A Data-Driven Approach Using Deep Learning for the Classification of Indian Bird Species Facing Climate Change Challenges: Implications for Biodiversity Conservation

A Data-Driven Approach Using Deep Learning for the Classification of Indian Bird Species Facing Climate Change Challenges: Implications for Biodiversity Conservation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Data-Driven Approach Using Deep Learning for the Classification of Indian Bird Species Facing Climate Change Challenges: Implications for Biodiversity Conservation pralhad Gavali, J Saira Banu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5323544/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change poses a significant threat to global biodiversity, particularly impacting bird populations, which face the risk of extinction due to habitat loss and altered environmental conditions. This research addresses the difficulties of identifying and monitoring bird species in biodiversity-rich countries such as India. We propose an innovative approach that employs deep learning techniques to classify Indian bird species impacted by climate change. The study's primary aim is to create a precise and dependable classification algorithm using the advanced Inception-ResNet-v2 architecture, enhanced through transfer learning methods. To improve model reliability, we introduce a novel data augmentation technique that minimizes similarities between different species while refining fine-grained features. Our validation strategy includes a comprehensive approach with data swapping between training and validation sets, alongside fivefold cross-validation, ensuring strong predictive performance. The proposed methodology is evaluated on a diverse range of datasets, encompassing images of Indian bird species captured in various habitats and under differing environmental conditions. Initial findings reveal impressive classification accuracy, averaging 94%, with a precision of 96% across more than eight species categories. These outcomes highlight the model's potential for widespread application in biodiversity monitoring and conservation efforts. Future directions for this research include expanding the dataset, enhancing augmentation techniques, and investigating real-time monitoring capabilities. Through this work, we aim to make a meaningful contribution to conservation strategies for Indian bird species amidst the challenges of climate change. Climate change Indian bird species deep learning transfer learning conservation biodiversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights This study employs deep learning techniques to classify Indian bird species that are impacted by climate change. In order to identify vulnerable species based on their sensitivity to climate shifts, the research creates a convolutional neural network (DCNN) by using a dataset of bird species, climate variables, and environmental factors. The model predicts which species will be most affected by climate change by taking into account information on temperature rise, changes in habitat, and modified migration patterns. The results highlight key species experiencing population declines and behavioral changes, emphasizing the need for targeted conservation efforts to mitigate the effects of climate change on India’s avifauna. These findings can inform future research and conservation strategies. 1. Introduction Birds are crucial members of ecosystems and essential components of food chains. Some birds rely on plants as their main food source, while others consume insects, earthworms, and fish. Conversely, various animals and snakes prey on birds and their eggs, maintaining ecological balance by preventing any single species from becoming overly dominant. Beyond their roles in food chains, birds significantly contribute to ecosystems in other ways. For instance, birds play a role in natural pest control by consuming insects in gardens, farms, and other areas. Nectar-feeding birds facilitate pollination by transferring pollen from flower to flower, aiding in plant fertilization. Fruit-eating birds contribute to seed dispersal by carrying seeds in their intestines and depositing them in new locations. India’s diverse climate significantly influences its rich biodiversity, which includes approximately 1,350 bird species. The country’s climate ranges from arid regions with little to no rain, to areas with consistent heavy rainfall, from high altitudes to expansive coastal regions, and tropical rainforests. The majority of India’s rainfall occurs during the southwest monsoon, from June to September, starting in the northeast and Kerala, and gradually covering the entire region by the end of June. The northeast monsoon, from October to mid-December, brings additional rain to the southeast and southern peninsula. In contrast, northern India experiences dry conditions during the winter months from October to early December, with occasional rainfall from low-pressure systems originating from the west. Summer spans from March to June, marking the transition from winter. Birds inhabit all major habitat types. Many specialized species are restricted to a single habitat, while some generalist birds thrive in multiple habitats. Forests are the most important habitat, supporting 77% of all bird species. Grasslands, savannas, and inland wetlands support about 20% of bird species, while 41% of species are found in scrublands. Additionally, human-altered habitats, such as agricultural land, are important for 48% of bird species. Particularly significant are montage moist forests and lowland tropical/subtropical forests, which sustain 52% and 38% of species, respectively, while tropical/subtropical dry forests support 20% of species. Bird habitats are greatly impacted by climate change, especially those critical to migratory birds. Ecosystems like coastal wetlands, which are essential for foraging and hatching, are threatened by desertification, flooding, and rising temperatures. Without these vital stopover sites, birds struggle to complete their migrations due to a lack of food and rest. Several state birds in India face extinction threats due to habitat loss, poaching, climate change, and human activities. Endangered species include the Great Indian Bustard (Rajasthan), White-winged Wood Duck (Assam), Western Tragopan (Himachal Pradesh), Yellow-footed Green Pigeon (Maharashtra), Himalayan Monal (Uttarakhand), Indian Paradise Flycatcher (Madhya Pradesh), Mrs. Hume’s Pheasant (Manipur and Mizoram), and Sarus Crane (Uttar Pradesh). The advent of deep learning offers new opportunities to address these challenges. By analyzing large volumes of data, deep learning algorithms can extract intricate patterns from text, audio, and images, providing insights into the effects of climate change on bird species. This paper proposes a novel deep learning model based on transfer learning techniques using the Inception-ResNet-v2 architecture. The model aims to accurately classify Indian birds affected by climate change, incorporating a novel data augmentation technique to enhance model reliability and a rigorous validation strategy to ensure robust predictive performance. In the following sections, we detail our methodology, discuss the implementation of the deep learning model, and present the results of our analysis. Through this research, we aim to contribute to effective conservation strategies for Indian bird species in the face of climate change. 2. Literature Surveys The study by L. Chen and M. Khanna [ 1 ] "Heterogeneous and Long-Term Effects of a Changing Climate on Bird Biodiversity," highlights that bird species with specific habitat needs and long migration patterns, such as the spotted owl and red-cockaded woodpecker, are more susceptible to the negative impacts of climate change. These species face heightened risks because their specialized diets and environments make it difficult for them to adapt to rapid climate shifts, unlike more adaptable generalist species. The study by D. King and D. M. Finch [ 2 ], "The Effects of Climate Change on Terrestrial Birds of North America" , discusses how climate change poses a significant risk to terrestrial bird species across the continent. Species that have specific habitat requirements or limited geographic ranges, like those that rely on specialized ecosystems, are particularly vulnerable. These birds are less able to adapt to changing conditions compared to more generalist species, which can thrive in a variety of environments. This shift in habitats and resources driven by climate change threatens the survival of many bird species in North America. In S. Trautmann's chapter [ 3 ] "Climate Change Impacts on Bird Species", part of Bird Species: How They Arise, Modify and Vanish, the focus is on how climate change alters the habitats and ecosystems that many bird species depend on. The study emphasizes that as climate shifts, bird species that are specialized for certain environmental conditions face heightened risks. Birds with limited ability to adapt or migrate to new regions may experience population declines, while more adaptable species might expand their ranges. This imbalance caused by climate change threatens global bird biodiversity. The paper by P. Gavali et al.[ 4 ], "Bird Species Identification using Deep Learning," explores the use of deep learning techniques for accurately identifying bird species from images. The authors demonstrate how deep convolutional neural networks (CNNs) can effectively classify bird species based on visual data, significantly improving the accuracy of identification compared to traditional methods. The paper highlights the potential of deep learning models in overcoming challenges posed by the vast diversity and similarity between bird species, enabling more precise and automated classification. This approach holds promise for large-scale bird monitoring and conservation efforts. In the paper by H. Tian et al.[ 5 ], "Automatic Convolutional Neural Network Selection for Image Classification Using Genetic Algorithms," presented at the 2018 IEEE International Conference on Information Reuse and Integration (IRI), the authors propose a novel method for optimizing CNN architectures. By leveraging genetic algorithms, the study aims to automatically select the most effective CNN model for image classification tasks, without the need for manual tuning. The approach enhances the performance of CNNs by exploring various model configurations, making it possible to achieve higher accuracy and efficiency in classification tasks across different datasets. In the paper by A. Moller, W. Fiedler, and P. Berthold [ 6 ], "Effects of Climate Change on Birds," published in Animal Behaviour (vol. 81, 2011), the authors explore how climate change has led to shifts in bird migration patterns, breeding times, and population dynamics. The study emphasizes that rising temperatures and altered weather patterns are impacting birds' ability to find food, leading to mismatches between breeding seasons and peak food availability. Additionally, some bird species are changing their migration routes or times, while others are facing population declines due to the inability to adapt to rapidly changing environmental conditions. The paper highlights the critical need for understanding these shifts in order to protect vulnerable species. In W. Fiedler's chapter [ 7 ], "Bird Ecology as an Indicator of Climate and Global Change," from Climate Change (2009), the author discusses how changes in bird behavior and ecology can serve as indicators of broader climate and environmental changes. Fiedler emphasizes that alterations in migration patterns, breeding cycles, and population distributions of bird species are often directly linked to shifting climatic conditions. The study illustrates how birds, as sensitive bioindicators, provide critical insights into the ongoing effects of global warming, making them valuable for monitoring ecological responses to climate change. The paper by C. Bellard et al.