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\begin{document}

\title{The role of large pre-trained models in ecology and biodiversity
conservation: Opportunities and Challenges}



\author[1]{Hideyuki Doi}%
\author[2]{Takeshi Osawa}%
\author[3]{Narumasa  Tsutsumida}%
\affil[1]{Kyoto University}%
\affil[2]{National Institute for Agro-environmental Science}%
\affil[3]{Saitama University}%


\vspace{-1em}



  
  \date{May 28, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Large pre-trained models (LPMs) have the capabilities to understand
natural language, code, and diverse data including images; e.g., large
language models (LLMs), code-generative models, and large vision models
(LVMs) as well as combined as multi-modal models. We outlines the main
applications of LPMs and multi-modal LPMs for ecology and biodiversity
conservation. These applications include generating ecological data,
generating code, providing insights into public opinion and sentiment.
We highlighted the significant potential of LPMs and the potential use
of Ecology-specialized LPMs for ecology and biodiversity conservation.
They offer unprecedented opportunities for analyzing diverse data,
extracting meaningful insights, and informing conservation decisions.%
\end{abstract}%



\sloppy


Viewpoint manuscript for Ecology Letters

\par\null

\textbf{The role of large pre-trained models in ecology and biodiversity
conservation: Opportunities and Challenges}

Hideyuki Doi\textsuperscript{1,*,+}, Takeshi
Osawa\textsuperscript{2,*,+}, and Narumasa
Tsutsumida\textsuperscript{3,*,+}

\textsuperscript{1} Graduate School of Informatics, Kyoto University,
Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan

\textsuperscript{2} Graduate School of Urban Environmental Sciences,
Tokyo Metropolitan University, Minami-Osawa 1-1, Hachiouji, Tokyo,
192-0397, Japan

\textsuperscript{3} Graduate School of Science \& Engineering, Saitama
University, 255 Shimo-Okubo, Sakura ward, Saitama City, Saitama,
338-8570, Japan

+ These authors equally contributed to this study and are ordered
alphabetically.

*Corresponding authors:

Hideyuki Doi, hideyuki.doi@icloud.com

Takeshi Osawa, arosawa@gmail.com

Narumasa Tsutsumida, rsnaru.jp@gmail.com

Authorship statement

All authors contributed to the conceptualization of this viewpoint. HD
wrote the first draft. all authors contributed to writing, review, and
editing.

A data statement

We did not use any data in this Viewpoint paper.

Short running title

LPMs in ecology and conservation

Keywords

artificial intelligence, modeling, digital data, public opinion and
sentiment, remote sensing, biodiversity

Type of article: Viewpoint

Number of words in abstract: 100

Number of words in main text: 2041

Number of cited references: 25

Number of figures: 1

Number of tables: 0

\textbf{Abstract}

Large pre-trained models (LPMs) have the capabilities to understand
natural language, code, and diverse data including images; e.g., large
language models (LLMs), code-generative models, and large vision models
(LVMs) as well as combined as multi-modal models. We outlines the main
applications of LPMs and multi-modal LPMs for ecology and biodiversity
conservation. These applications include generating ecological data,
generating code, providing insights into public opinion and sentiment.
We highlighted the significant potential of LPMs and the potential use
of Ecology-specialized LPMs for ecology and biodiversity conservation.
They offer unprecedented opportunities for analyzing diverse data,
extracting meaningful insights, and informing conservation decisions.

\par\null

Recent advancements in artificial intelligence (AI) have led to the
emergence of sophisticated large pre-trained models (LPMs). These LPMs
represent a significant leap forward in AI technology, offering
unparalleled capabilities in understanding natural language, generating
human-like text, and performing complex tasks across various domains.
LPMs have shown impressive proficiency in various applications,
including natural language processing, code generation, and data/vision
analysis.

