Research Article |
Corresponding author: Abhijit Das ( adscholarone@gmail.com ) Academic editor: Günter Gollmann
© 2020 Naitik G. Patel, Abhijit Das.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Patel NG, Das A (2020) Shot the spots: A reliable field method for individual identification of Amolops formosus (Anura, Ranidae). Herpetozoa 33: 7-15. https://doi.org/10.3897/herpetozoa.33.e47279
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Natural body patterns in amphibians are widely used for individual recognition. In this study, we photographed individuals of Amolops formosus for four days of sampling without handling them. We processed 301 photographs of dorsal blotch pattern in HotSpotter software and verified them visually for confirmation. We identified 160 unique individuals of A. formosus based on the images taken in the field, resulting in an abundance estimate of 180 individuals. The success rate in identifying individuals of A. formosus using the HotSpotter software was 94.3%. We tested the effect of image quality and distance on recognition efficiency. Poor image quality reduced the recognition efficiency of the software but with a careful user review it was possible to identify the individual. The difference between using only the software and software plus human confirmation was very small. This protocol is useful for rapid population estimation of frogs with natural body patterns.
Amphibia, mark recapture, pattern recognition software, population estimation
Conservation and management for any species requires explicit information about its demography, population status and dispersal patterns (
Amphibians possess a diverse range of color pattern and body markings (
We conducted fieldwork in Jamak Stream, near Maneri dam, Bhagirathi River Basin, Uttarkashi, Uttarakhand, India (39°43.8727'N, 78°31.6973'E (DDM); 1300 m asl; Fig.
Amolops formosus is a medium sized (male, maximum SVL 53 mm; female, maximum SVL 75 mm), slender bodied frog (Fig.
We employed Nocturnal Visual Encounter Surveys (NEVS) from 1900 to 2130 h (
We assumed that natural marking patterns of the individual adult frogs did not change with time during our study. We included only the dorsal patterns of A. formosus as Region of Interest (ROI) for individual identification (Fig.
We determined Matching and Non-Matching Image Score for each image for further evaluation. To test the effect of image quality on the identification efficiency, we classified the images into excellent, moderate and poor, based on image clarity, focus and resolution (Fig.
We also investigated the effect of photographic distance on individual identification based on the focal length of the camera as 100 mm ≈ 1 meter, 200 mm ≈ 2 to 3 meters, and 300 mm ≈ 5 to 6 meters. The file size of the cropped dorsal images was also negatively correlated with the photographic distance from the frog (1 meter ≈ 1563 kB to 6 meters ≈ 57.3 kB). We considered the image size as control for any effect of pixel size or image quality on the identification process. We considered the scores of matching images only since non-matching pairs should always have low scores irrespective of their distance and image quality. We carried out linear regression between the scores of matching images vs. focal length and scores for matching images vs. file size.
We calculated HotSpotter’s success rate and error rates on the basis of matching photos considered correct. The matching images were deemed incorrect if non-matching images were scored higher. Success and error rates were calculated as the number of correct and incorrect matches divided respectively by the total number of images. We prepared two capture histories to test the effect of misidentification on the estimation of abundance, one with error and the other with correct identification. For abundance estimate, we prepared individual capture histories for four occasions. Capture-recapture histories from the individual data were analyzed using closed population maximum likelihood estimator (MLE) in Program MARK (
We recorded 301 photographs taken over 20-man hours of NVES surveys, representing 160 individual A. formosus. We found 67 frogs on the first occasion and 81, 89 and 64 frogs on the next three occasions, respectively. Seventy-eight individuals were recorded once and 82 individuals were recorded more than once. Out of 82 recaptured individuals, 42 individuals were recaptured twice, 21 individuals were recaptured three times and 19 individuals were recaptured on all the occasions. Amolops formosus was most frequently encountered siting on bedrock and boulders followed by branches of the shrub and barren ground. Occasionally, we also found frogs half submerged at the stream edges. Frogs were recorded up to 2 m above the water level and horizontally within 5 m of the stream edge.
Matching pair scores ranged from 350 to 191644 with a mean value of 19069 ± 2800 and non-matching pair scores ranged from 0 to 3303 with a mean value of 1089 ± 36, respectively (Fig.
The success rate in ranking the same individual’s photo based on the similarity score by software was 94.3%, with an error rate of 5.6%. The time dependent model (Mt) was the best model to predict A. formosus abundance estimates based on lowest AIC scores (Table
Histogram depicting the distribution of scores of matching images and non-matching images of Amolops formosus (n= 301) as generated by HotSpotter software. Black bars indicate the distribution of non-matching images scores, and the grey bars indicate the distribution of matching images score. Red bar is threshold value. Grey shaded region on the left side to threshold bar is an overlapping zone between the scores of matching and non-matching images.
Dunn (1964) Kruskal-Wallis multiple comparison. The p-values adjusted with the Benjamini-Hochberg method. MSE (Matching Score Excellent images), MSM (Matching Score Moderate images), MSP (Matching Score Poor images), NMSE (Non-Matching Score Excellent images), NMSM (Non-Matching Score Moderate images), and NMSP (Non-Matching Score Poor images).
