each 854 curve indicate the number of sampled locations per land-cover type, solid lines represent interpolations, and dashed lines represent 855 extrapolations, with 95% confidence intervals. Statistically significant pairwise differences are detected visually by non-overlapping confidence 856 intervals and are considered conservative (MacGregor-Fors & Payton, 2013). Full iNEXT and iNextPD figures are in S3 and S4.

25 857 Figure 3 BB CL EC JC MP NF Cluster726828Cluster96058Cluster168999Cluster1044930Cluster1191583Cluster1120269 Cluster852926Cluster317705Cluster261786Cluster641699Cluster321338Cluster105406 Cluster532117Cluster574237Cluster724647Cluster1160615Cluster515604Cluster970338 Cluster14910Cluster236674Cluster1008496Cluster310116Cluster638434Cluster662085 Cluster201249Cluster18504Cluster1117056Cluster593252Cluster8795Cluster449561 Cluster515093Cluster762239Cluster519998Cluster1043256Cluster413866Cluster761967 Cluster1036793Cluster552615Cluster866530Cluster320933Cluster1014195Cluster148932 Cluster381399Cluster153235Cluster1322Cluster1147433Cluster360587Cluster1262671 Cluster283192Cluster549932Cluster487185Cluster292170Cluster1035165Cluster1027064 Cluster605186Cluster556802Cluster652901Cluster1078541Cluster273441Cluster190091 Cluster638055Cluster1190248Cluster1035399Cluster554946Cluster667421Cluster927147 Cluster254474Cluster999788Cluster554866Cluster170040Cluster1052098Cluster970757 Cluster1226805Cluster69978Cluster346941Cluster666290Cluster509685Cluster563424 Cluster866010Cluster794054Cluster551260Cluster603666Cluster258891Cluster641821 Cluster1264742Cluster62672Cluster1218901Cluster588978Cluster1156Cluster742617 Cluster1111802Cluster1228370Cluster246310Cluster374617Cluster807777Cluster185105 Cluster1102602Cluster909388Cluster63348Cluster731000Cluster334862Cluster1252116 Cluster628263Cluster465625Cluster271497Cluster723910Cluster248703Cluster987637 Cluster91284Cluster681238Cluster599965Cluster868323Cluster469416Cluster722821 Cluster1251081Cluster866046Cluster7237Cluster648953Cluster582971Cluster162949 Cluster680855Cluster260416Cluster1118816Cluster759071Cluster454109Cluster236078 Cluster638387Cluster1147021Cluster691418Cluster899998Cluster885812Cluster405715 Cluster957758Cluster571940Cluster320422Cluster893773Cluster368371Cluster735119 Cluster689312Cluster779302Cluster896917Cluster139804Cluster635994Cluster881883 Cluster914193Cluster1058962Cluster482725Cluster375655Cluster336931Cluster956580 Cluster313551Cluster1271357Cluster199147Cluster975075Cluster946116Cluster283921 Cluster1185877Cluster881319Cluster815919Cluster900429Cluster633345Cluster292040 Cluster313794Cluster1262791Cluster802315Cluster297807Cluster871445Cluster238661 Cluster190691Cluster777026Cluster403990Cluster300625Cluster1212246Cluster2299 Cluster1082093Cluster193554Cluster453676Cluster571783Cluster993654Cluster604765 Cluster141583Cluster160348Cluster319598Cluster317158Cluster781271Cluster629282 Cluster550517Cluster666208Cluster657934Cluster941879Cluster337634Cluster144694 Cluster829582Cluster95070Cluster900613Cluster1046555Cluster568976Cluster71294 Cluster451245Cluster1160549Cluster1244964Cluster312482Cluster680471Cluster781374 Cluster1116607Cluster89973Cluster932946Cluster1034115Cluster405735Cluster159898 Cluster860679Cluster466755Cluster975205Cluster1219908Cluster263522Cluster487820 Cluster866003Cluster1234815Cluster795289Cluster231396Cluster825580Cluster555842 Cluster264960Cluster31249Cluster705838Cluster1205684Cluster785061Cluster279779 Cluster96099Cluster1061819Cluster616735Cluster437190Cluster566789Cluster953387 Cluster421090Cluster714500Cluster8809Cluster89960Cluster10088Cluster435585 Cluster517616Cluster375548Cluster425852Cluster346275Cluster428661Cluster492428 Cluster872255Cluster1043055Cluster411007Cluster499788Cluster726965Cluster22396 Cluster1186330Cluster785398Cluster955298Cluster894190Cluster675258Cluster16393 Cluster1263180Cluster120067Cluster1115775Cluster933654Cluster188440Cluster957273 Cluster363044Cluster693469Cluster4912Cluster125385Cluster933936Cluster193702 Cluster1189880Cluster378570Cluster832583Cluster497237Cluster549198Cluster327486 Cluster881515Cluster356106Cluster354297Cluster1270869Cluster119182Cluster888658 Cluster1094626Cluster791843Cluster660889Cluster852624Cluster870147Cluster772602 Cluster1154095Cluster1031263Cluster120982Cluster27114Cluster528954Cluster1112567 Cluster466340Cluster268911Cluster209766Cluster1240745Cluster311923Cluster418592 Cluster1051566Cluster1123272Cluster566369Cluster1048454Cluster207337Cluster116476 Cluster1197464Cluster328858Cluster892866Cluster562057Cluster827888Cluster1053011 Cluster458565Cluster104659Cluster736211Cluster829257Cluster685794Cluster331968 Cluster982015Cluster1206358Cluster45771Cluster207055Cluster248717Cluster510789 Cluster991831Cluster349803Cluster995806Cluster507669Cluster173349Cluster1143779 Cluster672614Cluster307655Cluster1263929Cluster297322Cluster136141Cluster742248 Cluster349816Cluster251116Cluster452702Cluster540555Cluster56812Cluster695513 Cluster293462Cluster695429Cluster285830Cluster635168Cluster442017Cluster742933 Cluster90253Cluster27587Cluster169255Cluster774173Cluster727635Cluster1248675 Cluster601361Cluster1183739Cluster800586Cluster34774Cluster1023008Cluster893883 Cluster538054Cluster865514Cluster691869Cluster356886Cluster255441Cluster64231 Cluster1070534Cluster1005727Cluster135872Cluster749120Cluster991103Cluster1146018 Cluster269137Cluster366435Cluster771823Cluster860327Cluster635383Cluster1187172 Cluster457227Cluster577635Cluster209030Cluster62619Cluster831241Cluster866260 Cluster415758Cluster636161Cluster816928Cluster694201Cluster624167Cluster1003060 Cluster750082Cluster264378Cluster426879Cluster608280Cluster304037Cluster261438 Cluster575459Cluster324551Cluster826739Cluster746906Cluster170607Cluster216019 Cluster466805Cluster1146858Cluster89670Cluster824686Cluster3190Cluster28078 Cluster392350Cluster752894Cluster230928Cluster114519Cluster566878Cluster1036508 Cluster446078Cluster91278Cluster571733Cluster433998Cluster99619Cluster684154 Cluster241981Cluster38325Cluster1021117Cluster31032Cluster752397Cluster100854 Cluster21179Cluster772405Cluster1142276Cluster483478Cluster1248054Cluster482720 Cluster1122591Cluster94273Cluster162246Cluster4679Cluster554908Cluster1142338 Cluster337993Cluster1168817Cluster677759Cluster251185Cluster503203Cluster467158 Cluster796690Cluster238319Cluster738928Cluster729683Cluster982016Cluster589774 Cluster954211Cluster637214Cluster976166Cluster331302Cluster584236Cluster429497 Cluster154420Cluster1152240Cluster442199Cluster896261Cluster438618Cluster969729 Cluster474866Cluster571895Cluster161999Cluster348215Cluster323177Cluster333413 Cluster472574Cluster316161Cluster1236621Cluster970873Cluster1259595Cluster253994 Cluster489533Cluster1151646Cluster511497Cluster241830Cluster351266Cluster130962 Cluster1003587Cluster254331Cluster256405Cluster248444Cluster1049504Cluster47848 Cluster964340Cluster140027Cluster193759Cluster511143Cluster1271122Cluster53598 Cluster278879Cluster352812Cluster150586Cluster147938Cluster827049Cluster657584 Cluster1087215Cluster243322Cluster877719Cluster1149906Cluster506593Cluster214234 Cluster99986Cluster512825Cluster835156Cluster315196Cluster675458Cluster409890 Cluster749478Cluster636211Cluster382546Cluster477205Cluster549169Cluster1111808 2.5 7.5 12.5 Cluster534081Cluster238466Cluster195495Cluster436377Cluster509775Cluster956910 Cluster422387Cluster1027613Cluster712475Cluster697682Cluster796384Cluster1094366 858 Cluster560910Cluster1183836Cluster434789Cluster1230530Cluster914355 859 Figure 3. Phylogenetic distribution of OTUs by land-cover type, created using ‘iNextPD’. 860 Terminal nodes are black and represent the OTUs. Internal nodes are white. Sizes of the 861 squares on the right indicate each OTU’s incidence frequency (number of samples in which 862 the OTU is observed). Phylogenetic coverage is most complete in native forests (NF) and 863 croplands (CL), followed by mixed plantations (MP), followed by the three monocultures 864 (BB, EC, JC). Codes for land-cover types as in Figure 1. 865

