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Article

Comparative Study on Blowfly-Derived DNA and Camera Trapping in Assessing Mammalian Diversity in Subtropical Forests

by
Pingshin Lee
1,2,*,
Tianyi He
1,2,
Minhui Dong
1,2,
Qiang Huang
1,2,
Xiang Zhou
3,
Jun Liao
3,
Xiaochun Chen
3,
Xiaobing Wu
1,2,
Suk-Ling Wee
4,5,* and
Jinmin Chen
1,2,*
1
College of Life Sciences, Anhui Normal University, Wuhu 241000, China
2
Anhui Provincial Key Laboratory of the Conservation and Exploitation of Biological Resources, Anhui Normal University, Wuhu 241000, China
3
The Jiulongfeng Nature Reserve of the Huangshan Mountain, The Paradise Foundation, The Green Anhui, Huangshan 245000, China
4
Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
5
Centre for Insect Systematics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
*
Authors to whom correspondence should be addressed.
Forests 2023, 14(11), 2180; https://doi.org/10.3390/f14112180
Submission received: 15 September 2023 / Revised: 28 October 2023 / Accepted: 30 October 2023 / Published: 1 November 2023
(This article belongs to the Special Issue DNA Barcoding for Species Identification of Forest Organisms)
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Abstract

:
Mammalian DNA derived from invertebrates (iDNA), including blowfly iDNA, is an alternative tool to conventional camera trapping in assessing mammalian diversity. The method has been used in tropical mammal surveillance but no attempt has been made to compare the efficacy of blowfly iDNA and camera trapping for monitoring mammal diversity in subtropical forests. We compared the blowfly iDNA monitoring with camera trapping to assess mammal diversity in the subtropical Jiulongfeng Nature Reserve (JLF), China, over a one-month period. The camera traps captured 2508 animal photos of 11 species belonging to four orders, eight genera, and eight families, whereas the blowfly iDNA method successfully detected ten species from six orders, eight genera, and eight families in JLF. Both methods were complementing each other instead of competing due to the low overlaps of mammal species detected. Of the total number of mammal species listed in JLF’s threatened list, 40% and 10% were detected through camera traps and blowfly iDNA methods, respectively. The estimated species richness curves indicated that combining camera traps and blowfly traps would increase the detection of mammal species. The strategy would significantly contribute to mammalian diversity surveillance and conservation programs in the tropical and subtropical forests.

1. Introduction

Declining global biodiversity in the face of development that causes habitat fragmentation and losses, and ecosystem damage, is a great challenge to biodiversity conservation efforts [1,2]. With the constant decline in biodiversity, such that global vertebrate species populations have decreased by 68% over 46 years [3,4], it is necessary to adopt a more effective strategy to assess mammal species diversity, especially those vulnerable and threatened taxa, before they vanish from the face of the Earth.
Terrestrial mammalian diversity in tropical and subtropical forests may be driven by topographic complexity, whereby the topographically heterogeneous habitats and vegetation composition influence communities by modulating intraspecific and interspecific interactions, resulting in a high degree of mammal diversity [5]. In Asia, terrestrial mammals are particularly threatened due to habitat loss caused by the combined impact of deforestation and forest fragmentation [6].
Infrared cameras are the most commonly used method for surveying and monitoring the presence and diversity of terrestrial mammals [7,8,9,10]. Mammal monitoring using infrared cameras is non-invasive, thus enabling natural monitoring in non-distractive and destructive manners, and is least affected by environmental conditions [11]. Infrared cameras also work well in forests with reduced visibility and low encounter rates of medium- and large-size terrestrial mammals [12], in addition to the potential detection of a variety of rare and endangered mammals [13]. It is also currently more labor- and time-saving for monitoring mammal diversity as compared to the transect-sampling methods such as tracks and signs surveys, and sightings [14].
Nevertheless, there are some limitations in camera trapping, such as the inability to monitor elusive mammals or mammals with a low movement area or frequency in the forests [15]. The utilization of infrared cameras in mammal monitoring also requires a comprehensive camera locus and extensive expertise in species identification based on phenotypic characteristics, which proved to be challenging when dealing with cryptic species [16].
Mammalian DNA obtained through invertebrate DNA (iDNA) coupled with high-throughput sequencing technology has been recently introduced as an innovative strategy to survey mammal species diversity [17,18,19,20,21,22,23]. The iDNA concept has been successfully demonstrated using leeches [22], mosquitoes [24], dung beetles that feed on vertebrate dung [25], and the blowfly [21,26,27,28], in which their gut contents likely contain persisting fragmented mammalian DNA due to their dependence upon vertebrate blood or dung as a food source. The results suggested that iDNA is a promising alternative to conventional monitoring methods as it could detect rare and endangered species rather than other commonly used methods [18,29,30]. For instance, Ji et al. [31] detected 15 threatened and near-threatened vertebrate species from leech iDNA that would have not been detected without targeted, taxon-specific traditional surveys. The iDNA method can also detect a wider range of species and body sizes than is possible with other methods [21,30] and is able to distinguish cryptic mammalian species that camera trapping could not [17,22]. However, most of the species detected using the iDNA method were on average smaller in size than those captured by infrared cameras [19]. Recent studies showed that the iDNA method is more sensitive in mammal detection and requires a lower sampling effort compared with infrared camera monitoring [7].
Despite the many positive outcomes of the use of iDNA over conventional camera trapping, there is a need to compare the efficacy of infrared camera monitoring and iDNA methods to better evaluate their possible bias, advantages, or disadvantages [32]. Most of the published research on mammal diversity monitoring using iDNA was carried out in the tropics [19,20,21,28], and fewer studies used iDNA to monitor mammal diversity in the subtropical region, especially blowfly-derived DNA studies. There was only one incidental detection in a temperate region [33], although there have been studies on detecting and monitoring mammal species diversity through environmental DNA, including soil, water and feces [34].
In this study, we seek to compare the mammalian diversity and rate of coverage of rare and threatened species as detected by blowfly iDNA and infrared camera methods in Jiulongfeng Nature Reserve (JLF), China. The findings would significantly contribute to the optimization of biodiversity conservation strategy, particularly the endangered and threatened terrestrial mammal species in the subtropical region.