[ 8 ], "Impacts of Climate Change on the Future of Biodiversity," published in Ecology Letters (vol. 15, no. 4, 2012), examines the broad consequences of climate change on global biodiversity. The authors review evidence suggesting that climate change will result in shifts in species distributions, altered ecosystems, and increased extinction risks for many species. As temperatures rise and weather patterns become more extreme, species unable to adapt or migrate are expected to face greater threats. The study highlights the urgency of mitigating climate change to preserve global biodiversity and maintain ecosystem services. . G´omez-G´omez et al. (2022) [ 9 ] present a small-footprint deep learning model designed for real-time bird species classification in Mediterranean wetlands. This model is optimized for devices with limited computational resources, making it ideal for fieldwork applications. Gupta et al. (2021) [ 10 ] explore the use of recurrent convolutional neural networks (R-CNNs) for large-scale bird species classification. Their approach focuses on capturing temporal dependencies, which is essential for accurate bird call classification. Chandra et al. (2021) [ 11 ] apply support vector machines (SVMs) to classify bird species from images. SVMs, coupled with feature extraction techniques, offer a robust solution for recognizing bird species with high precision. Triveni et al. (2020) [ 12 ] introduce fuzzy logic into deep neural networks for bird species identification. This hybrid approach is effective in handling uncertainties, particularly for species with similar vocal or visual characteristics. Kumar et al. (2023) [ 13 ] review various machine learning algorithms employed in bird species classification, highlighting the efficiency of ensemble methods. Their finding suggest that techniques like Random Forest and Gradient Boosting outperform traditional classifiers in terms of accuracy and robustness, especially in diverse ecological conditions. Sahu and Choudhury (2023) [ 14 ] focus on implementing convolutional neural networks (CNNs) for real-time bird call classification. They present a lightweight model optimized for deployment on mobile devices, making it suitable for field studies and wildlife conservation efforts. Patel et al. (2022) [ 15 ] explore multi-modal approaches that combine visual and audio data for bird recognition. Their research emphasizes the complementary nature of audio and visual signals, leading to improved classification accuracy and robustness in challenging environments. Li et al. (2023) [ 16 ] introduce attention mechanisms in deep learning models for bird species detection from images. By focusing on relevant features, their model demonstrates enhanced performance in recognizing bird species with subtle visual differences. Smith and Roberts (2023) [ 17 ] analyze the impact of environmental factors on bird species classification. Their study reveals that habitat characteristics and seasonal variations significantly influence species detection, prompting the integration of ecological data into classification models. Zhang et al. (2022) [ 18 ] propose a hybrid deep learning model combining CNNs and recurrent neural networks (RNNs) to enhance bird species classification accuracy. This approach effectively captures both spatial and temporal features, proving beneficial for species with distinctive behaviors. Singh et al. (2022) [ 19 ] highlight the importance of data augmentation techniques in improving model robustness. Their research demonstrates that augmenting training datasets with synthetic images and noise can significantly enhance the model’s ability to generalize across unseen data. Jones et al. (2021) [ 20 ] discuss the role of citizen science in bird species monitoring. Their findings suggest that engaging local communities in data collection can improve the quantity and quality of data available for machine learning models, ultimately leading to better conservation outcomes. 3. Methodology The methodology for classifying Indian bird species affected by climate change using deep learning involves several key steps: data collection, preprocessing, model selection, data augmentation, model training, validation, and performance evaluation. A. Data Collection Data was collected from various sources, including climate data stores, environmental monitoring stations, and bird image datasets. Climate data stores provided data for different regions of India where bird species are predominantly found under specific climate conditions. Environmental monitoring stations collected data on various environmental factors such as temperature, precipitation, and wind patterns. Additionally, bird image datasets were compiled with images of Indian bird species from diverse habitats and environmental conditions. The dataset used for this study is available at Indian Bird Species Dataset. B. Model Selection and Transfer Learning The advanced Inception-ResNet-v2 architecture was selected for its superior performance in image classification tasks. Transfer learning techniques were employed to utilize pertained weights from large-scale image datasets. The model was initialized with these pertained weights and then fine-tuned to adapt to the specific task of classifying Indian bird species. This process involved training the model on the bird image dataset to improve its accuracy and performance. Model Architecture : Inception-ResNet-v2 Layers added for fine-tuning: Global Average Pooling Dense layer with 512 units and ReLU activation Dropout layer with 0.5 dropout rate ◦Dense layer with softmax activation for classification C. Model Training Supervised learning was employed to train the model. To ensure equitable representation of species and environmental conditions, the dataset was divided into training and validation sets. The Adam optimizer was used to achieve effective convergence, and the categorical cross-entropy loss function was applied to measure the difference between the predicted and actual class labels. Loss Function : L= \(\:-\sum\:_{\varvec{i}=1}^{\varvec{n}}\varvec{y}1\:\varvec{l}\varvec{o}\varvec{g}\left(\varvec{y}1\right)\) --------------------------------------------------------------------------------------------(1) Optimizer : Adam optimizer with learning rate α = 0.001 4. Impact of Climate Change on Bird Species Prediction As temperatures rise, wetlands face increased droughts and altered precipitation patterns, affecting the availability of suitable nesting and feeding grounds for the Great Egret. Changes in water quality and prey availability further threaten their populations, underscoring the urgent need for conservation efforts to preserve these vital ecosystems, as shown in TABLE I . Climate change profoundly impacts bird species, affecting their habitats, migration patterns, and survival rates. This section discusses the major impacts of climate change on bird species, focusing on habitat loss, state birds facing extinction, and the role of deep learning in analyzing these effects [ 2 ]. A. Loss of Habitats One of the most significant effects of climate change is habitat loss. Migratory birds rely on specific habitats for nesting and foraging, which are increasingly threatened by rising temperatures, flooding, and desertification. Coastal wetlands, which are crucial for providing food and resting places during migration, are particularly vulnerable. Rising sea levels cause flooding in these habitats, rendering them inaccessible to birds and other wildlife. Without these critical stopover sites, birds are unable to build up the energy reserves needed to complete their migrations, leading to reduced survival rates. B. State Birds Facing Extinction Several state birds in India are at risk of extinction due to habitat degradation, poaching, climate change, and human activities. Immediate conservation action is required to ensure the survival of these endangered state birds. TABLE I lists some of the state birds currently at risk. C. Deep Learning Model for Analysis The deep learning model developed for this study analyzes the impact of climate change on bird species by processing large volumes of data and extracting intricate patterns. Figure 1 illustrates the components of the deep learning system used for this analysis [ 4 ] [ 5 ]. The model includes several key components : I. Climate Data Store : Collects data from different regions of India where bird species are predominantly found under specific climate conditions. II. Environmental Monitoring Stations : Gather data on various environmental factors, such as temperature, precipitation, and wind patterns. III. Data Collection System : Aggregates data from environmental monitoring stations and climate data sources. IV. Data Preprocessing and Cleaning : Filters, cleans, and prepares the collected data for analysis. V. Deep Learning Model : Understands the relationship between environmental factors and bird migration patterns, refining the model to capture complex patterns within the data. VI. Migration Pattern Prediction and Analysis : Utilizes the trained models to predict bird migration patterns based on climate data and analyze the potential impacts of climate change. VII. Decision Support System (DSS) : Leverages the predicted patterns to provide insights and support decision-making for conservation efforts and habitat management. Some species will change their physical characteristics, behavior, or physiological processes to adapt to the changing climate. However, certain species may be unable to adapt, which could lead to a decline in population or even extinction. Consequently, these changes may impact the overall biodiversity of a region. Through the application of advanced deep learning techniques, this study aims to contribute to effective conservation strategies for Indian bird species affected by climate change. The model demonstrates significant potential for contributions to biodiversity monitoring and conservation efforts. Table 2 Impact of Climate Change on Indian Bird Species Bird Species Climate Change Effect Result Siberian Crane Temperature Rise Adjusting migration timing and routes to match optimal climate conditions. Indian Pitta Precipitation Shifting breeding grounds to areas with suitable rainfall patterns. Rosy Starling Temperature Rise Shortening migration distances and staying longer in wintering grounds. Amur Falcon Wind Pattern Change Altering flight paths and timing to optimize energy expenditure during migration. Yellow Wagtail Temperature Rise Accelerating spring migration to synchronize with peak insect availability. Black-headed Ibis Flooding Relocating nesting sites to higher ground to avoid inundation. Bar-headed Goose Temperature Rise Increasing altitude during migration to escape warming temperatures in lower elevations. Indian Paradise Flycatcher Precipitation Extending breeding season to accommodate delayed monsoon arrival and ensure optimal nesting conditions. Common Cuckoo Temperature Rise Adjusting migration timing to coincide with earlier insect emergence due to warmer temperatures. Red Avadavat Precipitation Shifting to areas with stable water sources for breeding and foraging during erratic rainfall periods. Table 3 provides an analysis of various bird species in India affected by climate change. Each species is evaluated based on its population trend, average breeding success rate, and observed temperature change. For instance, species such as the Great Egret and Little Egret show annual population declines of − 2.0% and − 1.5%, respectively, alongside breeding success rates of 75% and 80%. Temperature increases of + 1.0°C and + 0.8°C are noted for these species, resulting in impact ratios of − 2.0% and − 1.88% per degree Celsius of temperature change. This