For natural language processing, large language models (LLMs) like
Generative Pre-trained Transformer (GPT) (Brown \emph{et al.} 2020),
such as ChatGPT (GPT-3 and 4), Llama, and Bard, leading the revolution
in natural language processing. These models possess the ability to
generate human-like text, decipher complex language structures, and even
translate languages with remarkable accuracy. LLMs prove invaluable for
sentiment analysis, text summarization, and cross-language communication
(e.g., Rane \emph{et al.} 2023; Patil \emph{et al.}2024a). Also,
code-generative LLM, like GitHub Copilot
(https://github.com/features/copilot) and CodeT5
(https://github.com/salesforce/CodeT5), contribute to the programming of
the codes for analysis and simulation by understanding complex code and
aiding developers in debugging. Large vision models (LVMs) have the
ability to process and interpret vast amounts of visual data with
exceptional accuracy and efficiency. LVMs utilize deep learning
techniques and extensive pre-training on massive image datasets.

Multi-modal LPMs is a combined model that integrates diverse data types,
such as text, images, and audio, broadening their scope. Using LLM+LVM,
the most developed multi-modal LPM, text-to-image (T2I) synthesis has
undergone significant advancements, particularly with the emergence of
LLM and their enhancement in LVM (Cheng \emph{et al.} 2024). These
models excel in tasks like image captioning, visual question answering,
and audio transcription, enabling more context-rich AI systems. The
potential applications of LPMs span from chatbots and virtual assistants
to content creation and language learning. A significant advantage lies
in their capacity to produce text indistinguishable from human writing,
paving the way for more natural interactions with chatbots and virtual
assistants (e.g., Rane \emph{et al.} 2023 in medical). Furthermore,
their language translation prowess holds the potential to reshape global
communication (Patil\emph{et al.} 2024a). Therefore, LPMs are now
emerging as promising tools to tackle ecological challenges by analyzing
vast data extracting meaningful insights, and forming a foundation for
well-informed conservation decisions. The integration of LPMs in
biodiversity conservation holds immense potential to revolutionize
research practices and bolster conservation efforts. The subsequent
sections delineate the applications of LPMs in ecology and biodiversity
conservation.

Ecological data generation by LPMs

LPMs play a pivotal role in processing extensive documents on ecology,
including scientific papers, reports, and online news articles,
extracting pertinent information. For instance, researchers leverage
LLMs to extract the description to identify novel or endangered species,
monitor changes in population size, and discern emerging threats to
biodiversity (e.g., Fabian et al. 2024). Many LLMs exhibit the ability
to translate across languages, enabling researchers to access crucial
information from non-English resources. Some studies suggested the
potential of non-English resources in filling conservation and
ecological knowledge gaps (e.g., Amano \emph{et al.} 2021;
Hannah\emph{et al.} 2024). Despite biodiversity information being
foundational to ecology, evolution, and conservation, Amano (\emph{et
al.} 2023) reported that non-English-language literature constituted
65\% of the references cited as biodiversity information. Therefore,
numerous natural history resources in non-English languages remain
unshared due to the language barriers. LLMs facilitate the integration
of natural history knowledge across languages, mitigating obstacles for
the researchers in selecting the language for publication.

LPMs excel in analyzing the data, such as satellite information, to
formulate statistical models that elucidate intricate relationships
between species and their surroundings by code generation. A multi-modal
LPM, LLM-LVM, can generate text from image analysis by automatically
recognizing and classifying species names based on visual data (e.g.,
Jain \emph{et al.} 2023; Parashar\emph{et al.} 2023). This feature can
enhance biodiversity monitoring and species identification efforts,
particularly in large-scale surveys (e.g., Maurer \emph{et al.} 2020;
Wang \emph{et al.} 2020). Therefore, LLM-LVM can process satellite
imagery and extract valuable environmental data, such as land cover
types, vegetation indices, and habitat connectivity. The combined use of
text mining, image analysis, and satellite data processing by LLM-LVM
has great potential for generating ecological data, which can enable
researchers and conservation practitioners to gain valuable insights for
effective biodiversity conservation and management strategies.