2. Materials and Methods

2.1. Study Sites

Jiulongfeng Nature Reserve (JLF; east longitude 117°58′–118°04′, north latitude 30°04′–30°08′), Huangshan District, Anhui Province, is located in Huangshan-Huaiyu Mountains, which is one of the 32 priority areas for land conservation in China’s Biodiversity Strategy and Action Plan 2011–2030. The protected forest covers an area of 37 km2. The climate of this region is humid subtropical with hot and humid summers and cold autumns and winters. The average annual temperature is 15.5 °C with a wide temperature range from −13.5 to 36.0 °C [35]. The average annual rainfall is 1759 mm with a monthly rainfall range of 10.2–755.6 mm, mainly concentrated in April to September each year [36,37].
The JLF is rich in wildlife diversity, with a record of 289 vertebrate species belonging to 84 families and 29 orders, including 52 mammal species from 19 families and 8 orders (Table S1). There are 35 species of state key protected animals in the reserve, including 5 species and 30 species of State Grade I- and State Grade II-protected animals, respectively. In recent years, protected species such as black muntjac (Muntiacus crinifrons), serow (Capricornis sumatraensis), and yellow muntjac (Muntiacus reevesi) have been detected by infrared cameras in JLF [38].

2.2. Field Survey with Infrared Camera

A 27-day field survey was carried out in JLF from 19 July to 14 August 2021. The survey consisted of two transect lines: (i) transect JLF (totaling 12 km) and (ii) transect Yanglong (YL)-Yanghu (YH) (totaling 13.6 km; transect YL = 8.7 km, transect YH = 4.9 km). The survey incorporated both camera traps and baited blowfly traps (Figure 1).

2.2.1. Infrared Camera Trapping

Fifteen infrared motion-triggered cameras (Yianws L710, Shenzhen, China) were placed in the two transects, in which ten were placed on transect JLF and five were placed on transect YL-YH. The cameras, fixed at, ca., 50 cm height above the ground, were placed at a distance of 0.2 to 1 km at potential animal trails or an old logging road known to have high detection probabilities for 24 h of continuous monitoring [39]. The duration of 24 h of continuous operation for each camera was recorded as an effective day. Data were collected in the form of photographs. Multiple successive photo-captures (<30 min apart) of the same species were considered as an independent monitoring event [40]. The effective monitoring time of 15 infrared cameras was 513 days (Table S2). Mammal species were identified based on morphological characteristics following Smith et al. [41].

2.2.2. Blowfly Trapping and Identification

Fifteen rotting fish-baited traps were employed in blowfly sampling. The cylindrical-shaped traps (45 cm height × 20 cm diameter) were made of nylon mesh (1 mm mesh size) (Figure 1). The blowfly traps were hung at 1 m above the ground on tree branches and at sites with partial shade and adequate sunlight exposure. The distance between any two nearest blowfly traps was between 0.05 and 1 km, and the distance between any nearest camera trap and blowfly trap was within 1 km. Blow fly traps were serviced every 24 h by emptying trap captures. The sampled blowflies were stored in different vials according to transects, traps, and dates, and kept at −20 °C until required for further examination. Blowflies were morphologically identified up to Chrysomya or Lucilia genus [42] under a stereomicroscope before proceeding for further molecular analysis. A total of 7487 blowfly individuals were collected throughout the sampling period. Blowflies captured from different fly traps per sampling day were pooled according to each of the two transects. For each sampling day, 100 individual blowflies were randomly chosen per transect for further analysis. For sampling days that collected less than 100 blowflies, all blowflies were included in molecular analysis. This resulted in a total of 3189 blowflies needed to be further analyzed.

2.3. Identification and Analysis of Mammalian Species from the Blowfly-Derived DNA

The guts of 3189 blowflies were individually dissected with sterilized forceps and pooled (five individual blowfly guts per tube) for DNA extraction using the NucleoSpin kit (Qiagen, Düren, Germany) by following the manufacturer’s protocol. The DNA samples were further pooled by day and transect. DNA extracts were then sent to Novogene sequencing company, China, for subsequent analysis. The universal primers designed for mammals, Uni-mini-barF and RonPingR [27], were used to amplify 205 bp COI mini barcodes using Phusion® High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, USA) with negative controls included. After library preparation using TruSeq® DNA PCR-Free Sample Preparation Kit, paired-end sequencing was performed on an Illumina, Inc. (San Diego, CA, USA) NovaSeq PE250 sequencing platform (2 × 250 bp reads). A total of 4,222,767 paired-end reads were generated. The outputs (FASTQ) were submitted to NCBI Sequence Read Archive under the accession number of SRP356538. Primer and barcode removal was followed by read splicing and filtering using FLASH according to Magoč et al. (2011) [43]. Then, filtering and chimera removal steps followed Caporaso et al. (2010) [44], and the resulting sequences were clustered into Operational Taxonomic Units (OTUs) based on 97% sequence identity.
The resulting DNA metabarcodes were uploaded to the Barcode of Life Data Systems (BOLD) and are available in the public dataset DS-HSJLF. For taxonomic assignment, species names were assigned to DNA metabarcodes that had sequence similarity matches of 98% and above to public barcodes on the BOLD database following Lee et al. (2016) (in the case of conflicts, see Zeale et al., 2011) [21,45].

2.4. Statistical Analysis

Based on the list of mammal species recorded in JLF using various survey methods (Table S1), we calculated the percentage of mammal species detected using each of the methods, i.e., camera traps and iDNA method (excluding Muridae from Table 1 due to inability to distinguish the species from camera trap photos). We also calculated the percentage of threatened mammal species covered by each method based on the IUCN status of the recorded mammals (excluding Muridae and Mesechinus sp. from Table 1). For each method, EstimateS version 9.1.0 [46] was used to compute the expected species richness (using Chao1), and rarefaction curves of expected species richness were generated based on cumulative sampling days. Venn diagram was also constructed to analyze species and group overlap between the two methods. We also calculated the sampling completeness ratio (detected species richness/predicted species richness) [47] of the two methods.