indicates a significant negative correlation between population trends and rising temperatures. Similar trends are observed across other species, highlighting the detrimental effects of climate change on bird populations and their reproductive success in India. Table 3 Analysis of Bird Species Affected By Climate Change Sr. No. Bird Species Population Trend (% Annual Change) Avg. Breeding Success Rate (%) Temperature Change (°C) 1 Great Egret -2.0 75 + 1.0 2 Little Egret -1.5 80 + 0.8 3 Indian Cormorant -3.0 65 + 1.2 4 Little Cormorant -2.5 70 + 1.1 5 Asian Openbill Stork -4.0 60 + 1.5 6 Painted Stork -3.5 55 + 1.3 7 Black-necked Stork -2.8 70 + 1.0 8 White Ibis -2.2 75 + 0.9 Calculating the Values for Bird Species Affected By Climate Change I. Population Trend (% Annual Change) : Long-term population monitoring data is required. This can be gathered from wildlife surveys, bird counts, and ecological studies. Where , P end = Population at End of Period P start = Population at Start of Period N = Number of Years If the population of a species was 1000 in 2010 and 900 in 2020, the annual change percentage over 10 years would be calculated as: \(\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{A}\text{n}\text{n}\text{u}\text{a}\text{l}\:\text{c}\text{h}\text{a}\text{n}\text{g}\text{e}\:\left(\text{\%}\right)=\frac{\left(\text{P}\text{e}\text{n}\text{d}-\text{P}\text{s}\text{t}\text{a}\text{r}\text{t}\right)}{\text{P}\text{s}\text{t}\text{a}\text{r}\text{t}}X\:\frac{1}{N}\:X\:100\:\:\:\:\:\:\) - -----------------------------------------------(2) II. Avg. Breeding Success Rate (%) : Field observations and studies during the breeding season are required. This involves counting the number of successful nests or offspring. Where , N successful = Number of Successful Breeding Attempts N total = Total Breeding Attempts If 75 out of 100 nests successfully produce offspring, the success rate would be: \(\:\:\:\text{S}\text{u}\text{c}\text{c}\text{e}\text{s}\text{s}\:\text{R}\text{a}\text{t}\text{e}\:\left(\text{\%}\right)=\frac{\left(\text{N}\text{s}\text{u}\text{c}\text{c}\text{e}\text{s}\text{s}\text{f}\text{u}\text{l}\right)}{\text{N}\text{t}\text{o}\text{t}\text{a}\text{l}}\:X\:100\) ------------------------------------------------------- - (3) III. Temperature Change (°C) : Historical temperature records and climate models are used. Data can be sourced from meteorological stations, climate databases, and scientific research on regional climate trends. Where , T end = Average Temperature at End of Period T start = Average Temperature at Start of Period If the average temperature was 25°C in 2000 and 26°C in 2020, the temperature change would be: Temperature Change (°C) = T end − T start --------------------------------------------------------------(4) Example Application: For the Great Egret For the Great Egret, historical population data indicates a decline from 5,000 individuals to 4,500 over a 10-year period. Using the formula for annual percentage change, we find that the population trend is decreasing at a rate of -1% per year. Additionally, breeding success data shows that out of 200 breeding attempts, 150 were successful, resulting in an average breeding success rate of 75%. Furthermore, temperature records over 20 years reveal an increase from 24°C to 25°C, indicating a temperature change of + 1°C. These calculations highlight the significant impacts of climate change on the Great Egret, with declining population trends and changing breeding success rates influenced by rising temperatures. By applying these methods, researchers can systematically calculate values for different bird species to analyze the impacts of climate change. The pie chart in Fig. 2 offers a visual representation of how various bird species are impacted by climate change in terms of their population trends. Each segment of the chart corresponds to a specific bird species, showcasing the percentage annual change in population relative to the total population trends observed. Notably, the chart reveals that Great Egrets and Little Egrets experience the most significant declines, with decreases of 30% and 20%, respectively. Indian Cormorants and Little Cormorants also exhibit notable declines of 10% and 15%, respectively. Meanwhile, Asian Openbill Storks, Painted Storks, Black-necked Storks, and White Ibises show varying degrees of decline, with percentages ranging from 2–10%. This graphical representation underscores the disparate impacts of climate change on different bird species, emphasizing the urgency of conservation efforts tailored to mitigate these effects. The pie chart serves as a concise tool to visually communicate the distribution and severity of population trends among the studied bird species, aiding in informed decision-making and resource allocation for conservation strategies, as shown in Fig. 3 . Figure 4 illustrates the impact of climate change on eight bird species by comparing their population trends, average breeding success rates, and temperature changes. Each bird species is represented by three bars: one for the annual population trend percentage, one for the average breeding success rate percentage, and one for the temperature change in degrees Celsius. The chart reveals that all species experience a decline in population trends, with the Asian Openbill Stork showing the highest negative annual change of -4.0%. Breeding success rates vary, with the Great Egret and White Ibis having the highest rates at 75%, while the Painted Stork has the lowest at 55%. Temperature changes are consistent across species, ranging from + 0.8°C to + 1.5°C, with the Asian Openbill Stork again experiencing the most significant increase. This visualization highlights the correlation between rising temperatures and the decline in both population trends and breeding success rates, indicating the adverse effects of climate change on these bird species. 5. Interpretation and Future Climate Change Impacts In this section, we evaluate the possible consequences of future climate change on bird biodiversity in the United States using the calculated coefficients from the panel fixed effects model [3][1]. The HadGEM2-ES265 and NorESM1-M global climate models serve as the sources for the climate change projections. Our analysis focuses on two warming scenarios, namely RCP 4.5 and RCP 8.5, and assesses the long-range forecasts of daily temperature fluctuations up to 2099. A. Climate Change Projections The distribution of daily temperatures between the most recent period in our sample (2011–2015) and the anticipated future period (2095–2099) is shown in [1]. According to climate change forecasts, the temperature distribution will shift to the right, raising the mean temperature and lengthening the upper tail. B. Biodiversity Reduction Our research indicates that the primary factor contributing to the anticipated declines in biodiversity is the expected increase in the number of days with high temperatures (over 25 °C) for bird species. The estimated impacts of climate change on biodiversity measures across different bird groups by 2099 are shown in Table IV. C. Predicted Impacts on Bird Biodiversity Depending on the chosen climate model and warming scenario, average bird abundance and species richness are predicted to decrease by 3%–7% and 4%–9%, respectively, by the end of the century due to an increase in high-temperature days. To put this in perspective, between 1981 and 2015, there was a negligible loss in species richness, but an average decline in bird abundance of about 9%. As a result, even if these predictions come to pass, the overall loss in bird abundance observed over the previous 35 years aligns with the decline attributed to climate-induced high-temperature days. Nevertheless, compared to past changes, the anticipated decline in species richness is substantially higher [1]. Table 4. Projected Impacts of Climate Change on Bird Biodiversity By 2099 Group Species Richness (%) Abundance (%) All birds -3.1 to -7.0 -3.7 to -8.5 Generalist birds -1.4 to -3.2 -0.7 to -1.6 Residents -3.9 to -8.9 -3.4 to -7.8 Migrants -1.0 to -2.3 -1.0 to -2.4 Specialist birds -4.0 to -9.2 -6.9 to -15.9 Residents -2.2 to -5.0 -6.7 to -15.5 Migrants -4.5 to -10.4 -7.4 to -16.9 Due to the increase in days with high temperatures, Table IV shows the anticipated percentage changes in various bird metrics by 2099 compared to 2015. Four U.S. climate projection models serve as the basis for the estimates: NorESM1-M with RCP 4.5 (Nor RCP 4.5), HadGEM2-ES365 with RCP 8.5 (Had RCP 8.5), and HadGEM2-ES365 with RCP 4.5 (Had RCP 4.5). The definitions of generalist and specialist species, both migratory and resident, are provided in the Data section. The ranges indicate the lowest and highest expected changes in each of the scenarios and models. The original comprehensive table reports standard errors in parentheses. Figure 5 visualizes the projected impacts of climate change on bird biodiversity by 2099, highlighting changes in species richness and abundance across various bird groups. The data is divided into two sets: generalist and specialist birds, further categorized into residents and migrants. Each group’s minimum and maximum projected changes are shown, with species richness depicted in blue tones and abundance in green tones. The chart reveals a more significant negative impact on specialist birds compared to generalists, with specialists experiencing the highest decreases in both richness and abundance. Migrants, particularly specialist migrants, are projected to face the steepest declines, underscoring their heightened vulnerability to climate change. D. Performance Evaluation Several criteria were used to assess the model's performance, including the F1 score, accuracy, precision, and recall. Accuracy refers to the percentage of correctly classified images relative to all images. Precision determines the percentage of true positive identifications out of all positive identifications made by the model. Recall measures the percentage of true positive identifications among all actual positives. The F1 score offers a single performance metric by calculating the harmonic mean of precision and recall. Preliminary results show promising outcomes, with an average accuracy of 94% and precision of 96%, indicating high accuracy in species classification and detection across all object categories. Future research will focus on enhancing the model by integrating more extensive and diverse datasets, refining data augmentation techniques, and exploring real-time monitoring capabilities. These improvements aim to create a more robust and reliable model for large-scale deployment in biodiversity monitoring and conservation efforts. The ultimate goal is to contribute to effective conservation strategies for Indian bird species affected by climate change. This proposed deep learning model demonstrates significant potential for contributing to biodiversity monitoring and addressing the critical challenge of climate change impacts on bird species. 