26 866 Figure 4

Plot of latent variable posterior medians (a) 36 4

Japanese cedar

37 2 30 38 21 4 39 Native forest 59 56 13 31 33 11 57 9 15 19 5851 1012 16 32 Cropland 20 34 48 14 1 4547 35 0 2 18 4342 55 46 44 4941 40 Altitude: 1100m ~ 1250m 17 73 54 50 22 5 53

Latent variable 2 Latent variable 24 52 25 Altitude: ~ 400m Mosaic plantation 8 6 23 2 − Bamboo 27 60 28 61 Eucalyptus 29 4 − 26 Altitude: 400 ~ 600m

−2 0 2 4

Latent variable 1

150150 (b) CL

130

NF

110

100100 Intersection Size

Intersection Size Intersection MP 5050 44 BB

37 EC JC 31 27

22 22

15 15 13 13 12 12 9 8 8 9 9 9 6 6 6 6 5 4 4 5 4 4 5 2 1 1 1 2 00 CL CL

JCJC

EC EC

BB

MP

NF

200200 150150 100100 5050 00 Set Size 867 Set Size 868

27 869 Figure 4. Community composition differences in all land-cover types. (a) ‘Boral’ ordination. 870 Colors represent land-cover types, and numbers represent individual samples. Cropland (CL) 871 sites separate into two clusters by elevation. Overlap of native forests (NF) and mixed- 872 plantations (MP) points indicates greater compositional similarity between these two land- 873 cover types. Ovals manually added to visualize community groupings. Residuals of the 874 ‘boral’ fit in Fig. S5. (b) UpSetR intersection map of OTUs unique to and shared between and 875 among land-cover types. Croplands and native forests support the highest numbers of unique 876 OTUs (CL=130, NF=110), followed by the four plantations (MP=44, BB=37, EC=31, 877 JC=27). Native forests uniquely share almost as many OTUs with mixed plantations (22 878 OTUs) as native forests share with the three monocultures combined (27 OTUs, =13+9+5). 879 Horizontal bars on the left indicate the total number of OTUs in each land-cover class. Codes 880 for land-cover types as in Figure 1. For clarity, only pairwise comparisons are shown. A non- 881 truncated version is presented in Fig. S6. 882

28 883 Figure 5

All beta diversity Turnover beta diversity only (a) (b) 0.4 0.2 EC

JC

0.1 BB 0.2

MP CL NF MP 0.0

CL 0.0 NF BB NMDS2 NMDS2 0.1 − JC 0.2 − EC 0.2 − 0.4 − 0.3 − −0.4 −0.2 0.0 0.2 0.4 −1.0 −0.5 0.0 0.5

NMDS1 NMDS1

(c) Nestedness beta diversity only 0.4 0.2

BB EC NF CL

0.0 MPJC NMDS2 0.2 − 0.4 −

−0.5 0.0 0.5 1.0

NMDS1 884 885 Figure 5. NMDS (non-metric multidimensional scaling) ordination of beta diversity by land- 886 cover type (binary Jaccard dissimilarities), partitioned with ‘betapart’. (a) Total beta diversity. 887 (b) Beta diversity based on species turnover only. (c) Beta diversity based on species 888 nestedness only. Turnover accounts for most the observed beta diversity across land-cover 889 types, which is visualized as greater distances between points in the turnover figure (b) and 890 almost no distances between points in the nestedness figure (c). Codes for land-cover types as 891 in Figure 1. 892