3. Results

A total of six orders, eleven families, and 17 mammal species were detected in JLF through both camera trapping and blowfly iDNA monitoring within 513 and 405 trap-days, respectively (Table 1). Between both methods, we detected three families and four species from Artiodactyla, two families and five species from Carnivora, one family and one species Chiroptera, two families and two species from Insectivora, one family and two species from Primates, and two families and three species from Rodentia (Table 1).
The camera traps detected four orders of mammals, namely Artiodactyla, Carnivora, Primates, and Rodentia, while the blowfly iDNA method detected six, with two additional orders, Chiroptera and Insectivora (Figure 2). Two major mammals were detected through camera trapping, Artiodactyla and Carnivora (36.36% each), followed by 18.18% Primates and 9.1% Rodentia (Figure 2). The mammals detected through the blowfly iDNA method were distributed more evenly among six orders, with 20% for Carnivora, Insectivora, Rodentia, and Primates and 10% for Artiodactyla and Chiroptera (Figure 2). Mammals detected through camera trapping were evenly distributed among seven families (about 18% for each of the four, and about 9% for each of the three). Similarly, mammals detected through blowfly trapping were evenly distributed among eight families (20% for each of the two, and 10% for each of the six).
A total of 2508 photos were taken by the camera traps, belonging to 4 orders, 8 families, 9 genera, and 11 species, accounting for 21.15% of the total number of mammal species recorded from JLF (Table 1). Among the 11 species, the infrared camera successfully detected five national protected animals—black muntjac (Muntiacus crinifrons), serow (Capricornis sumatraensis), Malayan porcupine (Hystrix brachyura), Tibetan macaque (Macaca thibetana huangshanensis), and macaque (Macaca mulatta) (Figure 3). This includes two IUCN vulnerable species—Capricornis sumatraensis and Muntiacus crinifrons—and two near-threatened species—Arctonyx collaris and Macaca thibetana huangshanensis (Figure 3).
A total of 524 independent detections of mammals were obtained (Table S2), and the majority of detections belonged to three protected species: Muntiacus reevesi with the largest number of independent detections (n = 288), followed by Arctonyx collaris (n = 60) and Macaca thibetana huangshanensis (n = 55) (Figure 4A). In contrast, mammals with rare detections were Capricornis sumatraensis (n = 1), Martes flavigula (n = 2), and Herpestes urva (n = 3) (Figure 4; excluding Muridae from Table S2).
The Blowfly iDNA method detected 10 species that belonged to six orders, eight families, and nine genera, accounting for 19.23% of the total number of mammal species (n = 52) known to be present in JLF (Table 1; for all species, see Table S1). Amongst the ten species detected, Macaca thibetana huangshanensis [BOLD:AAJ2469] is an IUCN Red List near-threatened species, and Macaca mulatta [BOLD:ADX8484] is a National Level II-protected species in China. In addition, the blowfly-derived DNA method also detected a big brown bat, Eptesicus serotinus [BOLD:ABY8747]; Chinese ferret-badger, Melogale moschata [BOLD:ADC7520]; Asian gray shrewm, Crocidura crocidura [BOLD:AAU0727]; and hedgehog, Mesechinus sp. [BOLD:ADW9183]. Two domestic mammals, cattle (Bos taurus) and cat (Felis catus), as well as some non-mammal groups, including two bird species (Lophura nycthemera; Lxos mcclellandii), three snake species (Cyclophiops major; Macropisthodon rudis; Pareas boulengeri; Ptyas dhumnades), and three amphibian species (Bufo gargarizans; Odorrana schmackeri; Quasipaa spinosa), were also detected using the blowfly-derived DNA method (Table 1). These animals were excluded from further analysis because our comparative study only focused on wild mammals. The results of high-throughput analysis of the blowfly-derived DNA method also included three blowfly species, including two from Chrysomya [BOLD:AAA5667, BOLD:ACD5557] and one from Lucilia [BOLD:AAC3450].
In JLF, the number of mammal species detected by the camera traps (n = 11) and using the blowfly-derived DNA method (n = 10) were almost similar (Figure 5). Only four detected mammal species were shared by both methods—Paguma larvata, Sus scrofa, Macaca mulatta, and Macaca thibetana huangshanensis (Figure 5). Out of 10 IUCN threatened species recorded at JLF (including endangered, vulnerable, and near-threatened species; Table S1), the camera trapping and blowfly-derived DNA method detected 40% and 10% of the total threatened species, respectively.
The estimated number of species in JLF was 11 for camera tapping and 10 for the blowfly-derived DNA method (Figure 6). The sampling saturation was 1.0 for the camera traps and 0.83 for the blowfly-derived DNA method. The estimated number of species after combining detections from camera trapping and the blowfly-derived DNA method was 17 (Figure 6), and the sampling saturation was 0.85.

