DOI: 10.5061/dryad.c2fqz61p0
title: Data from: Individual identification and confirmation of nest site fidelity in Painted Stork (<em>Mycteria leucocephala</em>) using Deep Transfer Learning

description: Accurate individual identification is vital in field studies. Since
 traditional marking techniques, though effective, can be intrusive and
 potentially disrupt natural behaviours, identification using natural
 markings has gained popularity across various taxa as a non-invasive
 alternative. Here, we report on a Painted Stork (Mycteria leucocephala)
 with a distinctive neck injury mark, observed at the National Zoological
 Park (Delhi Zoo) over three consecutive breeding seasons (2022–2024). To
 verify its identity and assess nest-site fidelity, we employed a
 non-invasive approach combining morphometric measurements and Deep
 Transfer Learning-based image analysis. High-resolution photographs were
 used to extract linear measurements and assess repeatability, while a Deep
 Transfer Learning classifier further validated the individual’s identity
 with 98% accuracy. Image-based morphometric measurements were particularly
 reliable for longer morphological features, confirming that the
 scar-marked stork observed over three consecutive years is indeed the same
 individual. The repeated sightings of the scar-marked stork on the same
 patch support evidence of nest-site fidelity. Our findings highlight the
 potential of Deep Transfer Learning and pattern-based recognition as
 powerful, non-intrusive tools for long-term monitoring of colonial
 waterbirds.
description: 1.     Morphological measurements of Ringo We
 used 82 photographs of Ringo from a collection of 100 that had proper
 orientation. The mean of the morphological variables of Ringo taken during
 2024 was compared with randomly selected images of 2022 and 2023. With tps
 DIG we carefully measured morphological features in pixel units, namely
 (1) Upper mandible, (2) Lower mandible, (3) the distance between the mouth
 corner to the inside corner of the eye, (4) the distance between the
 inside corner of the eye to the top end of the right nostril, (5) The
 distance from the base of the pectoral wing to the tip of the wing
 covering black and white, (6) distance from the feather region to the
 tarsus and tibia junction. All the above morphological measurements are
 available in the Excel format. 2.     Deep Transfer
 Learning Using the pre-trained model (ResNet-50), we
 fine-tuned our model with Ringo datasets collected during 2022, 2023, and
 2024 to train, validate, and test the accuracy. Our input data size is 224
 by 224 pixels, and output with multiclass layers (n=3). Three classes,
 namely 1) the right side of the Ringo (n = 824 images), 2) the left side
 of the Ringo (n = 678 images), and 3) non-marked or unknown Painted Storks
 (n = 1755 images) were created. Using a Transfer Learning approach, we
 developed a model trained with the digital images of Ringo and other
 Painted Storks. We have provided the Deep Transfer
 Learning model and its MATLAB code along with few sample images to cross
 check and validate the model. To run this model, one requires a MATLAB
 environment. We also provided the layer details of the
 model.  
description: # Individual identification and confirmation of nest site fidelity in
 Painted Stork (Mycteria leucocephala) using Deep Transfer Learning Dataset
 DOI: [10.5061/dryad.c2fqz61p0](10.5061/dryad.c2fqz61p0) ## Description of
 the data and file structure #### File: Details_of_the_PsScarNet_Layers.txt
 **Description:** The layer details of the Deep Transfer Learning model
 which we have developed is available in text file.  ##### Variables * It
 has 177 layers. #### File: PsScarNet_Code.m **Description:** The MATLAB
 code is provided to run the model. % This **PsScarNet_Code.m** MATLAB code
 classifies a new image using a pre-trained neural network model
 (**PsScarNet.mat**). % Ensure you have the following prerequisites before
 running the code: **% Prerequisites:** % 1. MATLAB installed with the Deep
 Learning Toolbox. % 2. The pre-trained neural network model
 (**PsScarNet.mat**) should be in MATLAB workspace. % 3. Extract the images
 from the **Sample_images_PS.zip** and the image you want to classify
 should be in the working directory. % Instructions to run the code: % 1.
 Update the 'newImagePath' variable with the path to your image
 file. % Example: newImagePath = 'path_to_your_image.jpg'; % 2.
 Run the **PsScarNet_Code.m** code in MATLAB. The code will display the
 image and the predicted label. #### File: Sample_images_PS.zip
 **Description:** The sample photographs (representing the right, left side
 of Ringo and the unknown Painted Stork) can be used to test the model. 
 #### File: PsScarNet.mat **Description:** This is the deep transfer
 learning model that we have developed to identify the Ringo #### File:
 Morphometrics_of_Ringo.xlsx **Description:** The morphological
 measurements of Ringo are available in the excel format, measured in
 pixels ##### Variables - Description * Name - Image number * Upper BillM1
 - Upper Bill (mandible) measurement * Lower BillM2 - Lower Bill (mandible)
 measurement * Mouth to EyeM3 - Mouth to Eye measurement   * Eye to
 nostrilM4 - Eye to nostril measurement * wing length M5 - wing
 length measurement * Ref mean tibia thicknessM6 - Tibia
 thickness measurement * Angle MEN - Angle of Mouth, Ear and Nostril *
 AngleMTN - Angle of Mouth, Bill_Tip and Nostril ## Code/software
 **1:** The layer details of the Deep Transfer Learning model which we have
 developed are available in a text file, and it has 177 layers
 (Details_of_the_PsScarNet_Layers.txt). **2:** The MATLAB code is provided
 to run the model (PsScarNet_Code.m). **3:** The sample photographs
 (representing the right, left side of Ringo and the unknown Painted Stork)
 can be used to test the model (Sample_images_PS.zip).  **4:** This is the
 deep transfer learning model that we have developed to identify the Ringo.
 To run this model MATLAB environment is mandatory (PsScarNet.mat). ####
 File: Details - SIFT Feature extraction **Description:** The sample jpg
 images (Ringo and non-Ringo) and the required MATLAB code
 (detect_sift_batch_interactive_final.m) to extract the SIFT features is
 given in the zip file (Ringo-SIFT.zip).  ## Access information Other
 publicly accessible locations of the data: * No Data was derived from the
 following sources: * No