29 CL EC JC MP NF BB CL

CL EC JC MP NF 893 Figure 6

EC JC MP NF BB BB EC CL

Moundinothrips Frankliniella OpiusAthalia Thrips Megalurothrips Prenolepis Carpelimus Pheidole Harmonia Thinophilus Reticulitermes Megaselia Loxoblemmus

Urelliosoma Hylyphantes EC Tenthredinidae Thripidae Delia Braconidae Elateridae Parasteatoda Formicidae Staphylinidae Emmesomyia Coccinellidae Dolichopodidae Rhinotermitidae Chrysso Phoridae Gryllidae Mycetophila Eriophora Tephritidae Linyphiidae EC

Chorizopes Anthomyiidae Theridiidae Hymenoptera Thysanoptera JC Dicranosepsis Coleoptera Mycetophilidae Isoptera Gasteracantha Orthoptera Tachinidae Musca Cyclosa Moundinothrips Frankliniella Araneidae OpiusAthalia Thrips Sepsidae Megalurothrips Prenolepis Carpelimus Graphomya Pheidole Harmonia Nephila Thinophilus Reticulitermes Megaselia Loxoblemmus Dichaetomyia Moundinothrips Urelliosoma Frankliniella Hylyphantes OpiusAthalia Thrips Muscidae Megalurothrips Nephilidae Prenolepis Tenthredinidae Thripidae Delia Carpelimus Pheidole Braconidae Elateridae Parasteatoda Formicidae Staphylinidae Harmonia Emmesomyia Thinophilus CoccinellidaeReticulitermes Psychoda Dolichopodidae Rhinotermitidae Chrysso Megaselia LoxoblemmusSalticidae Trochosa Phoridae Araneae Gryllidae

Mycetophila JC Urelliosoma Eriophora Tephritidae Linyphiidae Hylyphantes Psychodidae Delia Tenthredinidae Thripidae Lycosidae Simulium Braconidae Elateridae Parasteatoda Chorizopes Anthomyiidae Staphylinidae Theridiidae Ebelingia Formicidae Thysanoptera JC Emmesomyia Hymenoptera Coccinellidae Dolichopodidae Coleoptera Chrysso Dicranosepsis Arthropoda Arachnida Rhinotermitidae Simuliidae Mycetophilidae Gasteracantha Phoridae Isoptera Gryllidae Thomisidae Insecta Mycetophila Eriophora Tephritidae Orthoptera Phytomyza Tachinidae Linyphiidae Musca CyclosaLeucauge Agromyzidae Anthomyiidae Araneidae Chorizopes Sepsidae Thysanoptera Theridiidae Graphomya Hymenoptera Coleoptera Nephila Dicranosepsis Tetragnathidae Gasteracantha Scatella Mycetophilidae Isoptera Orthoptera Tetragnatha Diptera Dichaetomyia Musca Tachinidae Ephydridae Muscidae Nephilidae Cyclosa Araneidae Sepsidae Phlogotettix Nostima Psychoda Graphomya root AraneaeCicadellidaeSalticidae Nephila Trochosa

Psychodidae MP SimuliumDichaetomyia Lycosidae Muscidae Nephilidae ThagriaEbelingia Chlorops Arthropoda Arachnida Chloropidae Simuliidae Thomisidae Psychoda Insecta Phytomyza Salticidae Trochosa Araneae Leucauge Agromyzidae Hemiptera Psychodidae Aphididae Aphis Simulium Lycosidae Gaurax Scatella Tetragnathidae Ebelingia Arthropoda Arachnida Tetragnatha Simuliidae Thomisidae Ephydridae Diptera Insecta Phytomyza Phlogotettix Leucauge Nostima Agromyzidae Takecallis root Cicadellidae Stegana Miridae Drosophilidae Scatella Tetragnathidae MP TetragnathaThagria Chlorops ChloropidaeEphydridae Diptera MP Greenideidae ProboscidocorisPhlogotettix Nostima Hemiptera Scaptomyza root CicadellidaeAphididae Aphis Gaurax Thagria Nodes Chlorops Erebidae Stratiomyidae Chloropidae Takecallis MiridaeGreenidea Drosophila Stegana Drosophilidae Hemiptera Hesperiidae Aphididae Aphis Gaurax Greenideidae Proboscidocoris −3 Scaptomyza 1.0 Sciaridae Lepidoptera Termessa Takecallis Nodes Stegana Noctuidae ErebidaeMiridae Hermetia DrosophilidaeStratiomyidae Greenidea Drosophila HesperiidaeGreenideidae Proboscidocoris Scaptomyza Pieridae Halpe −3 1.0 Sciaridae Lepidoptera Termessa −2 Ctenosciara Hermetia NoctuidaeErebidae Nodes 15.9 Stratiomyidae Greenidea Drosophila Lecithoceridae Culicidae PieridaeHesperiidae Halpe Ctenosciara Mythimna −3−2 1.0 15.9 Bradysia Sciaridae CrambidaeLepidoptera Lecithoceridae Termessa Hermetia Culicidae Noctuidae Mythimna Bradysia Crambidae −1 Lycaenidae Halpe 60.4 Sarcophagidae PierisPieridae −1 Ctenosciara Lycaenidae −2 15.9 60.4 Ochlerotatus Sarcophagidae Pieris Ochlerotatus Uraniidae Lecithoceridae Culicidae Uraniidae Mythimna Bradysia Lymantriidae Crambidae Homaloxestis Syrphidae Lymantriidae Homaloxestis Syrphidae Aedes