4. Discussion

This is the first study comparing the blowfly iDNA method and camera trapping for mammal diversity assessment in a subtropical forest. The low overlap of mammal species detected using both methods in this study is similar to the previous comparative study of both methods conducted in a tropical forest [19,21]. Of the ten species detected by the blowfly iDNA, six were not shared by the camera trapping. These included two rodent species, a hedgehog species, a shrew species, a medium-sized carnivore species, and a bat species, which also explains the higher number of orders detected using the blowfly-derived DNA method. This is not surprising, as the blowfly iDNA method could detect a wider range of mammal diversity from small- to large-sized, including arboreal and flying mammals, although certain species might not be sampled by blowflies [17,21]. Although small mammals such as rodents, shrews, and flying mammals could also be photographed by the infrared camera, it is often difficult to distinguish the species based on the photos taken due to their small size and fast-moving behavior.
Seven out of the eleven species captured by the infrared cameras were mostly medium- to large-sized artiodactylas and carnivores. This is because the infrared camera is more likely to detect medium- to large-size mammals [48]. The blowfly iDNA method also detected a few medium- to large-sized mammals, including Sus scrofa, Macaca thibetana huangshanensis, and Macaca mulatta. However, an Artiodactyla, Muntiacus reevesi, which has a relatively large number of photos captured by the camera traps, was not detected using the blowfly iDNA method.
Such sampling bias by blowflies has been reported in similar studies by Lee et al. [21] and Rodgers et al. [30]. This could be due to primer amplification or flies’ feeding preference that remains to be addressed [21,26,30]. Previous work by Lee et al. [27] has shown the effectiveness of COI primers (89% of 41 mammal species) in the amplification of mammalian sequences. Hence, the possibility of COI primer mismatch with the mammalian binding sites is unlikely but cannot be entirely ruled out. Nevertheless, the low overlap of mammal species detection using camera traps and the blowfly-derived DNA method in the subtropical forest provides further support to Gogarten et al.’s [19] observation in the tropics that both methods complement each other instead of competing.
The camera trapping was generally more effective in detecting threatened or protected species than the blowfly iDNA method over the same sampling period. This is possibly due to the fact that most large-sized mammals detected by camera traps were also protected mammals, including Arctonyx collaris, Muntiacus crinifrons, and Capricornis sumatraensis. However, both camera traps and the blowfly-derived DNA method were equally effective in detecting protected primates at JLF (Macaca thibetana huangshanensis and Macaca mulatta). Interestingly, a previous study by Lee et al. [21] showed that the blowfly iDNA method successfully detected all primate species recorded from tropical forests, including a new record of a near-threatened primate species that was not detected using traditional methods. This suggested that the blowfly iDNA method might be particularly effective for detecting primates [21,30]. However, both methods showed a low coverage of threatened species in protected areas, possibly due to geographic and temporal limitations of sampling, or due to the fact that existing published reports were not updated [21]. For example, Neofelis nebulosa nebulosi has not been detected by infrared cameras for nearly 5 years at JLF (unpublished data) despite being recorded in the check list of mammals [37].
Blowfly iDNA also detected non-mammals, including birds, snakes, and amphibians and birds, as in the case of previous similar studies that detected non-mammal vertebrates [17,21,30]. This could be due to the generalist feeding behavior of blowflies that concentrate vertebrate DNA in their bodies upon opportunistic sampling, although the host feeding preference of such saprophagous or coprophagous generalists remains unknown and requires further investigations [17]. These species were not detected by camera traps except for birds; thus, the blowfly iDNA method may be a potential monitoring tool for reptiles and amphibians in subtropical and tropical forests [30]. If the monitoring of non-mammals (such as amphibians) is to be expanded in future, it is necessary to consider the use of universal primers for vertebrates (such as 12S and 16S) for amplification and sequencing [30]. Since the accuracy of metabarcoding datasets relies on the use of conserved amplification primers to recover target taxa, for most amplicon-based metabarcoding applications, marker selection should receive more attention, and existing marker selection should be broadened [49].
The estimated species richness for both methods were similar. The sampling saturation of camera traps reached 1.0, but those of blowfly traps did not reach 1.0. The results may be due to limitations in the selected location of camera traps, as some mammal species may be limited to specific habitat types [50,51], and species detection can be further improved by increasing sampling transects and also sampling seasons (from late spring to summer) in subsequent studies. Blowflies, in comparison, could fly up to a few kilometers away from the sampling location of the targeted mammal species, and are less habitat-restricted for species detection [52]. Mammal species detection using blowfly traps can be further experimented by having different baits as attractants. It can also be further improved by increasing the sampling effort in future studies, such as more sampling transects and a longer sampling period covering late spring to summer where mammals are most active. In addition, although we included negative controls in the iDNA study and no band was presented, there was no attempt for its subsequent sequencing. Therefore, it is best practice to sequence negative controls to rule out any possibility of contamination that may present negative results.
Although camera trapping has proved to be the least labor-intensive method [14], a large number of photos taken by the camera need manual recognition, which requires a lot of time and profound professional knowledge. The use of infrared cameras is potentially limited by installation difficulties and high maintenance costs. The cost of the blowfly iDNA method is relatively low, and the trapping regime is simple. The iDNA method, however, has its limitations in species monitoring as it could only provide information on species richness rather than species abundance as compared to camera trapping. The mobility and abundance of blowflies allow rapid collection across seasons and habitats or microhabitats as demonstrated in the leech iDNA study [22]. This is especially true for subtropical forests, as the increase in rainfall during summer or late spring brought many challenges in blowfly trapping as experienced in this study.
Despite having different climatic conditions and species richness as in tropical forests, our present study also showed that both blowfly iDNA and camera trapping are complementing instead of competing with each other in assessing mammal diversity in the subtropical forests. Both methods complement each other in their own limitations and increase chances for mammal species detection, including elusive species. Within a 30-day sampling period, the expected species richness when combining both methods showed an improved species detection with the same sampling frequency. Thus, the strategy of combining both camera trapping and blowfly iDNA monitoring would optimize biodiversity monitoring and assessment, especially under the short monitoring period and limited sampling times. This study will serve as an important reference for future studies to further develop the blowfly iDNA method for vertebrate diversity in the tropical and subtropical regions.

5. Conclusions

This is the first effort to compare the efficacy of the iDNA method with the infrared camera trapping for monitoring mammals in a subtropical forest. The results indicate that the two methods detect almost a similar number of species in JLF but with a low overlap of common mammal species. In addition, the infrared camera traps detect more threatened species than the iDNA method, whereby the former is more likely to detect medium- to large-sized mammals while the latter is more likely to detect small- to medium-sized mammals, including fast-moving and flying mammals. This information is particularly helpful when a specific group of mammals is targeted for monitoring so that the best monitoring tool can be employed. Our analyses also indicate that combining camera and blowfly traps will detect more species. However, it will be prudent to prioritize one monitoring method over the other for programs with limited resources if the interest is either threatened species or elusive mammals. In conclusion, combining camera trapping and iDNA methods would optimize mammalian diversity monitoring programs and conservation. Further studies in different climatic zones and regions and addressing uncertainties such as blowfly feeding preferences and the possible bias in primer amplification using integrative monitoring methods can help to enhance future biodiversity assessments.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f14112180/s1, Table S1: List of mammal species recorded at Jiulongfeng Nature Reserve, Huangshan of Anhui Province; Table S2: Number of camera photos captured in Jiulongfeng Provincial Nature Reserve, Huangshan of Anhui Province.