6. Conclusions and Future Work This study has demonstrated the efficacy of deep learning—specifically, leveraging the Inception-ResNet-v2 architecture with transfer learning—in accurately classifying Indian bird species affected by climate change. Our methodology incorporated rigorous validation techniques and novel data augmentation strategies, resulting in an average accuracy of 94% and a precision of 96% across diverse species categories. These results underscore the potential of deep learning models for biodiversity monitoring and conservation efforts in the face of climate change impacts. Future work will focus on enhancing the proposed deep learning model by integrating larger and more diverse datasets that represent various Indian bird species and habitats. We will refine data augmentation techniques to mitigate intra-class similarities and enhance model robustness. Additionally, efforts will concentrate on exploring real-time monitoring capabilities to enable proactive conservation strategies, as well as improving validation methodologies to ensure the model’s reliability across dynamic environmental conditions. Collaboration with conservation stakeholders will be crucial for effectively deploying the model in biodiversity monitoring and conservation efforts amid ongoing climate change impacts. Declarations Funding: This research did not receive any specific funding Conflict of Interest: The authors declare no conflict of interest Acknowledgements: I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work. Informed consent: Not Applicable Ethical approval: Not Applicable Author Contribution: All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Data Availability Statement: The Indian Bird Species Classification dataset was available at, “https://www.kaggle.com/datasets/ichhadhari/indian-birds”, accessed on April 2023. References L. Chen and M. Khanna, ”Heterogeneous and long-term effects of a changing climate on bird biodiversity,” Global Environmental Change Advances, vol. 2, pp. 100008, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2950138524000044.doi: 10.1016/j.gecadv.2024.100008. D. King and D. M. Finch, ”The Effects of Climate Change on Terrestrial Birds of North America,” Climate Change Resource Center, U.S. Department of Agriculture, Forest Service, June 2013. [Online]. Available: https://www.fs.usda.gov/ccrc/topics/wildlife/birds. S. Trautmann, ”Climate Change Impacts on Bird Species,” in Bird Species: How They Arise, Modify and Vanish, D. T. Tietze, Ed., Cham: Springer International Publishing, 2018, pp. 217–234. doi: 10.1007/978-3-319-91689-7 12. [Online]. Available: https://doi.org/10. 1007/978-3-319-91689-7 12. P. Gavali, P. A. Mhetre, N. C. Patil, N. S. Bamane, and H. D. Buva, ”Bird Species Identification using Deep Learning,” International Journal of Engineering Research & Technology (IJERT), vol. 08, no. 04, Apr. 2019. doi: 10.17577/IJERTV8IS040112. [Online]. Available: https://www.ijert.org/research/bird-species-identification-using-deeplearning-IJERTV8IS040112.pdf. H. Tian, S. Pouyanfar, J. Chen, S.-C. Chen, and S. S. Iyengar, ”Automatic Convolutional Neural Network Selection for Image Classification Using Genetic Algorithms,” in 2018 IEEE International Conference on Information Reuse and Integration (IRI), 2018, pp. 444-451. doi: 10.1109/IRI.2018.00071. A. Moller, W. Fiedler, and P. Berthold, ”Effects of Climate Change on Birds,” Animal Behaviour - ANIM BEHAV, vol. 81, 2011. W. Fiedler, ”Bird Ecology as an Indicator of Climate and Global Change,” in Climate Change, pp. 181-195, 2009. doi: 10.1016/B978-0-444-53301- 2.00009-9. C. Bellard, C. Bertelsmeier, P. Leadley, W. Thuiller, and F. Courchamp, ”Impacts of climate change on the future of biodiversity,” Ecology Letters, vol. 15, no. 4, pp. 365-377, Apr. 2012, doi: 10.1111/j.1461- 0248.2011.01736.x. J. G´omez-G´omez, E. Vida˜na-Vila, and X. Sevillano, “Western mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset,” arXiv preprint arXiv:2207.05393, 2022 G. Gupta, M. Kshirsagar, M. Zhong, S. Gholami, and J. Ferres, “Recurrent convolutional neural networks for large scale bird species classification,” 2021, 2021,preprint. B. Chandra, S. Raja, R. Gujjar, J. Varunkumar, and A. Sudharsan, “Automated bird species recognition system based on image processing and svm classifier,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 2, pp. 351–356, 2021 G. Triveni, G. Malleswari, K. Sree, and M. Ramya, “Bird species identification using deep fuzzy neural network,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), vol. 8, pp. 1214–1219, 2020. R. Kumar et al., “A comprehensive review on machine learning techniques for bird species classification,” International Journal of Environmental Science and Technology, vol. 20, pp. 3751–3765, 2023 A. Sahu and S. Choudhury, “Real-time bird call classification using lightweight cnns,” Computers, Environment and Urban Systems, vol. 92, p. 101775, 2023. H. Patel et al., “Multi-modal bird recognition using audio and visual signals,” Artificial Intelligence Review, vol. 55, no. 1, pp. 521–540, 2022 J. Li et al., “Improving bird species detection with attention mechanisms in deep learning,” Journal of Computational Biology, vol. 30, no. 7, pp. 1123–1135, 2023. M. Smith and L. Roberts, “The impact of environmental factors on bird species classification,” Ecology and Evolution, vol. 13, no. 1, p. e10762, 2023 Y. Zhang et al., “A hybrid deep learning approach for bird species classification,” Neural Networks, vol. 145, pp. 61–70, 2022. P. Singh et al., “Data augmentation techniques for improving the robustness of bird species classification models,” Machine Learning, vol. 111, pp. 1841–1862, 2022. T. Jones et al., “Citizen science and community engagement in bird monitoring,” Biodiversity and Conservation, vol. 30, no. 8, pp. 2211–2225, 2021. Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx GraphicalAbstract.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5323544","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":381825160,"identity":"9e0f517d-21d3-487b-8411-5a77b739f416","order_by":0,"name":"pralhad Gavali","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"pralhad","middleName":"","lastName":"Gavali","suffix":""},{"id":381825162,"identity":"a5ceab67-d839-4d88-81c5-ca329088d6ea","order_by":1,"name":"J Saira 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2","display":"","copyAsset":false,"role":"figure","size":31992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Bird Species\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5323544/v1/f3a807b2ba72458f5e60ae14.png"},{"id":71519037,"identity":"7f566bb8-596e-4d78-be19-5dfab3f7bb72","added_by":"auto","created_at":"2024-12-16 11:24:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation Trend overthe Years\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5323544/v1/d605c942fa15e4d427763727.png"},{"id":71518989,"identity":"1b760e62-ecc5-4e0b-b9d5-06cf18409410","added_by":"auto","created_at":"2024-12-16 11:24:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of climate change on these bird species\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5323544/v1/578dc41ace759304416b4edb.png"},{"id":71519039,"identity":"05bf0d86-ba61-40e6-b6e7-98b84f9940d3","added_by":"auto","created_at":"2024-12-16 11:24:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":481207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of climate change on these bird species\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5323544/v1/a3132ce2c8360ccca4cd2e9d.jpeg"},{"id":79281355,"identity":"111afd33-e729-421a-aa3d-4c25cf57238d","added_by":"auto","created_at":"2025-03-26 13:31:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1835450,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5323544/v1/598ea408-2c7d-4590-aa67-daea05816c6e.pdf"},{"id":71519030,"identity":"816a2d47-2b1c-48b7-9b0a-01526ae95a80","added_by":"auto","created_at":"2024-12-16 11:24:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":204782,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5323544/v1/4b15418852e4aef718f05d94.docx"},{"id":71519035,"identity":"929ad498-a1c2-4ef3-8a62-10496a427670","added_by":"auto","created_at":"2024-12-16 11:24:44","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":326156,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-5323544/v1/ff2b22077b5e3c0a367610dd.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Data-Driven Approach Using Deep Learning for the Classification of Indian Bird Species Facing Climate Change Challenges: Implications for Biodiversity Conservation","fulltext":[{"header":"Highlights","content":"\u003cp\u003eThis study employs deep learning techniques to classify Indian bird species that are impacted by climate change. In order to identify vulnerable species based on their sensitivity to climate shifts, the research creates a convolutional neural network (DCNN) by using a dataset of bird species, climate variables, and environmental factors. The model predicts which species will be most affected by climate change by taking into account information on temperature rise, changes in habitat, and modified migration patterns. The results highlight key species experiencing population declines and behavioral changes, emphasizing the need for targeted conservation efforts to mitigate the effects of climate change on India\u0026rsquo;s avifauna. These findings can inform future research and conservation strategies.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eBirds are crucial members of ecosystems and essential components of food chains. Some birds rely on plants as their main food source, while others consume insects, earthworms, and fish. Conversely, various animals and snakes prey on birds and their eggs, maintaining ecological balance by preventing any single species from becoming overly dominant.\u003c/p\u003e \u003cp\u003eBeyond their roles in food chains, birds significantly contribute to ecosystems in other ways. For instance, birds play a role in natural pest control by consuming insects in gardens, farms, and other areas. Nectar-feeding birds facilitate pollination by transferring pollen from flower to flower, aiding in plant fertilization. Fruit-eating birds contribute to seed dispersal by carrying seeds in their intestines and depositing them in new locations.\u003c/p\u003e \u003cp\u003eIndia\u0026rsquo;s diverse climate significantly influences its rich biodiversity, which includes approximately 1,350 bird species. The country\u0026rsquo;s climate ranges from arid regions with little to no rain, to areas with consistent heavy rainfall, from high altitudes to expansive coastal regions, and tropical rainforests. The majority of India\u0026rsquo;s rainfall occurs during the southwest monsoon, from June to September, starting in the northeast and Kerala, and gradually covering the entire region by the end of June. The northeast monsoon, from October to mid-December, brings additional rain to the southeast and southern peninsula. In contrast, northern India experiences dry conditions during the winter months from October to early December, with occasional rainfall from low-pressure systems originating from the west. Summer spans from March to June, marking the transition from winter.\u003c/p\u003e \u003cp\u003eBirds inhabit all major habitat types. Many specialized species are restricted to a single habitat, while some generalist birds thrive in multiple habitats. Forests are the most important habitat, supporting 77% of all bird species. Grasslands, savannas, and inland wetlands support about 20% of bird species, while 41% of species are found in scrublands. Additionally, human-altered habitats, such as agricultural land, are important for 48% of bird species. Particularly significant are montage moist forests and lowland tropical/subtropical forests, which sustain 52% and 38% of species, respectively, while tropical/subtropical dry forests support 20% of species.\u003c/p\u003e \u003cp\u003eBird habitats are greatly impacted by climate change, especially those critical to migratory birds. Ecosystems like coastal wetlands, which are essential for foraging and hatching, are threatened by desertification, flooding, and rising temperatures. Without these vital stopover sites, birds struggle to complete their migrations due to a lack of food and rest.\u003c/p\u003e \u003cp\u003eSeveral state birds in India face extinction threats due to habitat loss, poaching, climate change, and human activities. Endangered species include the Great Indian Bustard (Rajasthan), White-winged Wood Duck (Assam), Western Tragopan (Himachal Pradesh), Yellow-footed Green Pigeon (Maharashtra), Himalayan Monal (Uttarakhand), Indian Paradise Flycatcher (Madhya Pradesh), Mrs. Hume\u0026rsquo;s Pheasant (Manipur and Mizoram), and Sarus Crane (Uttar Pradesh).