Aedes −1 Cyclidia Lycaenidae Pieris 0 60.4135.0 Ochlerotatus Sarcophagidae Cyclidia Tortricidae Paratalanta 0 135.0 Culex Tortricidae ParatalantaUraniidae Chironomidae Lymantriidae TarakaHomaloxestis Culex Aedes Sarcophaga Syrphidae Cyclidia 0 1 135.0239.0 ChironomidaeSphaerophoria TortricidaeTaraka EversmanniaParatalanta Sarcophaga Culex Baccha Chironomidae Somena Taraka Sarcophaga 1 239.0 Eristalis Eversmannia Cyclidia Sphaerophoria Bactra Eversmannia 1 2 239.0373.0 Sphaerophoria Procladius Ablabesmyia Chironomus Baccha Nilodorum SomenaCricotopus Somena

Baccha Polypedilum Number of OTUs Eristalis Nanocladius TanytarsusCyclidia Cyclidia Eristalis Procladius Bactra 2 3 373.0536.0 Ablabesmyia Bactra Chironomus 2 373.0 Procladius Nilodorum Cricotopus

Chironomus Polypedilum Number of OTUs Ablabesmyia Nanocladius Tanytarsus Nilodorum Cricotopus 3 536.0 Polypedilum Number of OTUs 894 Nanocladius Tanytarsus median proportions Log2 ratio 3 536.0

895 Figure 6. Pairwise taxonomic comparisons of all land-cover types. Upper right triangle: 896 greener branches indicate taxa that are relatively more abundant (in numbers of OTUs) in the 897 land-cover types along the right column, and browner branches indicate taxa that are 898 relatively more abundant in the land-cover types along the top row. Lower left: taxonomic 899 identities of the branches. Note that this is a taxonomic tree, not a phylogenetic tree. Legend: 900 width indicates number of OTUs at a given taxonomic rank, and color indicates relative

901 differences in log2(number of OTUs). Codes for land-cover types as in Figure 1. A figure 902 including croplands and a zoomable taxonomic tree is in supplementary information (Figure 903 S8, S9). 904

30 Title: The biodiversity benefit of native forests and mixed-species plantations over monoculture plantations

Appendix

Authors: Xiaoyang Wang1,2, Fangyuan Hua3,4, Lin Wang1, David S. Wilcove5,6, Douglas W Yu1,7,8*

Author affiliations: 1 State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China 2 Kunming College of Life Sciences, University of Chinese Academy of Sciences, Kunming, Yunnan 650223, China 3 Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge CB2 3QZ, U.K. 4 Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan 650201, China 5 Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, United States 6 Program in Science, Technology, and Environmental Policy, Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544, United States 7 Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming Yunnan, 650223 China 8 School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk NR4 7TJ, UK

Appendix S1: Figures S1 – S9, Table S1 – S3 Figure S1. Spatial arrangement of pan traps in each one-hectare quadrat (= 1 sampling site). Each quadrat was subdivided into four subquadrats to balance pan colors. Each dot’s color represents that pan-trap’s color (white, yellow, blue, red, purple), which within each subquadrat were arranged randomly.

100m 15m

5m

15m

Figure S2. Each land-cover type’s observed species richness, visualized using ‘beanplot’ 1.2 (Kampstra, 2008). White lines are observed values at each sampling site, black lines are the mean per land-cover type, and the dashed line is the grand mean. Codes for land-cover types as in Figure 1.

60 40 Species richness 20 0

BB CL EC JC MP NF

Figure S3. ‘iNEXT’ estimates of species richness, Shannon diversity, and Simpson diversity by land-cover type, using sample-based rarefaction and extrapolation. Native forests (NF) have the highest species richness and diversities, followed by croplands (CL) and mixed plantations (MP), followed by the three monoculture plantations (BB, EC, and JC). Codes for land-cover types as in Figure 1. Symbols on each curve indicate the number of sampled locations per land-cover type, solid lines represent ‘iNEXT’ interpolations, and dashed lines represent ‘iNEXT’ extrapolations, with 95% confidence intervals. Statistically significant pairwise differences are detected visually by non-overlapping confidence intervals and are somewhat conservative (MacGregor-Fors & Payton, 2013).

Species0 richness Shannon1 diversity Simpson2 diversity

NF 300

Guides NF CL BB CL EC JC 200 MP MP MP NF NF

CL Method BB Species diversity MP interpolation BB extrapolation NA JC JC CL 100

EC BB EC JC EC

0 0 10 20 30 0 10 20 30 0 10 20 30 Number of sampling units

Figure S4. ‘iNextPD’ estimates of phylogenetic diversity by land-cover type, using sample- based rarefaction and extrapolation. Similar to the results in Figure S3, two of the three estimators of phylogenetic diversity are higher in native forests (NF), followed by croplands (CL) and mixed plantations (MP), followed by the three monocultures (BB, EC, and JC). Codes for land-cover types as in Figure 1). Symbols indicate sample sizes per land-cover type, solid lines represent ‘iNextPD’ interpolations, and dashed lines represent ‘iNextPD’ extrapolations, with 95% confidence intervals. Statistically significant pairwise differences are detected visually by non-overlapping confidence intervals and are somewhat conservative (MacGregor-Fors & Payton, 2013).

Faith’s0 PD Phylogenetic1 entropy Rao’s quadratic2 entropy

80

NF

60 MP Guides BB CL CL NF EC BB JC MP 40 NF JC MP Method CL EC NF interpolation Phylogenetic diversity Phylogenetic BB extrapolation EC CL 20 MP JC EC BB JC

0 0 10 20 30 0 10 20 30 0 10 20 30 Number of sampling units

Figure S5. Residual plots of the boral model fit in Fig. 4a. 4 4 2 2 0 0 Smyth Residuals Smyth Residuals Smyth − − 2 2 − − Dunn Dunn 4 4 − −

−4 −2 0 2 1 4 7 11 15 19 23 27 31 35 39 43 47 51 55 59

Linear Predictors Row index

Normal Quantile Plot 4 4 2 2 0 0 Smyth Residuals Smyth − Sample Quantiles 2 2 − − Dunn 4 4 − −

1 15 31 47 63 79 95 114 135 156 177 198 219 240 261 −4 −2 0 2 4

Column index Theoretical Quantiles

Figure S6. UpSetR intersection map of OTU distribution by land-cover type. Number of comparisons not truncated. Horizontal bars on the left bottom indicate the number of OTUs in each land-cover type, and vertical bars indicate the number of unique or shared OTUs. Codes for land-cover types as in Figure 1.