Author Contributions

Conceptualization, P.L., S.-L.W. and J.C.; methodology, all authors; investigation, P.L., T.H. and Q.H.; resources, X.Z., X.C. and X.W.; data curation, P.L., M.D. and J.L.; writing—original draft preparation, P.L., T.H. and M.D.; writing—review and editing, all authors; visualization, P.L. and S.-L.W.; supervision, P.L. and S.-L.W.; project administration, P.L., T.H. and M.D.; funding acquisition, P.L. and J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (NSFC 32001222) and the Innovation and Entrepreneurship Training Program for Undergraduates (X202210370039) to P.L., and the National Natural Science Foundation of China (NSFC 31900323) to J.C.

Data Availability Statement

Sequence data are available on the NCBI short read archive, with the SRA BioProject accession no. SRP356538 (https://www.ncbi.nlm.nih.gov/sra/SRP356538, accessed on 10 October 2023).

Acknowledgments

The infrared camera data and sampling approval provided by The Jiulongfeng Nature Reserve of the Huangshan Mountain, The Paradise Foundation, are greatly acknowledged.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. He, C.; Liu, Z.; Tian, J.; Ma, Q. Urban expansion dynamics and natural habitat loss in China: A multiscale landscape perspective. Glob. Chang. Biol. 2014, 20, 2886–2902. [Google Scholar] [CrossRef]
  2. Wilson, M.C.; Chen, X.Y.; Corlett, R.T.; Didham, R.K.; Ding, P.; Holt, R.D.; Holyoak, M.; Hu, G.; Hughes, A.C.; Jiang, L. Habitat fragmentation and biodiversity conservation: Key findings and future challenges. Landsc. Ecol. 2016, 31, 219–227. [Google Scholar] [CrossRef]
  3. Beaudrot, L.; Ahumada, J.A.; O’Brien, T.; Alvarez-Loayza, P.; Boekee, K.; Campos-Arceiz, A.; Eichberg, D.; Espinosa, S.; Fegraus, E.; Fletcher, C. Standardized Assessment of Biodiversity Trends in Tropical Forest Protected Areas: The End Is Not in Sight. PLoS Biol. 2016, 14, e1002357. [Google Scholar] [CrossRef]
  4. WWF. Living Planet Report 2020—Bending the Curve of Biodiversity Loss; Almond, R.E.A., Grooten, M., Petersen, T., Eds.; WWF: Gland, Switzerland, 2020. [Google Scholar]
  5. Hawkins, B.A.; Diniz-Filho, J.A.F.; Jaramillo, C.A.; Soeller, S.A. Post-Eocene climate change, niche conservatism, and the latitudinal diversity gradient of New World birds. J. Biogeogr. 2006, 33, 770–780. [Google Scholar] [CrossRef]
  6. Schipper, J.; Chanson, J.S.; Chiozza, F.; Cox, N.A.; Hoffmann, M.; Katariya, V.; Lamoreux, J.; Rodrigues, A.S.; Stuart, S.N.; Temple, H.J. The status of the world’s land and marine mammals: Diversity, threat, and knowledge. Science 2008, 322, 225–230. [Google Scholar] [CrossRef] [PubMed]
  7. Abrams, J.F.; Hoerig, L.A.; Brozovic, R.; Axtner, J.; Crampton-Platt, A.; Mohamed, A.; Wong, S.T.; Sollmann, R.; Yu, D.W.; Wilting, A. Shifting up a gear with iDNA: From mammal detection events to standardized surveys. Cold Spring Harb. Lab. 2019, 56, 1637–1648. [Google Scholar] [CrossRef]
  8. Burton, A.C.; Neilson, E.; Moreira, D.; Ladle, A.; Steenweg, R.; Fisher, J.T.; Bayne, E.; Boutin, S.; Stephens, P. REVIEW: Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 2015, 52, 675–685. [Google Scholar] [CrossRef]
  9. Nichols, J.D.; O’Connell, A.F.; Karanth, K.U. Camera Traps in Animal Ecology and Conservation: What’s Next? Springer: Tokyo, Japan, 2011; pp. 253–263. [Google Scholar]
  10. Trolliet, F.; Huynen, M.C.; Vermeulen, C.; Hambuckers, A. Use of camera traps for wildlife studies. A review. Biotechnol. Agron. Soc. Et Environ. 2014, 18, 446–454. [Google Scholar]
  11. Liu, X.; Wu, P.; He, X.; Zhao, X. Application and data mining of infra-red camera in the monitoring of species. Biodivers. Sci. 2018, 26, 850–861. [Google Scholar] [CrossRef]
  12. Ahumada, J.A.; Silva, C.E.; Gajapersad, K.; Hallam, C.; Hurtado, J.; Martin, E.; McWilliam, A.; Mugerwa, B.; O’Brien, T.; Rovero, F. Community structure and diversity of tropical forest mammals: Data from a global camera trap network. Philos. Trans. R. Soc. B Biol. Sci. 2011, 366, 2703–2711. [Google Scholar] [CrossRef]
  13. Li, X.; Bleisch, W.V.; Jiang, X. Using large spatial scale camera trap data and hierarchical occupancy models to evaluate species richness and occupancy of rare and elusive wildlife communities in southwest China. Divers. Distrib. 2018, 24, 1560–1572. [Google Scholar] [CrossRef]
  14. Roberts, N.J. Investigation into survey techniques of large mammals: Surveyor competence and camera-trapping vs. transect-sampling. Biosci. Horiz. 2011, 4, 40–49. [Google Scholar] [CrossRef]
  15. Guschanski, K.; Vigilant, L.; McNeilage, A.; Gray, M.; Kagoda, E.; Robbins, M.M. Counting elusive animals: Comparing field and genetic census of the entire mountain gorilla population of Bwindi Impenetrable National Park, Uganda. Biol. Conserv. 2009, 142, 290–300. [Google Scholar] [CrossRef]
  16. Bickford, D.; Lohman, D.J.; Sodhi, N.S.; Ng, P.K.; Meier, R.; Winker, K.; Ingram, K.K.; Das, I. Cryptic species as a window on diversity and conservation. Trends Ecol. Evol. 2007, 22, 148–155. [Google Scholar] [CrossRef] [PubMed]
