Ecology

Sequencing of Shorea leprosula genomeSample collectionLeaf samples of S. leprosula were obtained from a reproductively mature (diameter at breast height, 50 cm) diploid tree B1_19 (DNA ID 214) grown in the Dipterocarp Arboretum, Forest Research Institute Malaysia (FRIM).DNA extractionGenomic DNA was extracted from leaf samples using the 2% cetyltrimethylammonium bromide (CTAB) method90 and purified using a High Pure PCR Template Purification kit (Roche).Library preparation and sequencingPaired-end (170, 500, and 800 bp) and mate-pair (2 kb) genomic libraries were prepared using a TruSeq DNA Library Preparation kit (Illumina) and a Mate Pair Library Preparation kit (Illumina), respectively. Mate-pair libraries with larger insert sizes were constructed using a Nextera Mate Pair Library Preparation kit (Illumina). Ten micrograms of genomic DNA were tagmented in a 400 μl reaction and fractionated using SageELF, with the recovery of 11 fractions with 3–16+ kb. Each fraction was circularized and fragmented with a Covaris S2. Biotin-containing fragments were purified using Dynabeads M-280 streptavidin beads. Sequencing adapters (KAPA TruSeq Adapter kit) were attached using a KAPA Hyper Prep kit. The libraries were amplified for 10–13 cycles and purified with 0.8× AMpure XP. DNA libraries were then sequenced (~388× coverage) using Illumina HiSeq2000 (TruSeq libraries) and HiSeq2500 (Nextera libraries) at the Functional Genomics Center Zurich (FGCZ), University of Zurich, Switzerland (Supplementary Table 1).Genome assemblyAdapters and low-quality bases for all paired-end and mate-pair reads were removed using Trimmomatic91. The filtered paired-end reads of the 170 bp library were used to identify the genome size using k-mer distribution generated by Jellyfish92 that was implemented in the scripts by Joseph Ryan42. The raw R1 reads from paired-end 170 and 800 bp libraries (clipped at 95 bp, representing about 70 genome equivalents) were used to estimate the heterozygosity using KAT43 with a k-mer size of 23 nt. De novo genome assembly of all reads was performed using ALLPATHSLG assembler v5248840.Assembly verification and assessment of the assembled genomeAssembly validationTo validate the genome assembly, we mapped (i) the short reads used for the genome assembly, (ii) scanned the assembly for the presence of single-copy orthologs, and (iii) mapped transcriptome sequences obtained from seven organs.Assembly verification by mapping of short readsFor each library used for genome assembly, all trimmed reads were aligned to the assembled S. leprosula genome using Burrows–Wheeler Aligner (BWA) v0.7.1293. Then, mapping ratio was calculated for each BAM file using Samtools94 with “flagstat” command.Identification of highly conserved single-copy orthologsBUSCO v3.1.042 was run with the Embryophyta dataset and Arabidopsis as the species for AUGUSTUS prediction (see subsection below “Protein-coding gene prediction”).Assembly verification by mapping transcriptome sequencesFor mapping transcriptome sequences, samples of seven organs (leaf bud, flower bud, flower, inner bark, small seed, large seed, and calyx) were obtained from the S. leprosula individual used for the genome sequencing (Supplementary Table 2). Total RNA was extracted from each sample using RNeasy Plant Mini Kit (Qiagen) and it was treated with Turbo DNase I (Takara). Library preparation was carried out using a TruSeq RNA Library Preparation kit (Illumina). Paired-end sequencing was conducted for all the libraries using Illumina HiSeq2000 at the FGCZ, University of Zurich, Switzerland. Adapters and low-quality bases for all paired-end reads were removed using Trimmomatic. The trimmed sequences of each library were mapped onto the assembled genome using STAR aligner v2.4.2a95, and mapping ratio was obtained from the output file of STAR.Genome annotationRepeat sequence analysisBoth homology-based and de novo prediction analyses were used to identify the repeat content in the S. leprosula assembly. For the homology-based analysis, we used Repbase (version 20120418) to perform a TE search with RepeatMasker (4.0.5) and the WuBlast search engine. For the de novo prediction analysis, we used RepeatModeler to construct a TE library. Elements within the library were then classified by homology to Repbase sequences (see subsection below “Preparation of repeat sequences for evidence-based gene prediction”).Protein-coding gene predictionS. leprosula protein-coding genes were predicted by AUGUSTUS v3.245. For ab initio gene prediction, we used a pre-trained A. thaliana metaparameter implemented in AUGUSTUS. For the evidence-based gene prediction, we used the information of exon, intron and repeat sequences of S. leprosula as hints for the AUGUSTUS gene prediction. The details of the preparation of the hints were described in the following subsections.Preparation of repeat sequences for evidence-based gene predictionWe used RepeatModeler to construct a de novo library of repeated sequences in the S. leprosula assembly. Then, using RepeatMasker, we generated a file containing the information of the positions of repeat sequences in the S. leprosula genome based on the RepeatModeler library. Elements within the library were then classified by homology to Repbase sequences. Finally, the hint file for repeat sequences in GFF format was prepared using the two scripts, “10_makeGffRm.pl” and “12_makeTeHints.pl”, stored in https://gitlab.com/rbrisk/ahalassembly.Preparation of the exon and intron information for evidence-based gene predictionTo obtain the exon and intron hints, we used the mapping data of RNA-seq obtained from seven organs of the sequenced S. leprosula individual as described above. First, we merged all the mapping data stored in different BAM files into a single BAM file using SAMtools. Then, we prepared the intron hint file in GFF format using the, “bam2hints” script of AUGUSTUS. The exon hint file was also generated from the merged BAM file using the two AUGUSTUS scripts, “bam2wig” and “wig2hints.pl”. To conduct evidence-based gene prediction with AUGUSTUS, the three hint files (repeat sequences, intron and exon) described above were merged into a single file in GFF format.BUSCO analysisGenome annotation completeness were assessed with BUSCO v3.1.044 using the Embryophyta odb9 dataset composed of 1440 universal Embryophyta single-copy genes. We referred to these 1440 genes as core genes in the main text.Comparison with the proteome of Theobroma cacao
T. cacao’s gene models18 were downloaded from Phytozome 11 (https://phytozome.jgi.doe.gov/pz/portal.html). Then, comparison was conducted with BLASTP96 using the T. cacao proteomes as the BLAST database (E-value cutoff: 1.0E-10). Only the best hit was stored for each gene. We considered these best hits of the T. cacao genes as orthologs of the S. leprosula genes. When the T. cacao orthologs were identified by the BLASTP search, the orthologs of A. thaliana were defined based on the T. cacao-A. thaliana orthologous information provided by Phytozome 11 (Supplementary Table 4). When the T. cacao orthologs were not identified, the orthologs of A. thaliana were searched by BLASTP (E-value cutoff: 1.0E-10) using the A. thaliana proteomes obtained from TAIR 10 (https://www.arabidopsis.org) as the BLAST database.Synteny analysisBased on the result of the above BLASTP searches, we assessed synteny between the S. leprosula scaffolds and the T. cacao chromosomes using MCScanX97. Genome information of T. cacao in GFF format was also obtained from Phytozome 11 as described above, which was used as an input file for MCScanX.Assessment of the genome assemblyPopulation data and other dipterocarp speciesTo assess whether the genome assembly could be used as a reference for the S. leprosula individuals from various populations, we checked mapping ratio, SNP positions, and admixture using the distribution-wide S. leprosula samples. Similarly, to assess whether the S. leprosula assembly could be used as a reference for aligning data from closely related species and determining their mapping ratios. For interspecific analysis, the following three Dipterocarpoideae species: S. platycarpa, D. aromatica, and N. heimii were used (Supplementary Table 7).Sample collection and DNA extractionLeaf samples of 19 S. leprosula individuals from different populations and three other dipterocarp species (S. platycarpa, D. aromatica, and N. heimii) were used as described in Supplementary Tables 6 and 7. Genomic DNA was extracted using the same method as described above.Library preparation and sequencingPaired-end genomic libraries (200 bp) were prepared using a TruSeq DNA Library Preparation kit (Illumina). DNA libraries were then sequenced (~16× coverage each) using Illumina HiSeq2000.Mapping and SNP callingAdapters and low-quality bases from resequencing reads were removed using Trimmomatic. All trimmed reads were then mapped and aligned to the S. leprosula assembly using BWA. Variants were called using GATK v3.598. Duplicated reads were marked using Picard 2.6.0. Within GATK, HaplotypeCaller was used to identify variants for each sample by generating an intermediate genomic variant call format (gVCF). Subsequently, gVCF files were merged using GenotypeGVCFs to produce a raw VCF file containing SNPs and INDELs. Low-quality variants were removed from the raw VCF file by applying the hard filters implemented in GATK. Variants with genotype quality (GQ)  More