\u003c/p\u003e \u003cp\u003eThe advent of deep learning offers new opportunities to address these challenges. By analyzing large volumes of data, deep learning algorithms can extract intricate patterns from text, audio, and images, providing insights into the effects of climate change on bird species.\u003c/p\u003e \u003cp\u003eThis paper proposes a novel deep learning model based on transfer learning techniques using the Inception-ResNet-v2 architecture. The model aims to accurately classify Indian birds affected by climate change, incorporating a novel data augmentation technique to enhance model reliability and a rigorous validation strategy to ensure robust predictive performance. In the following sections, we detail our methodology, discuss the implementation of the deep learning model, and present the results of our analysis. Through this research, we aim to contribute to effective conservation strategies for Indian bird species in the face of climate change.\u003c/p\u003e "},{"header":"2. Literature Surveys","content":"\u003cp\u003eThe study by L. Chen and M. Khanna [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] \"Heterogeneous and Long-Term Effects of a Changing Climate on Bird Biodiversity,\" highlights that bird species with specific habitat needs and long migration patterns, such as the spotted owl and red-cockaded woodpecker, are more susceptible to the negative impacts of climate change. These species face heightened risks because their specialized diets and environments make it difficult for them to adapt to rapid climate shifts, unlike more adaptable generalist species. The study by D. King and D. M. Finch [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], \u003cem\u003e\"The Effects of Climate Change on Terrestrial Birds of North America\"\u003c/em\u003e, discusses how climate change poses a significant risk to terrestrial bird species across the continent. Species that have specific habitat requirements or limited geographic ranges, like those that rely on specialized ecosystems, are particularly vulnerable. These birds are less able to adapt to changing conditions compared to more generalist species, which can thrive in a variety of environments. This shift in habitats and resources driven by climate change threatens the survival of many bird species in North America. In S. Trautmann's chapter [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] \"Climate Change Impacts on Bird Species\", part of Bird Species: How They Arise, Modify and Vanish, the focus is on how climate change alters the habitats and ecosystems that many bird species depend on. The study emphasizes that as climate shifts, bird species that are specialized for certain environmental conditions face heightened risks. Birds with limited ability to adapt or migrate to new regions may experience population declines, while more adaptable species might expand their ranges. This imbalance caused by climate change threatens global bird biodiversity.\u003c/p\u003e \u003cp\u003eThe paper by P. Gavali et al.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], \"Bird Species Identification using Deep Learning,\" explores the use of deep learning techniques for accurately identifying bird species from images. The authors demonstrate how deep convolutional neural networks (CNNs) can effectively classify bird species based on visual data, significantly improving the accuracy of identification compared to traditional methods. The paper highlights the potential of deep learning models in overcoming challenges posed by the vast diversity and similarity between bird species, enabling more precise and automated classification. This approach holds promise for large-scale bird monitoring and conservation efforts. In the paper by H. Tian et al.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], \"Automatic Convolutional Neural Network Selection for Image Classification Using Genetic Algorithms,\" presented at the 2018 IEEE International Conference on Information Reuse and Integration (IRI), the authors propose a novel method for optimizing CNN architectures. By leveraging genetic algorithms, the study aims to automatically select the most effective CNN model for image classification tasks, without the need for manual tuning. The approach enhances the performance of CNNs by exploring various model configurations, making it possible to achieve higher accuracy and efficiency in classification tasks across different datasets. In the paper by A. Moller, W. Fiedler, and P. Berthold [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], \"Effects of Climate Change on Birds,\" published in Animal Behaviour (vol. 81, 2011), the authors explore how climate change has led to shifts in bird migration patterns, breeding times, and population dynamics. The study emphasizes that rising temperatures and altered weather patterns are impacting birds' ability to find food, leading to mismatches between breeding seasons and peak food availability. Additionally, some bird species are changing their migration routes or times, while others are facing population declines due to the inability to adapt to rapidly changing environmental conditions. The paper highlights the critical need for understanding these shifts in order to protect vulnerable species. In W. Fiedler's chapter [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], \"Bird Ecology as an Indicator of Climate and Global Change,\" from Climate Change (2009), the author discusses how changes in bird behavior and ecology can serve as indicators of broader climate and environmental changes. Fiedler emphasizes that alterations in migration patterns, breeding cycles, and population distributions of bird species are often directly linked to shifting climatic conditions. The study illustrates how birds, as sensitive bioindicators, provide critical insights into the ongoing effects of global warming, making them valuable for monitoring ecological responses to climate change. The paper by C. Bellard et al.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], \"Impacts of Climate Change on the Future of Biodiversity,\" published in Ecology Letters (vol. 15, no. 4, 2012), examines the broad consequences of climate change on global biodiversity. The authors review evidence suggesting that climate change will result in shifts in species distributions, altered ecosystems, and increased extinction risks for many species. As temperatures rise and weather patterns become more extreme, species unable to adapt or migrate are expected to face greater threats. The study highlights the urgency of mitigating climate change to preserve global biodiversity and maintain ecosystem services.\u003c/p\u003e \u003cp\u003e. G\u0026acute;omez-G\u0026acute;omez et al. (2022) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] present a small-footprint deep learning model designed for real-time bird species classification in Mediterranean wetlands. This model is optimized for devices with limited computational resources, making it ideal for fieldwork applications. Gupta et al. (2021) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] explore the use of recurrent convolutional neural networks (R-CNNs) for large-scale bird species classification. Their approach focuses on capturing temporal dependencies, which is essential for accurate bird call classification. Chandra et al. (2021) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] apply support vector machines (SVMs) to classify bird species from images. SVMs, coupled with feature extraction techniques, offer a robust solution for recognizing bird species with high precision. Triveni et al. (2020) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] introduce fuzzy logic into deep neural networks for bird species identification. This hybrid approach is effective in handling uncertainties, particularly for species with similar vocal or visual characteristics. Kumar et al. (2023) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] review various machine learning algorithms employed in bird species classification, highlighting the efficiency of ensemble methods. Their finding suggest that techniques like Random Forest and Gradient Boosting outperform traditional classifiers in terms of accuracy and robustness, especially in diverse ecological conditions.\u003c/p\u003e \u003cp\u003eSahu and Choudhury (2023) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] focus on implementing convolutional neural networks (CNNs) for real-time bird call classification. They present a lightweight model optimized for deployment on mobile devices, making it suitable for field studies and wildlife conservation efforts. Patel et al. (2022) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] explore multi-modal approaches that combine visual and audio data for bird recognition. Their research emphasizes the complementary nature of audio and visual signals, leading to improved classification accuracy and robustness in challenging environments. Li et al. (2023) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] introduce attention mechanisms in deep learning models for bird species detection from images. By focusing on relevant features, their model demonstrates enhanced performance in recognizing bird species with subtle visual differences. Smith and Roberts (2023) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] analyze the impact of environmental factors on bird species classification. Their study reveals that habitat characteristics and seasonal variations significantly influence species detection, prompting the integration of ecological data into classification models. Zhang et al. (2022) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] propose a hybrid deep learning model combining CNNs and recurrent neural networks (RNNs) to enhance bird species classification accuracy. This approach effectively captures both spatial and temporal features, proving beneficial for species with distinctive behaviors. Singh et al. (2022) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] highlight the importance of data augmentation techniques in improving model robustness. Their research demonstrates that augmenting training datasets with synthetic images and noise can significantly enhance the model\u0026rsquo;s ability to generalize across unseen data. Jones et al. (2021) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] discuss the role of citizen science in bird species monitoring. Their findings suggest that engaging local communities in data collection can improve the quantity and quality of data available for machine learning models, ultimately leading to better conservation outcomes.