  17. Calvignac-Spencer, S.; Merkel, K.; Kutzner, N.; Kühl, H.; Boesch, C.; Kappeler, P.M.; Metzger, S.; Schubert, G.; Leendertz, F.H. Carrion fly-derived DNA as a tool for comprehensive and cost-effective assessment of mammalian biodiversity. Mol. Ecol. 2013, 22, 915–924. [Google Scholar] [CrossRef]
  18. Drinkwater, R.; Jucker, T.; Potter, J.H.; Swinfield, T.; Coomes, D.A.; Slade, E.M.; Gilbert, M.T.P.; Lewis, O.T.; Bernard, H.; Struebig, M.J. Leech blood-meal invertebrate-derived DNA reveals differences in Bornean mammal diversity across habitats. Mol. Ecol. 2021, 30, 3299–3312. [Google Scholar] [CrossRef] [PubMed]
  19. Gogarten, J.F.; Hoffmann, C.; Arandjelovic, M.; Sachse, A.; Merkel, K.; Dieguez, P.; Agbor, A.; Angedakin, S.; Brazzola, G.; Jones, S. Fly-derived DNA and camera traps are complementary tools for assessing mammalian biodiversity. Environ. DNA 2020, 2, 63–76. [Google Scholar] [CrossRef]
  20. Kocher, A.; de Thoisy, B.; Catzeflis, F.; Valière, S.; Bañuls, A.L.; Murienne, J. iDNA screening: Disease vectors as vertebrate samplers. Mol. Ecol. 2017, 26, 6478–6486. [Google Scholar] [CrossRef]
  21. Lee, P.-S.; Gan, H.M.; Clements, G.R.; Wilson, J.-J. Field calibration of blowfly-derived DNA against traditional methods for assessing mammal diversity in tropical forests. Genome 2016, 59, 1008–1022. [Google Scholar] [CrossRef]
  22. Schnell, I.B.; Sollmann, R.; Calvignac-Spencer, S.; Siddall, M.E.; Yu, D.W.; Wilting, A.; Gilbert, M.T. iDNA from terrestrial haematophagous leeches as a wildlife surveying and monitoring tool-prospects, pitfalls and avenues to be developed. Front. Zool. 2015, 12, 1–14. [Google Scholar] [CrossRef]
  23. Schnell, I.B.; Thomsen, P.F.; Wilkinson, N.; Rasmussen, M.; Jensen, L.R.; Willerslev, E.; Bertelsen, M.F.; Gilbert, M.T.P. Screening mammal biodiversity using DNA from leeches. Curr. Biol. 2012, 22, 262–263. [Google Scholar] [CrossRef]
  24. Logue, K.; Keven, J.B.; Cannon, M.V.; Reimer, L.; Siba, P.; Walker, E.D.; Zimmerman, P.A.; Serre, D. Unbiased characterization of Anopheles mosquito blood meals by targeted high-throughput sequencing. PLoS Neglected Trop. Dis. 2016, 10, e0004512. [Google Scholar] [CrossRef] [PubMed]
  25. Gillett, C.P.; Johnson, A.J.; Barr, I.; Hulcr, J. Metagenomic sequencing of dung beetle intestinal contents directly detects and identifies mammalian fauna. BioRxiv 2016, 12, 074849. [Google Scholar]
  26. Calvignac-Spencer, S.; Leendertz, F.H.; Gilbert, M.T.P.; Schubert, G. An invertebrate stomach’s view on vertebrate ecology: Certain invertebrates could be used as “vertebrate samplers” and deliver DNA-based information on many aspects of vertebrate ecology. BioEssays 2013, 35, 1004–1013. [Google Scholar] [CrossRef] [PubMed]
  27. Lee, P.-S.; Sing, K.-W.; Wilson, J.-J. Reading mammal diversity from flies: The persistence period of amplifiable mammal mtDNA in blowfly guts (Chrysomya megacephala) and a new DNA mini-barcode target. PLoS ONE 2015, 10, e0123871. [Google Scholar] [CrossRef] [PubMed]
  28. Schubert, G.; Stockhausen, M.; Hoffmann, C.; Merkel, K.; Vigilant, L.; Leendertz, F.H.; Calvignac-Spencer, S. Targeted detection of mammalian species using carrion fly-derived DNA. Mol. Ecol. Resour. 2015, 15, 285–294. [Google Scholar] [CrossRef]
  29. Carvalho, C.S.; De Oliveira, M.E.; Rodriguez-Castro, K.G.; Saranholi, B.H.; Galetti, P.M., Jr. Efficiency of eDNA and iDNA in assessing vertebrate diversity and its abundance. Mol. Ecol. Resour. 2022, 22, 1262–1273. [Google Scholar] [CrossRef]
  30. Rodgers, T.W.; Xu, C.C.; Giacalone, J.; Kapheim, K.M.; Saltonstall, K.; Vargas, M.; Yu, D.W.; Somervuo, P.; McMillan, W.O.; Jansen, P.A. Carrion fly-derived DNA metabarcoding is an effective tool for mammal surveys: Evidence from a known tropical mammal community. Mol. Ecol. Resour. 2017, 17, 133–145. [Google Scholar] [CrossRef]
  31. Ji, Y.; Baker, C.C.; Popescu, V.D.; Wang, J.; Wu, C.; Wang, Z.; Li, Y.; Wang, L.; Hua, C.; Yang, Z. Measuring protected-area effectiveness using vertebrate distributions from leech iDNA. Nat. Commun. 2022, 13, 1555. [Google Scholar] [CrossRef]
  32. Massey, A.L.; Bronzoni, R.V.d.M.; da Silva, D.J.F.; Allen, J.M.; de Lázari, P.R.; dos Santos-Filho, M.; Canale, G.R.; Bernardo, C.S.S.; Peres, C.A.; Levi, T. Invertebrates for vertebrate biodiversity monitoring: Comparisons using three insect taxa as iDNA samplers. Mol. Ecol. Resour. 2022, 22, 962–977. [Google Scholar] [CrossRef]
  33. Cutajar, T.P.; Pulsford, S.A. Incidental invertebrate-derived DNA detection of invasive and threatened species in temperate dry Southeast Australian forest. Austral Ecol. 2023, 48, 774–786. [Google Scholar] [CrossRef]
  34. Chen, L.; Wu, L.; Liu, Y.; Xu, H. Application of environmental DNA metabarcoding in ecology. Acta Ecol. Sin. 2016, 36, 4573–4582. [Google Scholar]
  35. Cheng, F.-Y.; Cheng, Q.-Q.; Cheng, L.-H.; Chen, M.-Y.; Cheng, H.-J.; Yan, Y.-Z. Age determination of Phoxinus oxycephalus from the Jiulong Peak of the Huangshan Mountain. Chin. J. Ecol. 2014, 33, 2108. [Google Scholar]
  36. Yearly Statistics in Huangshan. Available online: https://tjj.huangshan.gov.cn (accessed on 12 July 2023).