Host and gut content samplingA total of 94 individuals (42 A. speciosus, 9 A. argenteus, and 43 M. rufocanus) were captured from four sites within the Kamikawa Chubu national forest in the central area on the island of Hokkaido, Japan (Supplementary Table S1), and a total of 280 gut content (from the small intestine, cecum, and colon) and fecal matter (from the rectum) samples were collected for microbiome analysis (Supplementary Table S2). Based on 16S rRNA amplicon sequencing using Illumina Miseq, a total of 12,286,171 paired-end reads were obtained after quality filtering and chimeric sequence removal. There was an average of 43,879 reads per sample, although it varied among species and gut region (Supplementary Table S3).Within host species/among gut region gut microbiota alpha diversityAlpha diversity of the gut microbiota in the small intestine was significantly lower than the rectum, colon, and cecum in all three host species based on Shannon diversity, Faith’s PD, evenness, and number of ASVs as expected (GLME: all p  0.05; Fig. 1, Supplementary Fig. S2, Supplementary Tables S4–S7). Males had significantly higher alpha diversity within all gut regions of A. speciosus while female A. argenteus had significantly higher alpha diversity as compared to males (GLME, all p  0.05; Supplementary Tables S4–S7) while age had no effect in any gut region of any rodent species (GLME: all p  > 0.05; Supplementary Tables S4–S7).Figure 1Alpha diversity within each gut region of each species based on (a) Shannon diversity and (b) Faith’s PD. Dashed lines separate host species.Full size imageAmong host species alpha diversityMyodes rufocanus had significantly higher alpha diversity in all four gut regions as compared to both A. speciosus and A. argenteus based on all four diversity measurements (GLME: all p  More