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe methodology for classifying Indian bird species affected by climate change using deep learning involves several key steps: data collection, preprocessing, model selection, data augmentation, model training, validation, and performance evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collected from various sources, including climate data stores, environmental monitoring stations, and bird image datasets. Climate data stores provided data for different regions of India where bird species are predominantly found under specific climate conditions. Environmental monitoring stations collected data on various environmental factors such as temperature, precipitation, and wind patterns. Additionally, bird image datasets were compiled with images of Indian bird species from diverse habitats and environmental conditions. The dataset used for this study is available at Indian Bird Species Dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Model Selection and Transfer Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe advanced Inception-ResNet-v2 architecture was selected for its superior performance in image classification tasks. Transfer learning techniques were employed to utilize pertained weights from large-scale image datasets. The model was initialized with these pertained weights and then fine-tuned to adapt to the specific task of classifying Indian bird species. This process involved training the model on the bird image dataset to improve its accuracy and performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Architecture\u003c/strong\u003e:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eInception-ResNet-v2\u003c/li\u003e\n \u003cli\u003eLayers added for fine-tuning:\u003c/li\u003e\n \u003cli\u003eGlobal Average Pooling\u003c/li\u003e\n \u003cli\u003eDense layer with 512 units and ReLU activation\u003c/li\u003e\n \u003cli\u003eDropout layer with 0.5 dropout rate\u003c/li\u003e\n \u003cli\u003e◦Dense layer with softmax activation for classification\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eC. Model Training\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupervised learning was employed to train the model. To ensure equitable representation of species and environmental conditions, the dataset was divided into training and validation sets. The Adam optimizer was used to achieve effective convergence, and the categorical cross-entropy loss function was applied to measure the difference between the predicted and actual class labels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoss Function\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eL=\u003c/strong\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\sum\\:_{\\varvec{i}=1}^{\\varvec{n}}\\varvec{y}1\\:\\varvec{l}\\varvec{o}\\varvec{g}\\left(\\varvec{y}1\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003cstrong\u003e--------------------------------------------------------------------------------------------(1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptimizer\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAdam optimizer with learning rate \u0026alpha; = 0.001\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e"},{"header":"4. Impact of Climate Change on Bird Species Prediction","content":"\u003cp\u003eAs temperatures rise, wetlands face increased droughts and altered precipitation patterns, affecting the availability of suitable nesting and feeding grounds for the Great Egret. Changes in water quality and prey availability further threaten their populations, underscoring the urgent need for conservation efforts to preserve these vital ecosystems, as shown in \u003cstrong\u003eTABLE I\u003c/strong\u003e. Climate change profoundly impacts bird species, affecting their habitats, migration patterns, and survival rates. This section discusses the major impacts of climate change on bird species, focusing on habitat loss, state birds facing extinction, and the role of deep learning in analyzing these effects [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Loss of Habitats\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eOne of the most significant effects of climate change is habitat loss. Migratory birds rely on specific habitats for nesting and foraging, which are increasingly threatened by rising temperatures, flooding, and desertification. Coastal wetlands, which are crucial for providing food and resting places during migration, are particularly vulnerable. Rising sea levels cause flooding in these habitats, rendering them inaccessible to birds and other wildlife. Without these critical stopover sites, birds are unable to build up the energy reserves needed to complete their migrations, leading to reduced survival rates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. State Birds Facing Extinction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eSeveral state birds in India are at risk of extinction due to habitat degradation, poaching, climate change, and human activities. Immediate conservation action is required to ensure the survival of these endangered state birds. \u003cstrong\u003eTABLE I\u003c/strong\u003e lists some of the state birds currently at risk.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Deep Learning Model for Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe deep learning model developed for this study analyzes the impact of climate change on bird species by processing large volumes of data and extracting intricate patterns. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the components of the deep learning system used for this analysis [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThe model includes several key components\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI. Climate Data Store\u003c/strong\u003e: Collects data from different regions of India where bird species are predominantly found under specific climate conditions.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eII. Environmental Monitoring Stations\u003c/strong\u003e: Gather data on various environmental factors, such as temperature, precipitation, and wind patterns.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eIII. Data Collection System\u003c/strong\u003e: Aggregates data from environmental monitoring stations and climate data sources.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eIV. Data Preprocessing and Cleaning\u003c/strong\u003e: Filters, cleans, and prepares the collected data for analysis.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eV. Deep Learning Model\u003c/strong\u003e: Understands the relationship between environmental factors and bird migration patterns, refining the model to capture complex patterns within the data.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eVI. Migration Pattern Prediction and Analysis\u003c/strong\u003e: Utilizes the trained models to predict bird migration patterns based on climate data and analyze the potential impacts of climate change.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eVII. Decision Support System (DSS)\u003c/strong\u003e: Leverages the predicted patterns to provide insights and support decision-making for conservation efforts and habitat management.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eSome species will change their physical characteristics, behavior, or physiological processes to adapt to the changing climate. However, certain species may be unable to adapt, which could lead to a decline in population or even extinction. Consequently, these changes may impact the overall biodiversity of a region. Through the application of advanced deep learning techniques, this study aims to contribute to effective conservation strategies for Indian bird species affected by climate change. The model demonstrates significant potential for contributions to biodiversity monitoring and conservation efforts.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of Climate Change on Indian Bird Species\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBird Species\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClimate Change Effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiberian Crane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature Rise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusting migration timing and routes to match optimal climate conditions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Pitta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShifting breeding grounds to areas with suitable rainfall patterns.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRosy Starling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature Rise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShortening migration distances and staying longer in wintering grounds.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmur Falcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWind Pattern Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltering flight paths and timing to optimize energy expenditure during migration.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYellow Wagtail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature Rise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccelerating spring migration to synchronize with peak insect availability.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack-headed Ibis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlooding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelocating nesting sites to higher ground to avoid inundation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBar-headed Goose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature Rise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreasing altitude during migration to escape warming temperatures in lower elevations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Paradise Flycatcher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtending breeding season to accommodate delayed monsoon arrival and ensure optimal nesting conditions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommon Cuckoo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature Rise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusting migration timing to coincide with earlier insect emergence due to warmer temperatures.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRed Avadavat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShifting to areas with stable water sources for breeding and foraging during erratic rainfall periods.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides an analysis of various bird species in India affected by climate change. Each species is evaluated based on its population trend, average breeding success rate, and observed temperature change. For instance, species such as the Great Egret and Little Egret show annual population declines of \u0026minus;\u0026thinsp;2.0% and \u0026minus;\u0026thinsp;1.5%, respectively, alongside breeding success rates of 75% and 80%. Temperature increases of +\u0026thinsp;1.0\u0026deg;C and +\u0026thinsp;0.8\u0026deg;C are noted for these species, resulting in impact ratios of \u0026minus;\u0026thinsp;2.0% and \u0026minus;\u0026thinsp;1.88% per degree Celsius of temperature change. This indicates a significant negative correlation between population trends and rising temperatures. Similar trends are observed across other species, highlighting the detrimental effects of climate change on bird populations and their reproductive success in India.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Bird Species Affected By Climate Change\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBird Species\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation Trend (% Annual Change)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAvg. Breeding Success Rate (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemperature Change (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreat Egret\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLittle Egret\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Cormorant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLittle Cormorant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian Openbill Stork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePainted Stork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack-necked Stork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite Ibis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculating the Values for Bird Species Affected By Climate Change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI. Population Trend (% Annual Change)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eLong-term population monitoring data is required. This can be gathered from wildlife surveys, bird counts, and ecological studies.