150

130

110

100 Intersection Size

50 44

37

31 27

22 22

15 15 13 1312 12 10 10 10 9 8 8 9 9 9 6 6 6 6 5 5 4 4 4 5 5 4 4 5 4 4 2 1 1 1 2 1 1 1 2 2 2 1 1 1 1 1 2 1 1 1 2 2 2 1 2 1 2 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 2 2 1 2 1 1 2 1 1 1 2 2 1 2 1 1 1 1 1 2 1 1 2 2 2 1 0

CL

JC

EC

BB

MP

NF

200 150 100 50 0 Set Size Figure S7. Heat map of OTU distribution by land-cover type, showing that beta diversity is dominated by species turnover rather than by nestedness. The vertical line separates two compartments of communities, one dominated by croplands and one dominated by forests and plantations. Each column is a sample site, and rows are OTUs. Codes for land-cover types as in Figure 1.

Cluster185105Cluster1048454Cluster264960Cluster1049504 Cluster418592Cluster900429Cluster589774Cluster428661 Cluster549198Cluster161999Cluster95070Cluster892866 Cluster993654Cluster881883Cluster684154Cluster680471 Cluster1052098Cluster731000Cluster162949Cluster681238 Cluster896917Cluster415758Cluster261786Cluster120982 Cluster453676Cluster236078Cluster231396Cluster465625 Cluster582971Cluster549885Cluster466805Cluster466340 Cluster104659Cluster827888Cluster759071Cluster633345 Cluster349816Cluster310116Cluster442199Cluster116476 Cluster454109Cluster1122591Cluster568976Cluster193554 Cluster246310Cluster871445Cluster99619Cluster742248 Cluster987637Cluster446078Cluster616735Cluster497237 Cluster1094626Cluster409890Cluster297807Cluster1219908 Cluster872255Cluster1218901Cluster230928Cluster885812 Cluster1102602Cluster549169Cluster119182Cluster870147 Cluster354297Cluster351266Cluster241830Cluster1187172 Cluster672614Cluster566369Cluster469416Cluster466755 Cluster363044Cluster268911Cluster953387Cluster771823 Cluster1111802Cluster955298Cluster896261Cluster868323 Cluster254474Cluster130962Cluster1051566Cluster571940 Cluster574237Cluster571783Cluster438618Cluster433998 Cluster752894Cluster736211Cluster735119Cluster729683 Cluster260416Cluster99986Cluster894190Cluster893773 Cluster781374Cluster772602Cluster638387Cluster139804 Cluster957273Cluster662085Cluster1251081Cluster190091 Cluster510789Cluster1252116Cluster474866Cluster311923 Cluster562057Cluster995806Cluster254331Cluster346941 Cluster209766Cluster7237Cluster1146858Cluster680855 Cluster532117Cluster38325Cluster320422Cluster27587 Cluster506593Cluster815919Cluster451245Cluster566878 Cluster251185Cluster1082093Cluster827049Cluster56812 Cluster120067Cluster1160615Cluster1152240Cluster1149906 Cluster3190Cluster27114Cluster437190Cluster201249 Cluster411007Cluster413866Cluster392350Cluster511497 Cluster1156Cluster1142338Cluster159898Cluster293462 Cluster375548Cluster324551Cluster193759Cluster1168817 Cluster63348Cluster540555Cluster534081Cluster378570 Cluster312482Cluster1197464Cluster785398Cluster694201 Cluster999788Cluster982016Cluster666290Cluster482720 Cluster135872Cluster458565Cluster209030Cluster96058 Cluster509685Cluster503203Cluster263522Cluster173349 Cluster1236621Cluster796690Cluster638055Cluster556802 Cluster1043256Cluster1264742Cluster436377Cluster1115775 Cluster1043055Cluster1027064Cluster442017Cluster193702 Cluster248444Cluster1118816Cluster1259595Cluster1142276 Cluster337634Cluster1230530Cluster366435Cluster304037 Cluster970873Cluster726828Cluster752397Cluster1123272 Cluster241981Cluster136141Cluster328858Cluster538054 Cluster566789Cluster552615Cluster499788Cluster28078 Cluster866003Cluster824686Cluster675258Cluster635383 Cluster956910Cluster551260Cluster550517Cluster1190248 Cluster1035399Cluster1034115Cluster1031263Cluster1023008 Cluster1271122Cluster1263929Cluster1262791Cluster1248675 Cluster336931Cluster334862Cluster292170Cluster2299 Cluster652901Cluster636161Cluster403990Cluster337993 Cluster726965Cluster722821Cluster712475Cluster705838 Cluster969729Cluster941879Cluster89973Cluster727635 Cluster1147433Cluster292040Cluster628263Cluster571733 Cluster10088Cluster8795Cluster933654Cluster238319 Cluster1003587Cluster300625Cluster970757Cluster865514 Cluster449561Cluster297322Cluster472574Cluster866530 Cluster487185Cluster62619Cluster248703Cluster675458 Cluster405715Cluster382546Cluster349803Cluster1160549 Cluster53598Cluster691418Cluster255441Cluster608280 Cluster278879Cluster216019Cluster970338Cluster636975 Cluster153235Cluster150586Cluster147938Cluster1186330 Cluster877719Cluster467158Cluster352812Cluster251116 Cluster691869Cluster4912Cluster62672Cluster991831 Cluster273441Cluster1021117Cluster418132Cluster236674 Cluster825580Cluster723910Cluster593252Cluster381399 Cluster1036508Cluster331968Cluster914355Cluster909388 Cluster689312Cluster517616Cluster256405Cluster21179 Cluster976166Cluster785061Cluster14910Cluster831241 Cluster1120269Cluster1003060Cluster554866Cluster1147021 Cluster457227Cluster435585Cluster188440Cluster125385 Cluster860327Cluster832583Cluster829582Cluster635994 Cluster1154095Cluster1117056Cluster1322Cluster860679 Cluster519998Cluster207055Cluster238661Cluster317705 Cluster422387Cluster695429Cluster16393Cluster96099 Cluster866046Cluster375655Cluster1205684Cluster405735 Cluster528954Cluster1183739Cluster802315Cluster975075 Cluster348215Cluster190691Cluster1240745Cluster1046555 Cluster1244964Cluster800586Cluster749120Cluster603666 Cluster667421Cluster356106Cluster738928Cluster635168 Cluster368371Cluster18504Cluster1014195Cluster685794 Cluster816928Cluster307655Cluster89670Cluster693469 Cluster1035165Cluster1027613Cluster248717Cluster1036793 Cluster660889Cluster657584Cluster641699Cluster1044930 Cluster1262671Cluster957758Cluster991103Cluster90253 Cluster932946Cluster515604Cluster1151646Cluster774173 Cluster781271Cluster452702Cluster1234815Cluster1087215 Cluster956580Cluster914193Cluster91278Cluster1143779 Cluster91284Cluster599965Cluster114519Cluster1263180 Cluster964340Cluster695513Cluster636211Cluster750082 Cluster577635Cluster571895Cluster320933Cluster253994 Cluster881319Cluster162246Cluster214234Cluster47848 Cluster601361Cluster714500Cluster933936Cluster169255 Cluster777026Cluster772405Cluster742933Cluster742617 Cluster927147Cluster946116Cluster835156Cluster975205 Cluster154420Cluster1070534Cluster1005727Cluster1228370 Cluster374617Cluster356886Cluster492428Cluster195495 Cluster1248054Cluster893883Cluster852624Cluster1008496 Cluster881515Cluster346275Cluster283921Cluster69978 Cluster434789Cluster425852Cluster170040Cluster168999 Cluster657934Cluster852926Cluster22396Cluster105406 Cluster283192Cluster515093Cluster604765Cluster477205 Cluster148932Cluster866260Cluster666208Cluster315196 Cluster1094366Cluster333413Cluster327486Cluster316161 Cluster795289Cluster487820Cluster483478Cluster160348 Cluster279779Cluster199147Cluster323177Cluster638434 Cluster1183836Cluster761967Cluster549932Cluster319598 Cluster170607Cluster313551Cluster34774Cluster866010 Cluster888658Cluster360587Cluster31032Cluster285830 Cluster648953Cluster697682Cluster762239Cluster426879 Cluster89960Cluster746906Cluster982015Cluster64231 Cluster261438Cluster1078541Cluster1053011Cluster100854 Cluster421090Cluster1058962Cluster954211Cluster575459 Cluster1270869Cluster588978Cluster258891Cluster1185877 Cluster1112567Cluster826739Cluster560910Cluster511143 Cluster1206358Cluster1271357Cluster507669Cluster1212246 Cluster269137Cluster1116607Cluster144694Cluster629282 Cluster45771Cluster1146018Cluster794054Cluster900613 Cluster317158Cluster624167Cluster605186Cluster899998 Cluster313794Cluster271497Cluster264378Cluster1189880 Cluster637214Cluster141583Cluster829257Cluster509775 Cluster489533Cluster238466Cluster140027Cluster677759 Cluster243322Cluster796384Cluster555842Cluster554946 Cluster641821Cluster563424Cluster331302Cluster31249 Cluster1061819Cluster207337Cluster1111808Cluster779302 Cluster584236Cluster429497Cluster321338Cluster1226805 Cluster8809Cluster512825Cluster94273Cluster749478 Cluster4679Cluster1191583Cluster724647Cluster71294 Cluster807777Cluster791843Cluster554908Cluster482725 JC JC JC JC JC JC JC JC JC JC CL CL CL CL CL CL CL CL CL CL CL CL CL CL CL NF NF NF NF NF NF NF NF NF NF NF NF NF BB BB BB BB BB BB BB EC EC EC EC EC EC EC MF MF MF MF MF MF MF MF MF Cropland-dominated Forest-dominated