Comparison | Z | P.unadj | P.adj |
---|---|---|---|
MSE-MSM | 0.6495634 | 5.16E-01 | 5.16E-01 |
MSE-MSP | 3.1501276 | 1.63E-03 | 2.04E-03 |
MSE-NMSE | 10.4214294 | 1.98E-25 | 3.71E-25 |
MSE-NMSM | 14.6256754 | 1.93E-48 | 7.22E-48 |
MSE-NMSP | 15.5695782 | 1.17E-54 | 5.86E-54 |
MSM-MSP | 2.817835 | 4.83E-03 | 5.18E-03 |
MSM-NMSE | 10.9538365 | 6.37E-28 | 1.59E-27 |
MSM-NMSM | 15.962014 | 2.35E-57 | 1.76E-56 |
MSM-NMSP | 16.6177518 | 5.18E-62 | 7.78E-61 |
MSP-NMSE | 6.9131649 | 4.74E-12 | 7.90E-12 |
MSP-NMSM | 10.7557339 | 5.57E-27 | 1.19E-26 |
MSP-NMSP | 12.1053287 | 9.90E-34 | 2.97E-33 |
NMSE-NMSM | 3.6733033 | 2.39E-04 | 3.27E-04 |
NMSE-NMSP | 5.9042941 | 3.54E-09 | 5.31E-09 |
NMSM-NMSP | 2.9470791 | 3.21E-03 | 3.70E-03 |
Distribution of scores (log) generated by HotSpotter. The scores of MSE (Matching Score Excellent), MSM (Matching Score Moderate), and MSP (Matching Score Poor) are higher compared to NMSE (Non-Matching Score Excellent), NMSM (Non-Matching Score Moderate), and NMSP (Non-Matching Score Poor).
Model selection for Amolops formosus abundance estimation based on AICc score under closed capture recapture frame work.
Group | Model | Model Name | AIC | Delta AICc | Model likelihood | Number of Parameter |
---|---|---|---|---|---|---|
Correct identification | {M(t)} | Time dependent | -452.2 | 0.000 | 1.000 | 5 |
{M(.)} | Null Model | -449.10 | 3.4484 | 0.1783 | 2 | |
{M(b)} | Behavior dependent | -447.08 | 5.4673 | 0.065 | 3 | |
Identification errors | {M(t)} | Time dependent | -446.62 | 0.000 | 1.000 | 5 |
{M(.)} | Null Model | -444.81 | 1.810 | 0.4045 | 2 | |
{M(b)} | Behavior dependent | -443.06 | 3.560 | 0.107 | 3 |
Abundance estimation of Amolops formosus with identification error and without identification error.
Group | Abundance | Standard Error | Lower Confidence Limit | Upper Confidence Limit |
---|---|---|---|---|
Amolops abundance without error | 179.64 | 6.01 | 170.92 | 195.32 |
Amolops abundance with Misidentification error | 180.23 | 6.56 | 170.62 | 197.17 |
This study provides a purely non-invasive and reliable method for individual identification of amphibians with natural marking patterns. The successful recapture of more than 50% of the individuals within four sampling occasions tends to confirm the validity of this method. The use of zoom lens reduced the flight response of the frogs to the minimum. Only frogs that were encountered too close (< 1 m) had shown flight response.
There is no better approach than noninvasive sampling for population estimation of frogs when physically capturing each frog is not possible. It is feasible to photo-document and to identify individual frogs from within 1 m and thus eliminate the need for capturing and handling (
Pattern recognition gets influenced by animal posture, hormonal status, injury marks, environmental influences, and also dirt (
It is a fundamental requirement to correctly identify individuals in a mark-recapture population estimate study because misidentification can affect the abundance estimates (
Pattern recognition is utilized extensively for individual identification, and performs better than other traditional methods. However, the accuracy of software varies depending on the species and their patterns and image quality. Hence, a thorough evaluation of software is recommended. This contribution demonstrates the efficiency of HotSpotter software in estimating the abundance of stream frogs in a non-invasive manner. This technique is quick, easy, cheap and can be utilized in citizen science approach in monitoring amphibian populations. This method can be further improved by collecting parameters such as precise GPS location, time and macrohabitat with each photograph which will help in understanding aspects of species ecology such as home range, site fidelity, activity pattern and macrohabitat use.
We thank the Uttarakhand Forest Department for providing us with study permission (UK/FD/702/5-6). We thank the Department of Science and Technology, Govt. of India for funding the study under DST-NMSHE Project. We thank V.B. Mathur, G. S Rawat and S. Satyakumar of the Wildlife Institute of India for facilitating our research. We thank S.K. Dutta (Odisha), and Indraneil Das (Malaysia) for literature support. We also thank G. Harshavardhan for assisting in the field work. We are grateful to Neeraj Rawat, Anukul Nath, Rupa Bhardwaj, Surya Sharma, Urjit Bhatt and Naresh Rawat for assisting in both on-field and off-field work.