Code-generative LLMs foe ecolgical modeling and simulation

Code-generative LLMs play a crucial role in simulation research by
promoting reproducibility and interpretability, ensuring that research
findings can be validated and comprehended by the broader scientific
community. For example, PyRates (Gast \emph{et al.} 2023), a
code-generation tool for dynamical systems modeling applied to
biological systems, can be used for the dynamical models on ecological
insights and ecosystem management. Therefore, LLMs present a unique
opportunity for the standardization and commoditization of various
research aspects, streamlining processes from SQL and GBIF data to R or
Python codes. Researchers, by harnessing LLMs, can redirect their focus
toward leveraging the rich and diverse field data accessible through
platforms like GBIF, fostering a more comprehensive understanding of
biodiversity patterns and trends (Anderson et al. 2023). A research
approach with code-generative LLMs establishes a robust foundation for
integrating logical support and employing case studies to address
intricate conservation challenges, providing a radical yet promising
avenue to advance biodiversity understanding and guide effective
conservation strategies.

However, the utilization of code-generative LLMs in ecological modeling
poses potential limitations and challenges. A major concern revolves
around the risk of bias in the generated code, potentially leading to
inaccurate or misleading predictions (Shah \emph{et al.} 2020;
Weidinger\emph{et al.} 2021; Albrecht \emph{et al.} 2022). LLMs, being
trained on large datasets, may inherently contain biases and limitations
that are not always apparent to users. Additionally, LLMs may generate
code that proves challenging to understand or modify, restricting the
flexibility and adaptability of ecological models. To surmount these
challenges, it is imperative to rigorously evaluate the performance and
accuracy of LLM-generated code in ecological modeling applications.

Providing insights into public opinion and sentiment

LPMs have the potential to provide valuable insights into public opinion
and sentiment regarding biodiversity conservation. One application
involves using AI to generate summaries of complex environmental
reports, making them easier for the general public to understand. This
approach could enhance communication and promote wider engagement with
ecological and conservation issues.

LLMs can analyze a wide range of sources, including social media data,
which has increasingly become a significant platform for public
discourse. For instance, Lee \emph{et al.} (2023) tried to capture
public opinion on climate change by utilizing two nationally
representative climate change surveys. They found that LLMs (GPT-3)
could effectively capture presidential voting behaviors to decide the
climate change policies. Also, GPT-4 exhibits improved performance when
conditioned on both demographics and covariates. Several studies used
LLMs as a natural language processing approach to investigate public
attitudes and sentiment analysis (Koonchanok \emph{et al.}2023) for
public attitudes of news topic by Twitter, (Ahmad\emph{et al.} 2024) for
COVID-19 vaccination sentiment analysis).

LLMs have the potential to be exploited for creating fake news,
spreading propaganda, and manipulating public opinion (Chen \& Shu
2023), while their capacity to process extensive volumes of text also
enables the identification of instances\begin{CJK}{UTF8}{gbsn}　\end{CJK}\selectlanguage{english}involving fake news,
propaganda, and the manipulation of public opinion. This analytical
capability proves crucial in illuminating the narratives being
disseminated and their potential impact on public perception and
policy-making. Recognizing the influence of false narratives,
particularly concerning issues like climate change, becomes imperative
to prevent a lack of action or misguided policies. However, Patil
\emph{et al.} (2024b) provided the LLM approach for recognizing false
news using the LamaIndex (https://www.llamaindex.ai). So, multi-modal
LLMs using LLM with false-news detected LLM would be useful to summarize
the public opinion.

A significant advantage of utilizing LLMs for such analyses lies in
their immediacy. While a systematic review stands as a foundational
method for summarizing knowledge on a particular subject, it demands
considerable effort and essentially overlooks contemporaneous sources
such as news articles and social media content. Conversely, the analysis
of recent reports and information can alleviate the inherent limitations
of the systematic review approach. However, the application of LLMs in
ecology and biodiversity conservation presents its share of challenges.
The accuracy and reliability of LLMs hinge on the quality of the data
used for their training. Moreover, LLMs have the potential to perpetuate
biases and inequalities present in the data, posing a risk to the
effectiveness of conservation management efforts. Responsible use of
LLMs is paramount, considering not only their accuracy but also the
environmental impact of deploying these models for conservation
activities. LLMs should complement human expertise in conservation
rather than replacing it, acknowledging the unique insights, intuition,
and contextual understanding that models may not fully grasp. The
incorporation of LLMs in ecology and conservation necessitates
navigating intricate legal landscapes, ensuring compliance with various
international and national regulations.