CL EC JC MP NF BB

Figure S8. Pairwise taxonomic comparisons of all six land-cover types. Interpretation the same as in Figure 6 except that croplands is included in this version of the figure (boxes). Upper right triangle: greener branches indicate taxa that are relatively more abundant (in terms of numbers of OTUs) in the land-cover types along the right column, and browner branches indicate taxa that are relatively more abundant in the land-cover types along the top row. Lower left: taxonomic identities of the CL EC JC MP NF branches. Note that this is a taxonomic tree, not a phylogenetic tree. Legend: width indicates CL number of OTUs at a given taxonomic rank, and color indicates relative differences in log2(number

of OTUs). Codes for land-cover types as in Figure 1. BB

CL EC JC MP NF BB EC CL

Moundinothrips Frankliniella OpiusAthalia Thrips Megalurothrips Prenolepis Carpelimus Pheidole Harmonia CL Thinophilus Reticulitermes Megaselia Loxoblemmus

Urelliosoma Hylyphantes Tenthredinidae Thripidae Delia Braconidae Elateridae Parasteatoda Staphylinidae Formicidae EC Emmesomyia Coccinellidae Dolichopodidae Rhinotermitidae Chrysso Phoridae Gryllidae Mycetophila Eriophora Tephritidae Linyphiidae