Isolation and characterization of bean dreg-degrading strainsA 1362-bp amplification fragment of 16S rDNA was obtained by PCR (GenBank accession number MW406939). This sequence was compared with others in the GenBank database, aligning the 16S rDNA sequences with several Geobacillus sp. strains and constructed a phylogenetic tree (Fig. 2a). The phylogenetic tree clearly showed that strain GT-02 belongs to the G.toebii branch and was similar to G.toebii R-32652, G.toebii NBRC 107807, and G.toebii SK-1 with 99.78%, 99.63% and 99.05% similarities, respectively. According to the study described previously, G.toebii was a gram-positive, aerobic rod and motile bacterial26. G.toebii could produce acid from inositol and gas from nitrate. G.toebii could hydrolysis casein and utilize n-alkanes as carbon source27.Figure 2(a) Phylogenetic tree based on 16S rDNA gene sequences from related species of the genus Geobacillus constructed using the neighbour-joining method with 1000 bootstrap replicates. Branch length is indicated at each node. (b) The growth curve of strain GT-02 with temperature. (c) The growth curve of strain GT-02 with pH.Full size imageThe growth characteristics of strain GT-02, such as temperature and pH values, were investigated. The bacterial strain could grow within a range of 40–75 °C and pH 6.50–9.50, and the optimum temperature and pH were 65 °C and 7.50, respectively (Fig. 2b,c). Compared to other G.toebii strains, the maximum growth temperature and pH of strains R-32652 and SK-1 were 70 °C and 9.0026,28, respectively. These results showed that strain GT-02 was more resistant to high temperature and alkalinity. Fermentation temperature above 70 °C could effectively inactivate harmful microorganisms in organic solid waste12. Therefore, the fermentation temperature was set at 70 °C in this study.Changes in the composition of bean dregs during fermentationChanges in GI, TOC and TN of bean dregs during fermentationThe GI is traditionally used to evaluate the phytotoxicity and maturity of organic fertilizer12. As shown in Fig. 3a, both groups of experiments reached the standard of maturity (GI ≥ 85.00%). Therefore, the fermentation was terminated in five days. In the initial stage of fermentation, the GI of CK dropped to 51.85% on day 2, and the GI of T1 dropped to 41.98% on day 1. Phytotoxicity, which is usually caused by various heavy metals and low-molecular-weight substances, such as NH3 and organic acids, can reduce seed germination and inhibit root development29. During fermentation, bean dregs might produce NH3, organic acids and other substances, which could trigger a decrease in the GI. The GI of T1 showed a clear decrease, which was likely due to the production of toxic organic acids and might also explain the decrease in pH observed in T1 (Fig. 3d). Due to the degradation of organic acids, the GI of T1 increased to 95.06% on the third day and continued to increase to more than 100.00%, whereas in CK, the GI only reached 86.42% at the end of the fermentation. These results revealed that the maturity of T1 on day 3 was markedly higher than that of CK on day 5 and thus suggest that G.toebii can significantly enhance the fermentation efficiency by accelerating the maturation process and thus reducing the thermophilic fermentation period from 5 to 3 days.Figure 3Profiles of GI (a), TOC (b), TN (c), pH (d) and EC (e) during the fermentation process of CK and T1. The data represent the means ± standard deviations from three measurements.Full size imageTOC is usually used as an energy source by microorganisms30. The TOC loss in both CK and T1 increased during fermentation (Fig. 3b). The reduction of TOC was mainly caused by the production of carbon dioxide from bacterial respiration. The rate of TOC loss in T1 was higher than that in CK. At the end of the fermentation, the TOC loss of T1 was 11.78% higher than that in CK. Because of the addition of G.toebii, bacterial metabolism in T1 was more active, and organic degradation was faster.The TN loss in both CK and T1 also showed an upward trend (Fig. 3c). The loss of TN was mainly caused by the volatilization of ammonia nitrogen31. The rate of TN loss in T1 increased more than that of CK group. After fermentation (day 5), the TN loss in T1 was 6.83% higher than that of CK. The mineralization in T1 was more active and thus ammonia nitrogen was more, which was easy to cause volatilization. However, the bean dregs in CK were mature on the 5th day, while those in T1 were on the 3rd day. At this time, the TN loss of mature bean dregs in T1 was 5.66% lower than that in CK, which indicated that the bean dregs lost less nitrogen source when they reached the standard of maturity after the addition of G.toebii.Changes in pH and EC of bean dregs during fermentationThe variation in pH observed during fermentation is due to the interaction between inorganic nitrogen and organic acids produced by the decomposition of organic matter32. As shown in Fig. 3d, the pH of CK gradually increased to 8.72 at the end of the fermentation. The ammonification process and the release of free NH3 during organic matter (OM) degradation lead to increases in pH33. The pH of T1 decreased to 5.73 on day 1, which was due to the formation of more organic acids than CK, and then increased to 8.76 on day 2, which was due to acid consumption and ammonia formation. Figure 2c showed that GT-02 could hardly grow when the pH was lower than 6.00, but the heterogeneity of solid fermentation provided a possible living environment for the growth of GT-02. Subsequently, the pH of T1 slowly decreased to 8.10 due to ammonia volatilization or ammonia conversion. These study findings showed that the pH value of the fermentation process was significantly affected by the addition of GT-02. G.toebii can produce abundant high-temperature enzymes, such as amylase, protease, cellulase, xylanase, and mannanase17, which explains why the ammonification process was faster in T1 than in CK and thus the higher pH was found in T1.The EC, which is a measure of the total ion concentration, describes changes in the levels of organic and inorganic ions such as SO42−, Na+, NH4+, K+, Cl−, and NO3− during the fermentation process34. As shown in Fig. 3e, the EC of the two groups increased significantly during fermentation process (P  More

1.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by Northern ecosystems since 1960. Science 341, 1085–1089 (2013).ADS 
CAS 
Article 

Google Scholar 
2.Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
CAS 
Article 

Google Scholar 
3.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
CAS 
Article 

Google Scholar 
4.Schlesinger, W. H. & Jasechko, S. Agricultural and forest meteorology transpiration in the global water cycle. Agric. For. Meteorol. 189–190, 115–117 (2014).ADS 
Article 

Google Scholar 
5.Dawson, T. E. & Pate, J. S. Seasonal water uptake and movement in root systems of Australian phraeatophytic plants of dimorphic root morphology: a stable isotope investigation. Oecologia 107, 13–20 (1996).ADS 
Article 

Google Scholar 
6.Voltas, J., Devon, L., Maria Regina, C. & Juan Pedro, F. Intraspecific variation in the use of water sources by the circum-Mediterranean conifer Pinus halepensis. New Phytol. 208, 1031–1041 (2015).Article 

Google Scholar 
7.Grossiord, C. et al. Prolonged warming and drought modify belowground interactions for water among coexisting plants. Tree Physiol. 39, 55–63 (2018).Article 