Development of Ecology-specialized LPMs

Recently, there have been developments in specialized LPMs for specific
purposes (Ling \emph{et al.} 2023), such as specialized LLMs, BioMedLM,
and agriGPT, which focus on biomedicine and agriculture. It is worth
considering the potential benefits of developing specialized LLMs in
ecology and biodiversity science, as they could potentially accelerate
the advancement of the field. Specialized LPMs in ecology have the
potential to enhance our understanding of complex ecological systems and
contribute to decision-making processes related to biodiversity and
ecosystem conservation. Developing such a model requires a multifaceted
approach to create an AI system that can comprehend, generate, and
analyze ecological data and language at a high level of sophistication.
The development of LLMs specialized in Ecology is considered a
significant advancement in ecological research and biodiversity
conservation.

These models, when combined with LVM offer unparalleled opportunities to
revolutionize our understanding of complex ecological systems. There are
some models for specializing LLMs such as knowledge-updated domain
specialization (KUDS) with fine-tune step (Ling \emph{et al.} 2023).
KUDS needs more cost and less real-time use with tuning the LLMs, but
for ecological knowledge based on long-term history would be suitable to
use KUDS for the tuning. Through the use of LLMs with KUDS encompassing
ecological literature and field observations, researchers can leverage
their significant computational power to reveal complex relationships,
forecast ecosystem dynamics, and provide support for conservation
strategies based on evidence. The integration of LVMs and LLM-LPM into
ecological studies enables the synthesis of diverse data sources,
including satellite imagery, remote sensing data, and environmental
sensor networks, thereby providing comprehensive insights into ecosystem
health and resilience. In fact, (Jain \emph{et al.} 2023) developed a
LLM-LVM for text generation such as landscape category using satellite
imagery. The combination of Ecology-specialized LPMs, including LLM,
LVM, and multi-modal LPM, has the potential to advance ecological
research and promote interdisciplinary collaboration. This could lead to
more effective conservation initiatives aimed at protecting biodiversity
and ecosystem services on a global scale.

\par\null

Negative impacts of using LPMs on the environment

Rillig \emph{et al.} (2023) noted that the energy consumption of LLMs
during training and operation has significant adverse effects on the
environment. The environmental impact of LLMs is closely linked to their
immense computational requirements, which translate into extensive
electricity usage. For instance, the training process of GPT-3 alone has
been estimated to consume over 1287 MWh of energy (Rillig \emph{et
al.}2023). According to the studies conducted by Strubell \emph{et
al.}(2019) and Patterson \emph{et al.} (2021), the estimated carbon
footprint associated with this is 552.1 t of CO\textsubscript{2}. It is
worth noting that as LLMs continue to evolve and their demand for larger
datasets increases, their energy requirements are expected to escalate
even further (e.g., Luccioni \emph{et al.} 2023). Therefore, it is
important to evaluate and address the environmental impact of LPMs in a
conscientious manner. It is crucial to align with responsible
environmental practices and emphasize the necessity of ethical and
eco-friendly advancements in the field of LPMs.

Conclusion

We examine here the potential of LPMs to address challenges in ecology
and biodiversity conservation. The development and integration of
specialized LPMs, particularly those tailored for ecology and
biodiversity conservation, represent a significant advancement in
ecological research and conservation practices. Moreover, multi-modal
LPMs with LVM have the potential to offer extensive insights into
ecology and biodiversity conservation by integrating various data
sources and texts in different languages. LPMs are considered to
substantially improve methods for ecology and biodiversity conservation.
Nonetheless, it is imperative to collaborate among researchers,
practitioners, and stakeholders to achieve a successful integration of
LPM applications in ecological research and conservation practices.

Acknowledgments

We would like to thank Akira S. Mori for his comments on our manuscript.
DeepL Write has been valuable in helping us to improve our English
writing of this paper.

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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1 The illustration of the key applications of LPMs and
Ecology-specialized LPMs for ecology and biodiversity conservation.

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