  37. Wu, X.B. Comprehensive Investigation Report of Jiulongfeng Provincial Nature Reserve, Huangshan District, Anhui Province; Forestry Department of Jiulongfeng Nature Reserve of the Huangshan Mountain: Wuhu, China, 2019. [Google Scholar]
  38. Wang, C.G.; Cao, X.H.; Cao, Q.P.; Yan, L.; Wu, X.B. Application of the passive infrared sensor camera technology in monitoring the vertebrate diversity—A case study of Anhui Jiulongfeng Provincial Nature Reserve. Anhui For. Sci. Technol. 2015, 41, 5–8. [Google Scholar]
  39. Harmsen, B.J.; Foster, R.J.; Silver, S.; Ostro, L.; Doncaster, C.P. Differential use of trails by forest mammals and the implications for camera-trap studies: A case study from Belize. Biotropica 2010, 42, 126–133. [Google Scholar] [CrossRef]
  40. Tanwar, K.S.; Sadhu, A.; Jhala, Y.V. Camera trap placement for evaluating species richness, abundance, and activity. Sci. Rep. 2021, 11, 23050. [Google Scholar] [CrossRef]
  41. Smith, A.T.; Xie, Y.; Hoffmann, R.S.; Lunde, D.; MacKinnon, J.; Wilson, D.E.; Wozencraft, W.C. A Guide to the Mammals of China; Princeton University Press: Princeton, NJ, USA, 2010. [Google Scholar]
  42. Kurahashi, H. Blow flies (Insecta: Diptera: Calliphoridae) of Malaysia and Singapore. Raffles Bull. Zool. 1997, 5, 1–88. [Google Scholar]
  43. Magoč, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef]
  44. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef]
  45. Zeale, M.R.; Butlin, R.K.; Barker, G.L.; Lees, D.C.; Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Mol. Ecol. Resour. 2011, 11, 236–244. [Google Scholar] [CrossRef] [PubMed]
  46. Colwell, R.K. EstimateS: Statistical Estimation of Species Richness and Shared Species from Samples. Version 9.1.0. Available online: http://purl.oclc.org/estimates (accessed on 10 December 2022).
  47. Soberón, J.M.; Llorente, J.B.; Oñate, L. The use of specimen-label databases for conservation purposes: An example using Mexican Papilionid and Pierid butterflies. Biodivers. Conserv. 2000, 9, 1441–1466. [Google Scholar] [CrossRef]
  48. Tobler, M.W.; Carrillo-Percastegui, S.E.; Leite Pitman, R.; Mares, R.; Powell, G. An evaluation of camera traps for inventorying large-and medium-sized terrestrial rainforest mammals. Anim. Conserv. 2008, 11, 169–178. [Google Scholar] [CrossRef]
  49. Deagle, B.E.; Jarman, S.N.; Coissac, E.; Pompanon, F.; Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 2014, 10, 20140562. [Google Scholar] [CrossRef] [PubMed]
  50. Geier, A.R.; Best, L.B. Habitat selection by small mammals of riparian communities: Evaluating effects of habitat alterations. J. Wildl. Manag. 1980, 44, 16–24. [Google Scholar] [CrossRef]
  51. Goulart, F.V.B.; Cáceres, N.C.; Graipel, M.E.; Tortato, M.A.; Ghizoni, I.R., Jr.; Oliveira-Santos, L.G.R. Habitat selection by large mammals in a southern Brazilian Atlantic Forest. Mamm. Biol. 2009, 74, 182–190. [Google Scholar] [CrossRef]
  52. Lee, P.S.; Dong, M.H.; Yan, X.L.; He, T.Y.; Yu, S.F.; Wee, S.L.; Wilson, J.J. Blowfly-derived mammal DNA as mammal diversity assessment tool: Determination of dispersal activity and flight range of tropical blowflies. Biodivers. Data J. 2023, 11, e108438. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. The distribution map of infrared camera traps (A) and blowfly traps (B), and the Jiulongfeng Nature Reserve (C). Site distribution map of camera traps at Jiulongfeng Nature Reserve: JLF (29, 31, 33, 35) shows the locations of camera traps at sites of Yanglong Nature Reserve; JLF (02, 07, 08, 10, 11, 12, 13, 14, 16, 19, 21, 30) shows the locations of camera traps at Jiulongfeng Nature Reserve; Yh (001, 004, 009) shows the locations of camera traps at Yanghu Lake area.
Figure 1. The distribution map of infrared camera traps (A) and blowfly traps (B), and the Jiulongfeng Nature Reserve (C). Site distribution map of camera traps at Jiulongfeng Nature Reserve: JLF (29, 31, 33, 35) shows the locations of camera traps at sites of Yanglong Nature Reserve; JLF (02, 07, 08, 10, 11, 12, 13, 14, 16, 19, 21, 30) shows the locations of camera traps at Jiulongfeng Nature Reserve; Yh (001, 004, 009) shows the locations of camera traps at Yanghu Lake area.
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Figure 2. Composition of animal orders detected using blowfly traps (a) and camera traps (b) and composition of families detected using blowfly traps (c) and camera traps (d) in Jiulongfeng Nature Reserve in Huangshan Mountain, Anhui Province, China.
Figure 2. Composition of animal orders detected using blowfly traps (a) and camera traps (b) and composition of families detected using blowfly traps (c) and camera traps (d) in Jiulongfeng Nature Reserve in Huangshan Mountain, Anhui Province, China.
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Figure 3. Mammalian species captured by infrared cameras at the Jiulongfeng Nature Reserve: (A) Sus scrofa, (B) Macaca thibetana huangshanensis, (C) Hystrix hrachyura, (D) Muntiacus crinifrons, (E) Martes flavigula, (F) Muntiacus reevesi, (G) Macaca mulatta, (H) Arctonyx collaris, and (I) Capricornis sumatraensis. Camera settings: passive infrared sensor = 70 sampling times; flash brightness = normal; LED stealth mode = off; each trigger = 3, image delay interval = 1; night each trigger flash image = 1, image delay interval = 20 s; image resolution = 12 megapixels.