Google Scholar 
8.Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).ADS 
CAS 
Article 

Google Scholar 
9.Querejeta, J. I., Estrada-Medina, H., Allen, M. F. & Jiménez-Osornio, J. J. Water source partitioning among trees growing on shallow karst soils in a seasonally dry tropical climate. Oecologia 152, 26–36 (2007).ADS 
Article 

Google Scholar 
10.Evaristo, J. & McDonnell, J. J. Prevalence and magnitude of groundwater use by vegetation: a global stable isotope meta-analysis. Sci Rep. 7, 44110 (2017).ADS 
Article 

Google Scholar 
11.Barbeta, A. & Peñuelas, J. Relative contribution of groundwater to plant transpiration estimated with stable isotopes. Sci Rep. 7, 10580 (2017).ADS 
Article 

Google Scholar 
12.Jobbágy, E. G., Nosetto, M. D., Villagra, P. E. & Jackson, R. B. Water subsidies from mountains to deserts: their role in sustaining groundwater-fed oases in a sandy landscape. Ecol. Appl. 21, 678–694 (2011).Article 

Google Scholar 
13.Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).ADS 
CAS 
Article 

Google Scholar 
14.Ellsworth, P. Z. & Sternberg, L. S. L. Seasonal water use by deciduous and evergreen woody species in a scrub community is based on water availability and root distribution. Ecohydrology 551, 538–551 (2015).Article 

Google Scholar 
15.Sohel, S. Spatial and Temporal Variation of Sources of Water Across Multiple Tropical Rainforest Trees. PhD thesis, Univ. Queensland (2019).16.Williams, D. G. & Ehleringer, J. R. Intra- and interspecific variation for summer precipitation use in pinyon-juniper woodlands. Ecol. Monogr. 70, 517–537 (2000).
Google Scholar 
17.Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R., T. W. & Goldsmith, G. R. Seasonal origins of soil water used by trees. Hydrol. Earth Syst. Sci. 23, 1199–1210 (2019).ADS 
CAS 
Article 

Google Scholar 
18.David, T. S. et al. Water-use strategies in two co-occurring Mediterranean evergreen oaks: surviving the summer drought. Tree Physiol. 27, 793–803 (2007).CAS 
Article 

Google Scholar 
19.Zencich, S. J., Froend, R. H., Turner, J. V. & Gailitis, V. Influence of groundwater depth on the seasonal sources of water accessed by Banksia tree species on a shallow, sandy coastal aquifer. Oecologia 131, 8–19 (2002).ADS 
Article 

Google Scholar 
20.Naumburg, E., Mata-Gonzalez, R., Hunter, R. G. & Martin, D. W. Phreatophytic vegetation and groundwater fluctuations: a review of current research and application of ecosystem response modeling with an emphasis on Great Basin vegetation. Environ. Manage. 35, 726–740 (2005).Article 

Google Scholar 
21.Snyder, K. A. & Williams, D. G. Water sources used by riparian trees varies among stream types on the San Pedro River, Arizona. Agric. For. Meteorol. 105, 227–240 (2000).ADS 
Article 

Google Scholar 
22.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Köppen–Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).ADS 
Article 

Google Scholar 
23.Eleringer J. R. & Dawson T. Water uptake by plants: perspectives from stable isotope composition. Plant Cell Environ. 1073–1082 (1992).24.Dawson, T. E., Mambelli, S., Plamboeck, A. H., Templer, P. H. & Tu, K. P. Stable isotopes in plant ecology. Annu. Rev. Ecol. Syst. 33, 507–559 (2002).Article 

Google Scholar 
25.Rothfuss, Y. & Javaux, M. Reviews and syntheses: isotopic approaches to quantify root water uptake: a review and comparison of methods. Biogeosciences 14, 2199–2224 (2017).ADS 
CAS 
Article 

Google Scholar 
26.Orlowski, N. et al. Inter-laboratory comparison of cryogenic water extraction systems for stable isotope analysis of soil water. Hydrol. Earth Syst. Sci. 22, 3619–3637 (2018).ADS 
CAS 
Article 

Google Scholar 
27.Chen, Y. et al. Stem water cryogenic extraction biases estimation in deuterium isotope composition of plant source water. Proc. Natl Acad. Sci. USA 117, 33345–33350 (2021).ADS 
Article 

Google Scholar 
28.Pastorello, G., Trotta, C., Canfora, E. & Al., E. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 

Google Scholar 
29.Zhao, Y. & Wang, L. Plant water use strategy in response to spatial and temporal variation in precipitation patterns in China: a stable isotope analysis. Forests 9, 1–21 (2018).
Google Scholar 
30.Miguez-Macho, G. & Fan, Y. The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration. J. Geophys. Res. Atmos. 117, (2012).31.Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).ADS 
CAS 
Article 

Google Scholar 
32.Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).ADS 
Article 

Google Scholar  More

1.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484–1241484 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Treves, A. & Karanth, K. U. Human–carnivore conflict and perspectives on carnivore management worldwide. Conserv. Biol. 17, 1491–1499 (2003).Article 

Google Scholar 
3.Linnell, J. D. C. & Boitani, L. Building biological realism into wolf management policy: The development of the population approach in Europe. Hystrix Ital. J. Mammal. 23, 80–91 (2011).
Google Scholar 
4.Heurich, M. et al. Illegal hunting as a major driver of the source-sink dynamics of a reintroduced lynx population in Central Europe. Biol. Conserv. 224, 355–365 (2018).Article 

Google Scholar 
5.Breitenmoser-Würsten, C., Vandel, J.-M., Zimmermann, F. & Breitenmoser, U. Demography of lynx Lynx lynx in the Jura Mountains. Wildl. Biol. 13, 381–392 (2007).Article 