Chorizopes Anthomyiidae Theridiidae Hymenoptera Thysanoptera JC Dicranosepsis Coleoptera Moundinothrips Frankliniella Gasteracantha Mycetophilidae OpiusAthalia Thrips Isoptera EC Megalurothrips Prenolepis Orthoptera Carpelimus Pheidole Musca Tachinidae Harmonia Thinophilus Reticulitermes Cyclosa Megaselia AraneidaeLoxoblemmus Sepsidae Graphomya Urelliosoma Hylyphantes Nephila Tenthredinidae Thripidae Delia Braconidae Elateridae Parasteatoda Staphylinidae Formicidae Moundinothrips Frankliniella Emmesomyia OpiusAthalia Coccinellidae Thrips Megalurothrips Chrysso Dichaetomyia Dolichopodidae Prenolepis Rhinotermitidae Carpelimus Muscidae Phoridae Pheidole Gryllidae HarmoniaNephilidae Mycetophila Thinophilus Reticulitermes Eriophora Tephritidae Linyphiidae Megaselia Loxoblemmus Psychoda Urelliosoma Chorizopes Anthomyiidae TheridiidaeSalticidae Hylyphantes Trochosa Thysanoptera Araneae JC Tenthredinidae Thripidae DeliaHymenoptera Elateridae Parasteatoda Dicranosepsis BraconidaeColeoptera Psychodidae Formicidae Staphylinidae Gasteracantha Mycetophilidae Emmesomyia Isoptera Coccinellidae Dolichopodidae Lycosidae Chrysso Simulium Orthoptera Rhinotermitidae Phoridae Gryllidae Ebelingia Tachinidae Mycetophila Musca CyclosaEriophora Arthropoda Tephritidae Arachnida Linyphiidae Simuliidae Araneidae Sepsidae Thomisidae Insecta Anthomyiidae Chorizopes Graphomya Theridiidae Phytomyza Hymenoptera Thysanoptera Nephila JC Dicranosepsis Coleoptera Leucauge Agromyzidae Mycetophilidae Isoptera Gasteracantha Dichaetomyia Orthoptera Musca Tachinidae Muscidae Nephilidae Cyclosa Tetragnathidae Araneidae Scatella Sepsidae Tetragnatha Psychoda Graphomya Salticidae TrochosaNephila Ephydridae Diptera Araneae Dichaetomyia Phlogotettix Psychodidae Muscidae Nostima Simulium Lycosidae Nephilidae root Cicadellidae Ebelingia Psychoda Arthropoda Arachnida Simuliidae Salticidae Trochosa Insecta AraneaeThomisidae MP Phytomyza Thagria Psychodidae Leucauge Simulium Lycosidae Chlorops Agromyzidae Ebelingia Chloropidae Simuliidae Arthropoda Arachnida Insecta Tetragnathidae Thomisidae Scatella Phytomyza Hemiptera Tetragnatha Leucauge Agromyzidae Aphididae Aphis Ephydridae Diptera Phlogotettix Gaurax Scatella Tetragnathidae Nostima Diptera root Cicadellidae Tetragnatha Ephydridae Phlogotettix MP Nostima root TakecallisCicadellidaeThagria Chlorops Miridae Stegana Chloropidae MP Drosophilidae Thagria Chlorops Hemiptera Chloropidae Aphididae Aphis Hemiptera Gaurax Greenideidae ProboscidocorisAphididae Aphis Scaptomyza Gaurax Takecallis Stegana Miridae Takecallis Nodes Erebidae Miridae Stratiomyidae Drosophilidae Stegana Drosophilidae Greenidea Drosophila Greenideidae GreenideidaeProboscidocorisProboscidocoris Scaptomyza Scaptomyza Hesperiidae Erebidae −3 NodesNodes Stratiomyidae Erebidae 1.0 Stratiomyidae Greenidea Sciaridae Drosophila Lepidoptera TermessaGreenidea Hermetia Drosophila Noctuidae Hesperiidae Hesperiidae −3 Termessa 1.0 Sciaridae Lepidoptera Noctuidae −3 Hermetia Termessa 1.0 Sciaridae LepidopteraPieridae Noctuidae Halpe Hermetia Pieridae Halpe Ctenosciara Ctenosciara −2 −2 15.9 15.9 Lecithoceridae Culicidae Pieridae Halpe Ctenosciara Lecithoceridae Mythimna −2 Culicidae Bradysia Crambidae 15.9 Lecithoceridae Mythimna −1 Bradysia Culicidae Lycaenidae Mythimna Pieris 60.4 Ochlerotatus Sarcophagidae Crambidae Bradysia Crambidae Uraniidae −1 Lycaenidae Lymantriidae Homaloxestis −1 Syrphidae 60.4 Sarcophagidae Aedes Lycaenidae Pieris Pieris 60.4 Ochlerotatus Sarcophagidae Cyclidia 0 135.0 Ochlerotatus Tortricidae Paratalanta Culex Uraniidae Uraniidae ChironomidaeLymantriidae HomaloxestisTaraka Syrphidae Sarcophaga Aedes Lymantriidae Homaloxestis Syrphidae Cyclidia Aedes Eversmannia 0 1 135.0239.0 Sphaerophoria Cyclidia Tortricidae Paratalanta Culex 0 135.0 TortricidaeBaccha Paratalanta Somena Culex ChironomidaeEristalis TarakaCyclidia Sarcophaga Procladius Bactra 2 373.0 Ablabesmyia Chironomus 1 239.0 CricotopusEversmannia SphaerophoriaChironomidae Nilodorum Taraka Polypedilum Number of OTUs Sarcophaga Nanocladius Tanytarsus Baccha Somena 1 3 536.0239.0 Cyclidia Sphaerophoria Eristalis Eversmannia Procladius Bactra 2 373.0 Ablabesmyia SomenaChironomus Baccha Nilodorum Cricotopus

Polypedilum Number of OTUs Eristalis Nanocladius Tanytarsus Cyclidia Procladius Bactra 23 536.0373.0 Ablabesmyia Chironomus Nilodorum Cricotopus

Polypedilum Number of OTUs Nanocladius Tanytarsus Log2 ratio median proportions Log2 ratio 3 536.0

CL EC JC MP NF BB CL EC

Figure S9. Taxonomic tree of all OTUs in figure 6.