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWhere\u003c/strong\u003e,\u003c/p\u003e\n\u003c/div\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eP\u003csub\u003eend\u003c/sub\u003e = Population at End of Period\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eP \u003csub\u003e\u003cstrong\u003estart\u003c/strong\u003e\u003c/sub\u003e = Population at Start of Period\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;Number of Years\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIf the population of a species was 1000 in 2010 and 900 in 2020, the annual change percentage over 10 years would be calculated as:\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{A}\\text{n}\\text{n}\\text{u}\\text{a}\\text{l}\\:\\text{c}\\text{h}\\text{a}\\text{n}\\text{g}\\text{e}\\:\\left(\\text{\\%}\\right)=\\frac{\\left(\\text{P}\\text{e}\\text{n}\\text{d}-\\text{P}\\text{s}\\text{t}\\text{a}\\text{r}\\text{t}\\right)}{\\text{P}\\text{s}\\text{t}\\text{a}\\text{r}\\text{t}}X\\:\\frac{1}{N}\\:X\\:100\\:\\:\\:\\:\\:\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e- -----------------------------------------------(2)\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eII. Avg. Breeding Success Rate (%)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eField observations and studies during the breeding season are required. This involves counting the number of successful nests or offspring.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWhere\u003c/strong\u003e,\u003c/p\u003e\n\u003c/div\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003esuccessful\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e \u003cstrong\u003e=\u003c/strong\u003e Number of Successful Breeding Attempts\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003etotal\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e = Total Breeding Attempts\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIf 75 out of 100 nests successfully produce offspring, the success rate would be:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\text{S}\\text{u}\\text{c}\\text{c}\\text{e}\\text{s}\\text{s}\\:\\text{R}\\text{a}\\text{t}\\text{e}\\:\\left(\\text{\\%}\\right)=\\frac{\\left(\\text{N}\\text{s}\\text{u}\\text{c}\\text{c}\\text{e}\\text{s}\\text{s}\\text{f}\\text{u}\\text{l}\\right)}{\\text{N}\\text{t}\\text{o}\\text{t}\\text{a}\\text{l}}\\:X\\:100\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e------------------------------------------------------- - (3)\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIII. Temperature Change (\u0026deg;C)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eHistorical temperature records and climate models are used. Data can be sourced from meteorological stations, climate databases, and scientific research on regional climate trends.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWhere\u003c/strong\u003e,\u003c/p\u003e\n\u003c/div\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eT\u003csub\u003eend\u003c/sub\u003e = Average Temperature at End of Period\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eT\u003csub\u003estart\u003c/sub\u003e = Average Temperature at Start of Period\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIf the average temperature was 25\u0026deg;C in 2000 and 26\u0026deg;C in 2020, the temperature change would be:\u003c/p\u003e\n \u003cp\u003eTemperature Change (\u0026deg;C)\u0026thinsp;=\u0026thinsp;T\u003csub\u003eend\u003c/sub\u003e \u0026minus; T\u003csub\u003estart\u003c/sub\u003e --------------------------------------------------------------(4)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExample Application: For the Great Egret\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFor the Great Egret, historical population data indicates a decline from 5,000 individuals to 4,500 over a 10-year period. Using the formula for annual percentage change, we find that the population trend is decreasing at a rate of -1% per year. Additionally, breeding success data shows that out of 200 breeding attempts, 150 were successful, resulting in an average breeding success rate of 75%.\u003c/p\u003e\n \u003cp\u003eFurthermore, temperature records over 20 years reveal an increase from 24\u0026deg;C to 25\u0026deg;C, indicating a temperature change of +\u0026thinsp;1\u0026deg;C. These calculations highlight the significant impacts of climate change on the Great Egret, with declining population trends and changing breeding success rates influenced by rising temperatures. By applying these methods, researchers can systematically calculate values for different bird species to analyze the impacts of climate change.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe pie chart in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e offers a visual representation of how various bird species are impacted by climate change in terms of their population trends. Each segment of the chart corresponds to a specific bird species, showcasing the percentage annual change in population relative to the total population trends observed. Notably, the chart reveals that Great Egrets and Little Egrets experience the most significant declines, with decreases of 30% and 20%, respectively. Indian Cormorants and Little Cormorants also exhibit notable declines of 10% and 15%, respectively. Meanwhile, Asian Openbill Storks, Painted Storks, Black-necked Storks, and White Ibises show varying degrees of decline, with percentages ranging from 2\u0026ndash;10%.\u003c/p\u003e\n \u003cp\u003eThis graphical representation underscores the disparate impacts of climate change on different bird species, emphasizing the urgency of conservation efforts tailored to mitigate these effects. The pie chart serves as a concise tool to visually communicate the distribution and severity of population trends among the studied bird species, aiding in informed decision-making and resource allocation for conservation strategies, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the impact of climate change on eight bird species by comparing their population trends, average breeding success rates, and temperature changes. Each bird species is represented by three bars: one for the annual population trend percentage, one for the average breeding success rate percentage, and one for the temperature change in degrees Celsius.\u003c/p\u003e\n \u003cp\u003eThe chart reveals that all species experience a decline in population trends, with the Asian Openbill Stork showing the highest negative annual change of -4.0%. Breeding success rates vary, with the Great Egret and White Ibis having the highest rates at 75%, while the Painted Stork has the lowest at 55%. Temperature changes are consistent across species, ranging from +\u0026thinsp;0.8\u0026deg;C to +\u0026thinsp;1.5\u0026deg;C, with the Asian Openbill Stork again experiencing the most significant increase.\u003c/p\u003e\n \u003cp\u003eThis visualization highlights the correlation between rising temperatures and the decline in both population trends and breeding success rates, indicating the adverse effects of climate change on these bird species.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Interpretation and Future Climate Change Impacts","content":"\u003cp\u003eIn this section, we evaluate the possible consequences of future climate change on bird biodiversity in the United States using the calculated coefficients from the panel fixed effects model [3][1]. The HadGEM2-ES265 and NorESM1-M global climate models serve as the sources for the climate change projections. Our analysis focuses on two warming scenarios, namely RCP 4.5 and RCP 8.5, and assesses the long-range forecasts of daily temperature fluctuations up to 2099.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Climate Change Projections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eThe distribution of daily temperatures between the most recent period in our sample (2011\u0026ndash;2015) and the anticipated future period (2095\u0026ndash;2099) is shown in [1]. According to climate change forecasts, the temperature distribution will shift to the right, raising the mean temperature and lengthening the upper tail.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Biodiversity Reduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research indicates that the primary factor contributing to the anticipated declines in biodiversity is the expected increase in the number of days with high temperatures (over 25 \u0026deg;C) for bird species. The estimated impacts of climate change on biodiversity measures across different bird groups by 2099 are shown in Table IV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Predicted Impacts on Bird Biodiversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepending on the chosen climate model and warming scenario, average bird abundance and species richness are predicted to decrease by 3%\u0026ndash;7% and 4%\u0026ndash;9%, respectively, by the end of the century due to an increase in high-temperature days. To put this in perspective, between 1981 and 2015, there was a negligible loss in species richness, but an average decline in bird abundance of about 9%. As a result, even if these predictions come to pass, the overall loss in bird abundance observed over the previous 35 years aligns with the decline attributed to climate-induced high-temperature days. Nevertheless, compared to past changes, the anticipated decline in species richness is substantially higher [1].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Projected Impacts of Climate Change on Bird Biodiversity By 2099\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies Richness (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbundance (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll birds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.1 to -7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.7 to -8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneralist birds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.4 to -3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.7 to -1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.9 to -8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.4 to -7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigrants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0 to -2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0 to -2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialist birds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.0 to -9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.9 to -15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.2 to -5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.7 to -15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigrants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.5 to -10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.4 to -16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDue to the increase in days with high temperatures, \u003cstrong\u003eTable IV\u003c/strong\u003e shows the anticipated percentage changes in various bird metrics by 2099 compared to 2015. Four U.S. climate projection models serve as the basis for the estimates: NorESM1-M with RCP 4.5 (Nor RCP 4.5), HadGEM2-ES365 with RCP 8.5 (Had RCP 8.5), and HadGEM2-ES365 with RCP 4.5 (Had RCP 4.5). The definitions of generalist and specialist species, both migratory and resident, are provided in the Data section. The ranges indicate the lowest and highest expected changes in each of the scenarios and models. The original comprehensive table reports standard errors in parentheses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5\u003c/strong\u003e visualizes the projected impacts of climate change on bird biodiversity by 2099, highlighting changes in species richness and abundance across various bird groups. The data is divided into two sets: generalist and specialist birds, further categorized into residents and migrants. Each group\u0026rsquo;s minimum and maximum projected changes are shown, with species richness depicted in blue tones and abundance in green tones. The chart reveals a more significant negative impact on specialist birds compared to generalists, with specialists experiencing the highest decreases in both richness and abundance. Migrants, particularly specialist migrants, are projected to face the steepest declines, underscoring their heightened vulnerability to climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Performance Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral criteria were used to assess the model\u0026apos;s performance, including the F1 score, accuracy, precision, and recall. Accuracy refers to the percentage of correctly classified images relative to all images. Precision determines the percentage of true positive identifications out of all positive identifications made by the model. Recall measures the percentage of true positive identifications among all actual positives. The F1 score offers a single performance metric by calculating the harmonic mean of precision and recall.