Google Scholar 
6.Clutton-Brock, T. & Sheldon, B. C. Individuals and populations: The role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol. Evol. 25, 562–573 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
7.O’Connell, A., Nichols, J. D. & Karanth, K. U. Camera Traps in Animal Ecology: Methods and Analyses. (Springer Tokyo, 2011).8.Noss, A. J. et al. A Camera trapping and radio telemetry study of lowland tapir (Tapirus terrestris) in Bolivian Dry Forests. Tapir Cons. 12, 9 (2003).
Google Scholar 
9.Karanth, K. U. & Nichols, J. D. Estimation of tiger densities in India using photographic captures and recaptures. Ecology 79, 11 (1998).Article 

Google Scholar 
10.Satter, C. B., Augustine, B. C., Harmsen, B. J., Foster, R. J. & Kelly, M. J. Sex‐specific population dynamics of ocelots in Belize using open population spatial capture–recapture. Ecosphere 10, e02792 (2019).Article 

Google Scholar 
11.Silver, S. C. et al. The use of camera traps for estimating jaguar Panthera onca abundance and density using capture/recapture analysis. Oryx 38, 148–154 (2004).Article 

Google Scholar 
12.Zimmermann, F., Breitenmoser-Würsten, C., Molinari-Jobin, A. & Breitenmoser, U. Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture–recapture. Integr. Zool. 8, 232–243 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Royle, J. A., Chandler, R. B., Sollmann, R. & Gardner, B. Spatial Capture–Recapture. (Elsevier, 2014).
Google Scholar 
14.Chandler, R. B. & Clark, J. D. Spatially explicit integrated population models. Methods Ecol. Evol. 5, 1351–1360 (2014).Article 

Google Scholar 
15.Kaczensky, P. et al. Status, management and distribution of large carnivores—Bear, lynx, wolf and wolverine in Europe (EuropeanCommission, 2013).16.Magg, N. et al. Habitat availability is not limiting the distribution of the Bohemian–Bavarian lynx Lynx lynx population. Oryx 50, 742–752 (2016).Article 

Google Scholar 
17.Müller, J. et al. Protected areas shape the spatial distribution of a European lynx population more than 20 years after reintroduction. Biol. Conserv. 177, 210–217 (2014).Article 

Google Scholar 
18.Bull, J. K. et al. The effect of reintroductions on the genetic variability in Eurasian lynx populations: The cases of Bohemian–Bavarian and Vosges–Palatinian populations. Conserv. Genet. 17, 1229–1234 (2016).Article 

Google Scholar 
19.Walston, J. et al. Bringing the tiger back from the brink—The six percent solution. PLoS Biol. 8, e1000485 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Schmidt, K., Jędrzejewski, W. & Okarma, H. Spatial organization and social relations in the Eurasian lynx population in Bialowieza Primeval Forest, Poland. Acta Theriol. (Warsz.) 42, 289–312 (1997).Article 

Google Scholar 
21.Bunnefeld, N., Linnell, J. D. C., Odden, J., van Duijn, M. A. J. & Andersen, R. Risk taking by Eurasian lynx (Lynx lynx) in a human-dominated landscape: Effects of sex and reproductive status. J. Zool. 270, 31–39 (2006).
Google Scholar 
22.Gaillard, J.-M., Nilsen, E. B., Odden, J., Andrén, H. & Linnell, J. D. C. One size fits all: Eurasian lynx females share a common optimal litter size. J. Anim. Ecol. 83, 107–115 (2014).PubMed 
Article 

Google Scholar 
23.Nilsen, E. B., Linnell, J. D. C., Odden, J., Samelius, G. & Andrén, H. Patterns of variation in reproductive parameters in Eurasian lynx (Lynx lynx). Acta Theriol. (Warsz.) 57, 217–223 (2012).Article 

Google Scholar 
24.O’Brien, T. G., Kinnaird, M. F. & Wibisono, H. T. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Anim. Conserv. 6, 131–139 (2003).Article 

Google Scholar 
25.Cailleret, M., Heurich, M. & Bugmann, H. Reduction in browsing intensity may not compensate climate change effects on tree species composition in the Bavarian Forest National Park. For. Ecol. Manag. 328, 179–192 (2014).Article 

Google Scholar 
26.Heurich, M. et al. Country, cover or protection: What shapes the distribution of red deer and roe deer in the Bohemian Forest Ecosystem?. PLoS ONE 10, e0120960 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.van Beeck Calkoen, S. T. S. et al. The blame game: Using eDNA to identify species-specific tree browsing by red deer (Cervus elaphus) and roe deer (Capreolus capreolus) in a temperate forest. For. Ecol. Manag. 451, 117483 (2019).Article 

Google Scholar 
28.Wölfl, M. et al. Distribution and status of lynx in the border region between Czech Republic, Germany and Austria. Acta Theriol. 46, 181–194 (2001).Article 

Google Scholar 
29.Mináriková, T. et al. Lynx monitoring report for Bohemian–Bavarian–Austrian lynx population for lynx year 2017 (INTERREG Central Europe, 2019).30.Weingarth, K. et al. First estimation of Eurasian lynx (Lynx lynx) abundance and density using digital cameras and capture–recapture techniques in a German national park. Anim. Biodivers. Conserv. 35, 197–207 (2012).Article 

Google Scholar 
31.Belotti, E. et al. Patterns of lynx predation at the interface between protected areas and multi-use landscapes in Central Europe. PLoS ONE 10, e0138139 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Tobler, M. W. & Powell, G. V. N. Estimating jaguar densities with camera traps: Problems with current designs and recommendations for future studies. Biol. Conserv. 159, 109–118 (2013).Article 

Google Scholar 
33.Zimmermann, F., Breitenmoser-Würsten, C. & Breitenmoser, U. Natal dispersal of Eurasian lynx ( Lynx lynx ) in Switzerland. J. Zool. 267, 381 (2005).Article 