Figure 3. Mammalian species captured by infrared cameras at the Jiulongfeng Nature Reserve: (A) Sus scrofa, (B) Macaca thibetana huangshanensis, (C) Hystrix hrachyura, (D) Muntiacus crinifrons, (E) Martes flavigula, (F) Muntiacus reevesi, (G) Macaca mulatta, (H) Arctonyx collaris, and (I) Capricornis sumatraensis. Camera settings: passive infrared sensor = 70 sampling times; flash brightness = normal; LED stealth mode = off; each trigger = 3, image delay interval = 1; night each trigger flash image = 1, image delay interval = 20 s; image resolution = 12 megapixels.
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Figure 4. Number of independent detections (A) and number of photos captured (B) by infrared camera traps for each mammal species in decreasing order.
Figure 4. Number of independent detections (A) and number of photos captured (B) by infrared camera traps for each mammal species in decreasing order.
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Figure 5. Venn diagram showing the mammal order and species detected by both blowfly traps and camera traps, including species unique to each method and common species shared across different methods at Jiulongfeng Nature Reserve, China.
Figure 5. Venn diagram showing the mammal order and species detected by both blowfly traps and camera traps, including species unique to each method and common species shared across different methods at Jiulongfeng Nature Reserve, China.
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Figure 6. The expected species richness rarefaction curves of infrared camera traps and blowfly traps, and combined detections of both methods at the Jiulongfeng Nature Reserve. The dotted lines represent 95% confidence intervals for the expected species richness of each method.
Figure 6. The expected species richness rarefaction curves of infrared camera traps and blowfly traps, and combined detections of both methods at the Jiulongfeng Nature Reserve. The dotted lines represent 95% confidence intervals for the expected species richness of each method.
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Table 1. Mammal species detected using infrared camera traps and blowfly traps at Jiulongfeng Nature Reserve in Huangshan, Anhui Province.
Table 1. Mammal species detected using infrared camera traps and blowfly traps at Jiulongfeng Nature Reserve in Huangshan, Anhui Province.
SpeciesIUCN Species Red ListKey National Protection LevelCSRL Red List of Chinese SpeciesNumber of Photos CapturedCamera Trap DetectionBlowfly-Derived DNA
ARTIODACTYLA
Bovidae
Capricornis sumatraensisVUIIVU10√X
Cervidae
Muntiacus crinifronsVUIEN40√X
Muntiacus reevesiLC-VU1179√X
Suidae
Sus scrofaLC-LC176√√
CARNIVORA
Mustelidae
Arctonyx collarisNT-NT189√X
Martes flavigulaLC--5√X
Melogale moschataLC-NT X√
Viverridae
Herpestes urvaLC-NT14√X
Paguma larvataLC-NT125√√
CHIROPTERA
Vespertilionidae
Eptesicus serotinusLC-LC X√
INSECTIVORA
Erinaceidae
Mesechinus sp.- - X√
Soricidae
Crocidura attenuataLC-LC X√
PRIMATES
Cercopithecidae
Macaca mulattaLCIILC27√√
Macaca thibetana huangshanensisNTIIVU612√√
RODENTIA
Hystricidae
Hystrix brachyuraLCIILC65√X
Muridae 66√
Leopoldamys edwardsiLC-LC X√
Niviventer niviventerLC-- X√
NOTE: EN: endangered, VU: vulnerable, NT: near-threatened, LC: least concern. Key national protection levels represent level of protection for the mammal species in China. Names of mammal orders are in bold capital while families are in bold with first capital letter. √: present, X: absent.
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Lee, P.; He, T.; Dong, M.; Huang, Q.; Zhou, X.; Liao, J.; Chen, X.; Wu, X.; Wee, S.-L.; Chen, J. Comparative Study on Blowfly-Derived DNA and Camera Trapping in Assessing Mammalian Diversity in Subtropical Forests. Forests 2023, 14, 2180. https://doi.org/10.3390/f14112180

AMA Style

Lee P, He T, Dong M, Huang Q, Zhou X, Liao J, Chen X, Wu X, Wee S-L, Chen J. Comparative Study on Blowfly-Derived DNA and Camera Trapping in Assessing Mammalian Diversity in Subtropical Forests. Forests. 2023; 14(11):2180. https://doi.org/10.3390/f14112180

Chicago/Turabian Style

Lee, Pingshin, Tianyi He, Minhui Dong, Qiang Huang, Xiang Zhou, Jun Liao, Xiaochun Chen, Xiaobing Wu, Suk-Ling Wee, and Jinmin Chen. 2023. "Comparative Study on Blowfly-Derived DNA and Camera Trapping in Assessing Mammalian Diversity in Subtropical Forests" Forests 14, no. 11: 2180. https://doi.org/10.3390/f14112180

APA Style

Lee, P., He, T., Dong, M., Huang, Q., Zhou, X., Liao, J., Chen, X., Wu, X., Wee, S.-L., & Chen, J. (2023). Comparative Study on Blowfly-Derived DNA and Camera Trapping in Assessing Mammalian Diversity in Subtropical Forests. Forests, 14(11), 2180. https://doi.org/10.3390/f14112180

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