Moundinothrips Frankliniella OpiusAthalia Thrips Megalurothrips Prenolepis Carpelimus Pheidole Harmonia Thinophilus Reticulitermes Megaselia Loxoblemmus

Urelliosoma Hylyphantes Tenthredinidae Thripidae Delia Braconidae Elateridae Parasteatoda Formicidae Staphylinidae Emmesomyia Coccinellidae Dolichopodidae Rhinotermitidae Chrysso Phoridae Gryllidae Mycetophila Eriophora Tephritidae Linyphiidae

Chorizopes Anthomyiidae Theridiidae Hymenoptera Thysanoptera JC Dicranosepsis Coleoptera Mycetophilidae Isoptera Gasteracantha Orthoptera Tachinidae Musca Cyclosa Araneidae Sepsidae Graphomya Nephila

Dichaetomyia Muscidae Nephilidae

Psychoda Araneae Salticidae Trochosa

Psychodidae Simulium Lycosidae Arthropoda Arachnida Ebelingia Simuliidae Insecta Thomisidae Phytomyza Leucauge Agromyzidae

Scatella Tetragnathidae Tetragnatha Ephydridae Diptera Phlogotettix Nostima root Cicadellidae MP Thagria Chlorops Chloropidae Hemiptera Aphididae Aphis Gaurax

Takecallis Miridae Stegana Drosophilidae Greenideidae Proboscidocoris Scaptomyza Erebidae Nodes Stratiomyidae Greenidea Drosophila Hesperiidae −3 1.0 Sciaridae Lepidoptera Termessa Hermetia Noctuidae

Pieridae Halpe Ctenosciara −2 15.9 Lecithoceridae Culicidae Mythimna Bradysia Crambidae −1 Lycaenidae Pieris 60.4 Ochlerotatus Sarcophagidae Uraniidae Lymantriidae Homaloxestis Syrphidae

Aedes Cyclidia 0 135.0 Tortricidae Paratalanta Culex Chironomidae Taraka Sarcophaga 1 239.0 Sphaerophoria Eversmannia Baccha Somena Eristalis Cyclidia Procladius Bactra 2 373.0 Ablabesmyia Chironomus Nilodorum Cricotopus

Polypedilum Number of OTUs Nanocladius Tanytarsus 3 536.0

Table S1. Tags and primers used, and a table of tag combinations used for each sample (underlined), spread over two Illumina libraries. Both forward and reverse primers were tagged with sample-identifying tags. Lib1 Lib2 primer Tagged_primer Forward CL01 JC01 F1-R1 Tag1 CCTAAACTACGGGGTCAACAAATCATAAAGATATTGG CL02 JC02 F1-R2 Tag2 GTGGTATGGGAGTGGTCAACAAATCATAAAGATATTGG CL03 JC03 F1-R3 Tag3 TGTTGCGTTTCTGTGGTCAACAAATCATAAAGATATTGG CL04 JC04 F1-R4 Tag4 ACAGCCACCCATCGAGGTCAACAAATCATAAAGATATTGG CL05 JC05 F1-R5 Tag5 GTTACGTGGTTGATGAGGTCAACAAATCATAAAGATATTGG CL06 JC06 F1-R6 Tag6 TACCGGCTTGCATGCGAGGTCAACAAATCATAAAGATATTGG CL07 JC07 F2-R1 CL08 JC08 F2-R2 Tagged_primer Reverse CL09 JC09 F2-R3 Tag1 CCTAAACTACGGGGNGGRTANANNGTYCANCCNGYNCC CL10 JC10 F2-R4 Tag2 GTGGTATGGGAGTGGNGGRTANANNGTYCANCCNGYNCC CL11 JC11 F2-R5 Tag3 TGTTGCGTTTCTGTGGNGGRTANANNGTYCANCCNGYNCC CL12 JC12 F2-R6 Tag4 ACAGCCACCCATCGAGGNGGRTANANNGTYCANCCNGYNCC CL13 EC01-2-3 F3-R1 Tag5 GTTACGTGGTTGATGAGGNGGRTANANNGTYCANCCNGYNCC CL14 EC04 F3-R2 Tag6 TACCGGCTTGCATGCGAGGNGGRTANANNGTYCANCCNGYNCC CL15 EC05 F3-R3 CL16 EC06 F3-R4 Adapter_link_tag Forward BB01 EC07 F3-R5 Tag1 CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTAAACTACGG BB02 EC08 F3-R6 Tag2 CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGTGGTATGGGAG BB03 EC09 F4-R1 Tag3 CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGTTGCGTTTCT BB04 EC10 F4-R2 Tag4 CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACAGCCACCCAT BB05 NF01 F4-R3 Tag5 CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGTTACGTGGTTGATGA BB06 NF02-3 F4-R4 Tag6 CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTACCGGCTTGCATGCGA BB07 NF04 F4-R5 BB08 NF05 F4-R6 Adapter_link_tag Reverse BB09 NF06 F5-R1 Tag1 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTAAACTACGG BB10 NF07 F5-R2 Tag2 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGGTATGGGAG MF01 NF08 F5-R3 Tag3 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTGTTGCGTTTCT MF02 NF09 F5-R4 Tag4 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTACAGCCACCCAT MF03 NF10 F5-R5 Tag5 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTGTTACGTGGTTGATGA MF04 NF11 F5-R6 Tag6 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTACCGGCTTGCATGCGA MF05 NF12 F6-R1 MF06 NF13 F6-R2 MF07 NF14 F6-R3 MF08 NF15 F6-R4 MF09 NF16 F6-R5 MF10 - F6-R6 Table S2. Multiple pairwise Welch’s t tests for Chao2 estimates. P values adjusted by Bonferroni. Codes for land-cover types as in Figure 1.

CL EC JC MP NF BB 0.0432 0.1913 0.7236 0.1022 0.006* CL 0.00075* 0.0973 0.5307 0.0973 EC 0.1420 0.0455* 0.00075* JC 0.1400 0.0105* MP 0.5242

Table S3. mvabund compositional comparisons. We used mvabund to test whether arthropod species compositions in native forests and mixed plantations are significantly different from each other and from the other land-cover types in the study region. After Bonferroni correction, all comparisons were significantly different at p < 0.01. Codes for land-cover types as in Figure 1.

MP BB CL EC JC NF 0.00125 0.00125 0.00125 0.00125 0.003 MP 0.001 0.001 0.001 0.001

REFERENCE Kampstra, P. (2008). Beanplot: A Boxplot Alternative for Visual Comparison of Distributions. Journal of Statistical Software, 28, 1–9. https://doi.org/10.18637/jss.v028.c01 MacGregor-Fors, I., & Payton, M. E. (2013). Contrasting Diversity Values: Statistical Inferences Based on Overlapping Confidence Intervals. PLoS ONE, 8, 8–11. https://doi.org/10.1371/journal.pone.0056794