Google Scholar 
34.Andrén, H. et al. Survival rates and causes of mortality in Eurasian lynx (Lynx lynx) in multi-use landscapes. Biol. Conserv. 131, 23–32 (2006).Article 

Google Scholar 
35.Gimenez, O. et al. Spatial density estimates of Eurasian lynx (Lynx lynx) in the French Jura and Vosges Mountains. Ecol. Evol. 9, 11707–11715 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Pesenti, E. & Zimmermann, F. Density estimations of the Eurasian lynx (Lynx lynx) in the Swiss Alps. J. Mammal. 94, 73–81 (2013).Article 

Google Scholar 
37.Weingarth, K. et al. Hide and seek: Extended camera-trap session lengths and autumn provide best parameters for estimating lynx densities in mountainous areas. Biodivers. Conserv. 24, 2935–2952 (2015).Article 

Google Scholar 
38.Pollock, K. H. A capture–recapture design robust to unequal probability of capture. J. Wildl. Manag. 46, 752 (1982).Article 

Google Scholar 
39.Augustine, B. benaug/OpenPopSCR. (2019). https://github.com/benaug/OpenPopSCR.40.Ergon, T. & Gardner, B. Separating mortality and emigration: Modelling space use, dispersal and survival with robust-design spatial capture–recapture data. Methods Ecol. Evol. 5, 1327–1336 (2014).Article 

Google Scholar 
41.Schaub, M. & Royle, J. A. Estimating true instead of apparent survival using spatial Cormack–Jolly–Seber models. Methods Ecol. Evol. 5, 1316–1326 (2014).Article 

Google Scholar 
42.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2005).
Google Scholar 
43.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).MathSciNet 

Google Scholar 
44.Efford, M. secr 4.1—Spatially explicit capture–recapture in R. (2019). https://cran.microsoft.com/snapshot/2019-12-24/web/packages/secr/vignettes/secr-overview.pdf.45.Burnham, K. P. & Overton, W. S. Robust estimation of population size when capture probabilities vary among animals. Ecology 60, 927–936 (1979).Article 

Google Scholar 
46.Burnham, K. P., Anderson, D. R. & Burnham, K. P. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
47.O’Brien, T. G. Abundance, density and relative abundance: A conceptual framework. In Camera Traps in Animal Ecology (eds O’Connell, A. F. et al.) 71–96 (Springer Japan, 2011). https://doi.org/10.1007/978-4-431-99495-4_6.Chapter 

Google Scholar 
48.Rovero, F. & Zimmermann, F. Camera Trapping for Wildlife Research (Pelagic Publishing Ltd, 2016).
Google Scholar 
49.Augustine, B. C. et al. Sex-specific population dynamics and demography of capercaillie (Tetrao urogallus L.) in a patchy environment. Popul. Ecol. 62, 80–90 (2020).Article 

Google Scholar 
50.Duľa, M. et al. Multi-seasonal systematic camera-trapping reveals fluctuating densities and high turnover rates of Carpathian lynx on the western edge of its native range. Sci. Rep. 11, 9236 (2021).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
51.Avgan, B., Zimmermann, F., Güntert, M., Arıkan, F. & Breitenmoser, U. The first density estimation of an isolated Eurasian lynx population in southwest Asia. Wildl. Biol. 20, 217–221 (2014).Article 

Google Scholar 
52.Mengüllüoğlu, D., Ambarlı, H., Berger, A. & Hofer, H. Foraging ecology of Eurasian lynx populations in southwest Asia: Conservation implications for a diet specialist. Ecol. Evol. 8, 9451–9463 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Heurich, M. et al. Activity patterns of Eurasian lynx are modulated by light regime and individual traits over a wide latitudinal range. PLoS ONE 9, e114143 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
54.Jedrzejewski, W. et al. Population dynamics (1869–1994), demography, and home ranges of the lynx in Bialowieza Primeval Forest (Poland and Belarus). Ecography 19, 122–138 (1996).Article 

Google Scholar 
55.Gardner, B., Sollmann, R., Kumar, N. S., Jathanna, D. & Karanth, K. U. State space and movement specification in open population spatial capture–recapture models. Ecol. Evol. 8, 10336–10344 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
56.López-Bao, J. V. et al. Eurasian lynx fitness shows little variation across Scandinavian human-dominated landscapes. Sci. Rep. 9, 8903 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
57.Engleder, T. et al. First breeding record of a 1-year-old female Eurasian lynx. Eur. J. Wildl. Res. 65, 17 (2019).Article 

Google Scholar 
58.Heurich, M. et al. Selective predation of a stalking predator on ungulate prey. PLoS ONE 11, e0158449 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Andrén, H. & Liberg, O. Large impact of Eurasian lynx predation on roe deer population dynamics. PLoS ONE 10, e0120570 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Elmhagen, B. & Rushton, S. P. Trophic control of mesopredators in terrestrial ecosystems: Top-down or bottom-up?. Ecol. Lett. 10, 197–206 (2007).PubMed 
Article 

Google Scholar 
61.Wikenros, C. et al. Fear or food—Abundance of red fox in relation to occurrence of lynx and wolf. Sci. Rep. 7, 9059 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
62.Helldin, J. O., Liberg, O. & Glöersen, G. Lynx (Lynx lynx) killing red foxes (Vulpes vulpes) in boreal Sweden? Frequency and population effects. J. Zool. 270, 657–663 (2006).Article 

Google Scholar 
63.Sollmann, R., Mohamed, A., Samejima, H. & Wilting, A. Risky business or simple solution—Relative abundance indices from camera-trapping. Biol. Conserv. 159, 405–412 (2013).Article 

Google Scholar 
64.Linnell, J. D. C., Kaczensky, P., Wotschikowsky, U., Lescureux, N. & Boitani, L. Framing the relationship between people and nature in the context of European conservation: Relationship between people and nature. Conserv. Biol. 29, 978–985 (2015).PubMed 
Article 

Google Scholar  More

.readcube-buybox { display: none !important;}

Toxic algae are likely to begin blooming more frequently in Arctic waters as the climate and the ocean warm1.



Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
/* style specs end */Subscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-021-02715-z

References1.Anderson, D. M. et al. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2107387118 (2021).Article 

Google Scholar 
Download references

Subjects

Ecology

Latest on:

Ecology

Spatiotemporal origin of soil water taken up by vegetation
Article 06 OCT 21

Fund natural-history museums, not de-extinction
Correspondence 05 OCT 21

Illegal mining in the Amazon hits record high amid Indigenous protests
News 30 SEP 21

Jobs

Research Fellow/Postdoc

TUM CREATE Ltd
Singapore, Singapore

Postdoctoral Scientist (Immunogenetics)

The Pirbright Institute
Pirbright, United Kingdom

PhD Student (gn) Neuroimmunology

University Hospital of Muenster (UKM), WWU
Münster, Germany

Summer Fleming Scholar Internship

Oklahoma Medical Research Foundation (OMRF)
Oklahoma City, United States More

Inverted paradoxNeutral theory can reproduce properties of terrestrial biodiversity observed at local (e.g., an island) or metacommunity (i.e., a set of interacting communities linked by dispersal of species) scales, particularly ranked species abundance curves (i.e., histograms of species abundance ordered along the x-axis from most to least common) [14]. Central to neutral theory is the interplay between ‘stochastic exclusion’ and either immigration or speciation. Stochastic exclusion is the reduction in biodiversity caused by random deaths and abundance-dependent replacement and, if not countered by other processes, ultimately leads to only a single remaining species [14]. Immigration of species into a local community or speciation within the metacommunity offset stochastic exclusion and maintain biodiversity [14]. This relationship is illustrated in Fig. 1 by simulated time-series of phytoplankton diversity for three populations at steady-state with 10,000, 100,000, and 1,000,000 total individuals and an initial condition of 10,000 species each (Fig. 1) (Methods). Subjection of these populations to 50% random mortality per generation and replacement in proportion to the relative abundance of remaining species results in an eventual rate of decrease in diversity that is equivalent across population sizes (Fig. 1; dashed black lines), eventually yielding the expected final equilibrium of a single species. When a small rate of immigration is added to this simulation (here, 0.03% or 0.3% per generation), complete stochastic exclusion is replaced by steady-state diversities that vary in direct proportion to population size and immigration rate (Fig. 1; colored dashed and dotted lines). Similar considerations led Hubbell [14] to earlier propose in his “Unified Neutral Theory” a fundamental biodiversity number, θ, controlling both species richness and relative abundance:$$theta ,=, 2Jupsilon$$
(1)
where J is the total number of individuals in the community and υ the rate of immigration (local) or speciation (metacommunity).Fig. 1: Phytoplankton biodiversity following purely stochastic processes.Red, blue, and green = phytoplankton populations (J) of 10,000, 100,000, and 1,000,000 individuals, respectively (Methods). Colored solid lines = species richness in the absence of immigration (υ). Colored dashed and dotted lines = species richness for υ values of 0.03% and 0.3% per generation. Black dashed line = mean rate of decline for the primary phase of stochastic exclusion (slope of this line is the same for all three populations). Blue and green downturned triangles = threshold for the two larger populations where diversity begins to decline rapidly because a sufficient number of species have been reduced to an abundance where extinction within a generation becomes likely.Full size imageIn addition to illustrating the balance between stochastic exclusion and immigration into a local phytoplankton community, Fig. 1 shows that significant decreases in species richness only ensue after a subpopulation of species within a community has been sufficiently decimated in number that their remaining individuals might be lost through random mortality within a generation. In our simulations, this threshold is demarked by the downturn in species richness for the populations of 100,000 and 1,000,000 individuals (Fig. 1; blue and green triangles). The significance of stochastic exclusion is thus dependent on the relation between extant species number and size of the physically-homogenized community. With respect to the latter property, typical horizontal eddy diffusion values for the upper ocean are O(103 m2 s−1), implying that the length scale for mixing in 1 day is O(1000 m). Typical number concentrations for phytoplankton of different species in the ocean range from More