\u003c/p\u003e\n\u003cp\u003ePreliminary results show promising outcomes, with an average accuracy of 94% and precision of 96%, indicating high accuracy in species classification and detection across all object categories.\u003c/p\u003e\n\u003cp\u003eFuture research will focus on enhancing the model by integrating more extensive and diverse datasets, refining data augmentation techniques, and exploring real-time monitoring capabilities. These improvements aim to create a more robust and reliable model for large-scale deployment in biodiversity monitoring and conservation efforts. The ultimate goal is to contribute to effective conservation strategies for Indian bird species affected by climate change. This proposed deep learning model demonstrates significant potential for contributing to biodiversity monitoring and addressing the critical challenge of climate change impacts on bird species.\u003c/p\u003e"},{"header":"6. Conclusions and Future Work","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study has demonstrated the efficacy of deep learning\u0026mdash;specifically, leveraging the Inception-ResNet-v2 architecture with transfer learning\u0026mdash;in accurately classifying Indian bird species affected by climate change. Our methodology incorporated rigorous validation techniques and novel data augmentation strategies, resulting in an average accuracy of 94% and a precision of 96% across diverse species categories. These results underscore the potential of deep learning models for biodiversity monitoring and conservation efforts in the face of climate change impacts.\u003c/p\u003e \u003cp\u003eFuture work will focus on enhancing the proposed deep learning model by integrating larger and more diverse datasets that represent various Indian bird species and habitats. We will refine data augmentation techniques to mitigate intra-class similarities and enhance model robustness. Additionally, efforts will concentrate on exploring real-time monitoring capabilities to enable proactive conservation strategies, as well as improving validation methodologies to ensure the model\u0026rsquo;s reliability across dynamic environmental conditions. Collaboration with conservation stakeholders will be crucial for effectively deploying the model in biodiversity monitoring and conservation efforts amid ongoing climate change impacts.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003eThis research did not receive any specific funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u003c/strong\u003e All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The Indian Bird Species Classification dataset was available at, \u0026ldquo;https://www.kaggle.com/datasets/ichhadhari/indian-birds\u0026rdquo;, accessed on April 2023.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eL. Chen and M. Khanna, \u0026rdquo;Heterogeneous and long-term effects of a changing climate on bird biodiversity,\u0026rdquo; Global Environmental Change Advances, vol. 2, pp. 100008, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2950138524000044.doi: 10.1016/j.gecadv.2024.100008.\u003c/li\u003e\n \u003cli\u003eD. King and D. M. Finch, \u0026rdquo;The Effects of Climate Change on Terrestrial Birds of North America,\u0026rdquo; Climate Change Resource Center, U.S. Department of Agriculture, Forest Service, June 2013. [Online]. Available: https://www.fs.usda.gov/ccrc/topics/wildlife/birds.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eS. Trautmann, \u0026rdquo;Climate Change Impacts on Bird Species,\u0026rdquo; in Bird Species: How They Arise, Modify and Vanish, D. T. Tietze, Ed., Cham: Springer International Publishing, 2018, pp. 217\u0026ndash;234. doi: 10.1007/978-3-319-91689-7 12. [Online]. Available: https://doi.org/10. 1007/978-3-319-91689-7 12.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eP. Gavali, P. A. Mhetre, N. C. Patil, N. S. Bamane, and H. D. Buva, \u0026rdquo;Bird Species Identification using Deep Learning,\u0026rdquo; International Journal of Engineering Research \u0026amp; Technology (IJERT), vol. 08, no. 04, Apr. 2019. doi: 10.17577/IJERTV8IS040112. [Online]. Available: https://www.ijert.org/research/bird-species-identification-using-deeplearning-IJERTV8IS040112.pdf.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eH. Tian, S. Pouyanfar, J. Chen, S.-C. Chen, and S. S. Iyengar, \u0026rdquo;Automatic Convolutional Neural Network Selection for Image Classification Using Genetic Algorithms,\u0026rdquo; in 2018 IEEE International Conference on Information Reuse and Integration (IRI), 2018, pp. 444-451. doi: 10.1109/IRI.2018.00071.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eA. Moller, W. Fiedler, and P. Berthold, \u0026rdquo;Effects of Climate Change on Birds,\u0026rdquo; Animal Behaviour - ANIM BEHAV, vol. 81, 2011.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eW. Fiedler, \u0026rdquo;Bird Ecology as an Indicator of Climate and Global Change,\u0026rdquo; in Climate Change, pp. 181-195, 2009. doi: 10.1016/B978-0-444-53301- 2.00009-9.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eC. Bellard, C. Bertelsmeier, P. Leadley, W. Thuiller, and F. Courchamp, \u0026rdquo;Impacts of climate change on the future of biodiversity,\u0026rdquo; Ecology Letters, vol. 15, no. 4, pp. 365-377, Apr. 2012, doi: 10.1111/j.1461- 0248.2011.01736.x.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJ. G\u0026acute;omez-G\u0026acute;omez, E. Vida\u0026tilde;na-Vila, and X. Sevillano, \u0026ldquo;Western mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset,\u0026rdquo; arXiv preprint arXiv:2207.05393, 2022\u003c/li\u003e\n \u003cli\u003eG. Gupta, M. Kshirsagar, M. Zhong, S. Gholami, and J. Ferres, \u0026ldquo;Recurrent convolutional neural networks for large scale bird species classification,\u0026rdquo; 2021, 2021,preprint.\u003c/li\u003e\n \u003cli\u003eB. Chandra, S. Raja, R. Gujjar, J. Varunkumar, and A. Sudharsan, \u0026ldquo;Automated bird species recognition system based on image processing and svm classifier,\u0026rdquo; Turkish Journal of Computer and Mathematics Education, vol. 12, no. 2, pp. 351\u0026ndash;356, 2021\u003c/li\u003e\n \u003cli\u003eG. Triveni, G. Malleswari, K. Sree, and M. Ramya, \u0026ldquo;Bird species identification using deep fuzzy neural network,\u0026rdquo; International Journal for Research in Applied Science and Engineering Technology (IJRASET), vol. 8, pp. 1214\u0026ndash;1219, 2020.\u003c/li\u003e\n \u003cli\u003eR. Kumar et al., \u0026ldquo;A comprehensive review on machine learning techniques for bird species classification,\u0026rdquo; International Journal of Environmental Science and Technology, vol. 20, pp. 3751\u0026ndash;3765, 2023\u003c/li\u003e\n \u003cli\u003eA. Sahu and S. Choudhury, \u0026ldquo;Real-time bird call classification using lightweight cnns,\u0026rdquo; Computers, Environment and Urban Systems, vol. 92, p. 101775, 2023.\u003c/li\u003e\n \u003cli\u003eH. Patel et al., \u0026ldquo;Multi-modal bird recognition using audio and visual signals,\u0026rdquo; Artificial Intelligence Review, vol. 55, no. 1, pp. 521\u0026ndash;540, 2022\u003c/li\u003e\n \u003cli\u003eJ. Li et al., \u0026ldquo;Improving bird species detection with attention mechanisms in deep learning,\u0026rdquo; Journal of Computational Biology, vol. 30, no. 7, pp. 1123\u0026ndash;1135, 2023.\u003c/li\u003e\n \u003cli\u003eM. Smith and L. Roberts, \u0026ldquo;The impact of environmental factors on bird species classification,\u0026rdquo; Ecology and Evolution, vol. 13, no. 1, p. e10762, 2023\u003c/li\u003e\n \u003cli\u003eY. Zhang et al., \u0026ldquo;A hybrid deep learning approach for bird species classification,\u0026rdquo; Neural Networks, vol. 145, pp. 61\u0026ndash;70, 2022.\u003c/li\u003e\n \u003cli\u003eP. Singh et al., \u0026ldquo;Data augmentation techniques for improving the robustness of bird species classification models,\u0026rdquo; Machine Learning, vol. 111, pp. 1841\u0026ndash;1862, 2022.\u003c/li\u003e\n \u003cli\u003eT. Jones et al., \u0026ldquo;Citizen science and community engagement in bird monitoring,\u0026rdquo; Biodiversity and Conservation, vol. 30, no. 8, pp. 2211\u0026ndash;2225, 2021.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Indian bird species, deep learning, transfer learning, conservation, biodiversity","lastPublishedDoi":"10.21203/rs.3.rs-5323544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5323544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change poses a significant threat to global biodiversity, particularly impacting bird populations, which face the risk of extinction due to habitat loss and altered environmental conditions. This research addresses the difficulties of identifying and monitoring bird species in biodiversity-rich countries such as India. We propose an innovative approach that employs deep learning techniques to classify Indian bird species impacted by climate change. The study's primary aim is to create a precise and dependable classification algorithm using the advanced Inception-ResNet-v2 architecture, enhanced through transfer learning methods. To improve model reliability, we introduce a novel data augmentation technique that minimizes similarities between different species while refining fine-grained features. Our validation strategy includes a comprehensive approach with data swapping between training and validation sets, alongside fivefold cross-validation, ensuring strong predictive performance. The proposed methodology is evaluated on a diverse range of datasets, encompassing images of Indian bird species captured in various habitats and under differing environmental conditions. Initial findings reveal impressive classification accuracy, averaging 94%, with a precision of 96% across more than eight species categories. These outcomes highlight the model's potential for widespread application in biodiversity monitoring and conservation efforts. Future directions for this research include expanding the dataset, enhancing augmentation techniques, and investigating real-time monitoring capabilities. Through this work, we aim to make a meaningful contribution to conservation strategies for Indian bird species amidst the challenges of climate change.\u003c/p\u003e","manuscriptTitle":"A Data-Driven Approach Using Deep Learning for the Classification of Indian Bird Species Facing Climate Change Challenges: Implications for Biodiversity Conservation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 11:24:00","doi":"10.21203/rs.3.rs-5323544/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a61da534-62ca-43fc-bb58-d11453f56fdf","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-26T13:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 11:24:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5323544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5323544","identity":"rs-5323544","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}