Study sitesWe conducted this study in three protected areas in Sabah, Malaysian Borneo: Danum Valley Conservation Area (DVCA), the Lower Kinabatangan Wildlife Sanctuary (LKWS), and Tabin Wildlife Reserve (TWR) (Fig. 4). The minimum and maximum daily temperatures and annual precipitation among the three study sites did not differ significantly (annual temperature: 22–33 ℃, annual precipitation 2400–3100 mm; Mitchell37; Matsuda et al.39; South East Asia Rainforest Research Partnership Unpublished data. https://www.searrp.org/) although there is no recent precise climate data of TWR.Figure 4Location of the three study sites in Borneo.Full size imageThe DVCA (4° 50′–5° 05′ N, 117° 30′–117° 48′ E) is a Class I Protection Forest Reserve established by the Sabah state government in 1996 and managed by the Sabah Foundation (Yayasan Sabah Group) covering 438 km2. Approximately 90% of the area is comprised of mature lowland evergreen dipterocarp forests34. The study area is an old-growth forest surrounding the Borneo Rainforest Lodge (5° 01′ N, 117° 44′ E), a tourist lodging facility.The LKWS (5° 10′–5° 50′ N, 117° 40′–118° 30′ E), is located along the Kinabatangan River, which is the longest river flowing to the east coast, reaching 560 km inland and with a catchment area of 16,800 km2. Designated as a wildlife sanctuary and gazetted in 2005, the LKWS consists of ten forest blocks totaling 270 km2, comprised of seasonal and tidal swamp forests, permanent freshwater swamps, mangrove forests, and lowland dipterocarp forests35,36. The southern area of the Menanggul River is extensively covered by secondary forest. However, the northern area has been deforested for oil palm (Elaeis guineensis) plantations, except for a protected zone along the river. The TWR (5° 05′–5° 22′ N, 118° 30′–118° 55′ E) is located approximately 50 km northeast of Lahad Datu, eastern Sabah, and covers approximately 1225 km2.The TWR is exclusively surrounded by large oil palm plantations. Most parts of the TWR were heavily logged in the 1970s and the 1980s, leaving mainly regenerating mixed dipterocarp tropical rainforests dominated by pioneer species such as Neolamarckia cadamba and Macaranga bancana37,38. The study area was near the Sabah Wildlife Department base camp located on the western boundary of the TWR (5° 11′ N, 118° 30′ E). The study area includes heavily logged secondary forests and a small patchy old forest (0.74 km2).Data collectionWe set up 15, 30, and 28 infrared-triggered sensor cameras (Bushnell, Trophy Cam TM) in the DVCA (July 2010–August 2011 and May 2014–December 2016), LKWS (July 2010–December 2014) and TWR (May 2010–June 2012), respectively. As a result, the cumulative number of camera operation days in DVCA, LKWS, and TWR were 14,134, 18,265, and 4980, for a total of 37,379 days. Although it was impossible to record the animals during certain months because of adverse weather conditions, such as heavy rain, flooding, battery failure, other malfunctions mainly caused by insects nesting inside the cameras, or logistical problems, the cameras remained continuously activated. Due to these reasons, camera operating days differed among the cameras in each site. In this study, we used photos of animals, and we did not handle animals directly. All cameras were placed at heights of 30–50 cm above the forest floor and were tied to tree trunks using fabric belts to reduce damage to the trees.Because the terrain and level of regulations to conduct this study differed by the study site, we employed different layouts of camera stations at each study site. In the DVCA, T. K. and three trained assistants placed 15 cameras along six forest trails totaling 9000 m, which were established and maintained by the tourist lodging facility. Because it was prohibited to establish new trails and to place cameras at sites where tourism activity would be disturbed in the study area; therefore, the trails that were longer than 1 km and relatively easily accessible were selected as camera locations to maintain consistency of trail characteristics. Cameras were placed on each trail at 50 m intervals, alternating right and left to avoid bias of photo-capture frequency caused by terrain differences. Each station was at least 25 m away from each other on the different trails (Fig. 5a). The operating days differed among the 15 cameras, i.e., mean = 942.2; SD = 152.0; range = 682–1229.Figure 5Maps of camera locations at each study site. (a) Trails and camera stations at DVCA; (b1) trails and camera stations and (b2) trail locations at LKWS; (c) a trail and camera stations at TWR.Full size imageIn the LKWS, I. M. and two trained assistants had planned to install 30 cameras, but a maximum of only 27 cameras were in operation during the study period in the LKWS, probably owing to malfunctions caused by high humidity and rain in the tropical rainforest. All cameras were placed on the trails in the riverine forest along the Menanggul River. As part of a project on the primates of the riverine forests along the Menanggul River and to assist their observation and tracking in the swampy habitat in the LKWS39, trails 200–500 m long and 1 m wide were established at 500 m intervals on both sides of the river. Of the 16 trails, we selected ten trails that were all 500 m long and placed three cameras at the points from the riverbank to the inland forest in each trail, that is, 10 m, 250 m and 500 m from the riverbank (Fig. 5b1); cameras were set up 50 m away from the trails (Fig. 5b2). Consequently, the number of operating days differed among 30 cameras, i.e., mean = 608.8; SD = 531.4; range = 28–1315.In the TWR, M. N. and A. M placed 28 and three cameras on camera stations created by overlaying a 750 × 500 m grid in May and August 2010, respectively. Cameras were placed at each grid point at 250 m intervals (Fig. 5c). The operating days differed significantly among the 28 cameras, that is, mean = 177.9; SD = 123.2; range = 26–539.Temporal activity analysisWe defined non-independent photo capture events as consecutive photos of the same or different individuals of the same species taken within a 30-min interval and removed these photos from the analysis. We plotted the activity patterns of each species using a von Mises kernel40,41 using the package activity42 in R version 4.0.243. We estimated the activity level of animals with more than ten independent photo-capture events as indicated in the previous studies26,44. For our analysis, we pooled the images from all study sites if the photo number of a species was less than 10 in any study locations. If that was not the case, we used the package activity42 to compare species activity levels across the three research sites using a Wald test with Bonferroni correction for multiple pairwise comparisons. When there were significant differences, we separately estimated activity levels by the study sites. When there were no significant differences among the sites, we pooled the photo numbers to estimate activity levels.We divided a day into three periods: nighttime (19:00–04:59 h local time (GMT + 8)); daytime (07:00–16:59 h); and twilight (05:00–06:59 h and 17:00–18:59 h). During the study period, twilight hours essentially corresponded to 1 h between sunset and sunrise, at 5:54–6:25 and 17:50–18:25 in DVCA, 5:51–6:23 and 17:47–18:25 in LKWS, and 5:50–6:21 and 17:46–18:22 in TWR (data from https://www.timeanddate.com). After converting the time data of each photo-capture event into radians, we fitted a circular kernel density distribution estimated by 10,000 bootstrap resampling to radian time data, and we estimated the percentage of active time in each period. We then categorized the activity patterns of photo-captured carnivore species into four categories: nocturnal (active at night); crepuscular (active during twilight periods); diurnal (active during daytime); and cathemeral (active in all periods). We defined the activity pattern of the species as showing a statistically higher proportion of photo-captures at nighttime, daytime, and twilight periods than at other periods, such as nocturnal, diurnal, and crepuscular, respectively. When photo-capture proportions showed no differences among the three periods, we defined the activity pattern as cathemeral. For species with substantial sample size (50  More