A Diachronic Study of Syntactic Complexity in Academic Journal Abstracts: Disciplinary Variations Between Ecology and Linguistics

A Diachronic Study of Syntactic Complexity in Academic Journal Abstracts: Disciplinary Variations Between Ecology and Linguistics | 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 Diachronic Study of Syntactic Complexity in Academic Journal Abstracts: Disciplinary Variations Between Ecology and Linguistics Hua Lu, Zhi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8803809/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 In the context of increasing globalization and academic standardization, the stylistic features of academic writing are shaped by both disciplinary traditions and emerging communicative norms. This study explores the syntactic complexity of academic journal abstracts in the disciplines of ecology and linguistics, focusing on disciplinary variation and diachronic change between 2019 and 2024. Drawing on a corpus of 1,005 abstracts from high-impact journals, this study examines syntactic complexity patterns using indices generated by the Second Language Syntactic Complexity Analyzer (L2SCA) and the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC). Findings reveal that linguistics abstracts exhibit significantly greater clausal elaboration and use of determiners and possessives, aligning with the field’s analytic orientation and need for conceptual specificity. In contrast, ecology abstracts favor nominal compounding and adjectival modification, reflecting scientific conventions of brevity and empirical precision. Diachronic analysis shows a trend toward increased nominal density in ecology and greater clausal subordination in linguistics. These results suggest that disciplinary communicative norms shape syntactic preference and that globalization and digital-era communication are subtly reshaping academic writing styles. The study offers pedagogical implications for English for Specific Purposes (ESP) instruction and calls for more genre-sensitive academic writing guidance. syntactic complexity academic writing disciplinary variation diachronic analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction An abstract serves as a concise summary of the content of a research article, playing a pivotal role in academic writing. It functions as a brief yet comprehensive tool that enables researchers to present their ongoing work while assisting readers in quickly assessing the relevance and quality of a paper, particularly in the context of disseminating academic findings (Swales & Feak, 2009 ). Early studies on academic journal abstracts primarily focused on their rhetorical structure, examining the use of moves and steps within the abstract (Khedri et al., 2015 ; Tankó, 2017 ). These studies also extended to cross-disciplinary and cross-linguistic comparisons, exploring how rhetorical structures vary across different fields and languages (Khedri et al., 2013 ; Yin et al., 2021 ). More recently, a smaller body of research has emerged focusing on the syntactic complexity of academic journal abstracts (Liu & Li, 2024 ), particularly in the context of the increasing internationalization of academic journals and the standardization of academic writing conventions, which have led to greater uniformity in the norms and stylistic features of abstracts. Despite these standardizing trends, scholars across disciplines continue to tailor their abstracts to reflect the unique characteristics of their fields. Academic writing varies greatly in style depending on the disciplines, especially in the natural sciences and humanities (Hyland, 2005 ). According to Hyland ( 2005 ), writing in the natural sciences tends to prioritize objectivity and accuracy, whereas writing in the humanities may place greater emphasis on argumentation and critical thinking. More recent research on disciplinary variation has increasingly utilized syntactic complexity to illustrate disciplinary differences in academic writing (Lu et al., 2021 ), especially within the abstracts (Dong et al., 2023 ). By extension, the syntactic complexity of abstracts in the natural sciences and the humanities tends to exhibit very different writing characteristics. However, this contrast still remains underexplored. Against this backdrop, this study aims to investigate the syntactic complexity of academic journal abstracts in ecology and linguistics, focusing on both discipline variations and diachronic trend. Employing a corpus of 1,005 abstracts collected from Nature , this study utilized computational tools incorporating the Second Language Syntactic Complexity Analyzer (L2SCA) and the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC). It first examined whether syntactic complexity varies significantly between the two disciplines through large-grained and fine-grained indices, and then assessed the diachronic evolution of these syntactic features from 2019 to 2024. Through these analyses, this study seeks to deepen understanding of how disciplinary norms and globalization shape syntactic preferences, and provide pedagogically useful insights for academic writing instruction, thereby enhancing the efficiency of disseminating academic accomplishments. Literature Review Previous Studies on Academic Journal Abstracts Numerous studies have analyzed abstracts as an integral component of discourse, often grounding their analysis in genre analysis theory and systemic functional linguistics. Swales (1990) introduced the Create a Research Space (CARS) model to examine the introduction section of an academic paper through the functional lenses of discourse moves and steps. This move-based framework then informed subsequent studies on abstract structure. Regarding abstracts in applied linguistics, Santos (1996) examined 94 abstracts and proposed a five-move pattern: Situating the research, Presenting the research, Describing the methodology, Summarizing the results, and Discussing the research. Additional researchers have suggested alternative discourse step models for abstracts, as summarized in Table 1, which shows representative abstract move models proposed between 1985 and 1996. Table 1 Discourse Step Models for Abstracts Author (Year) Move Structure Graetz (1985) Purpose–Method–Results–Conclusion (PMRC) Swales (1990) Create A Research Space (CARS) Bhatia (1993) Introduction–Method–Results–Discussion (IMRD) Santos (1996) Situating the research–Presenting the research–Describing the methodology–Summarizing the results–Discussing the research The discourse moves models proposed during this period are grounded in genre analysis theory and have become the primary reference for subsequent scholars. Macro-level studies have further examined the internal characteristics of the abstract move structures and their relationship to the rest of the paper (Samraj, 2005; Swales, 1993). Alongside studies of macro-level abstract structures, researchers have also examined the micro-linguistic features such as lexical, grammatical, and discourse chunking elements. Lexical aspects include the prevalence of pronouns, self-referential verbs and modal verbs, while grammatical aspects exam the distribution of tenses and inflections. For instance, Ebrahimi & Chan (2015) classified the discourse roles of research article abstracts by developing a corpus to investigate grammatical variations in academic writing. Recent research has primarily focused on metadiscourse analysis within abstracts, particularly the examination of interactive metadiscourse markers (Khedri et al ., 2015), and how authors align arguments with readers, clarify abstract concepts, and enhance academic communication efficacy (Khedri et al ., 2013). Another area of interest is the examination of stance markers in abstract discourse, with studies such as Alghazo et al . (2021) investigating grammatical mechanisms and the semantic nuances of stance expression. However, there is a paucity of research regarding the trajectory of syntactic complexity in abstracts (Nasseri, 2021; Biber et al ., 2016), which this study aims to address. Although syntactic complexity has been increasingly recognized as a salient indicator of linguistic and disciplinary sophistication, especially in L2 writing research, its role in shaping the abstract genre remains insufficiently examined, particularly in relation to disciplinary variation and diachronic change. Conceptualizing and Measuring Syntactic Complexity Syntactic complexity is commonly defined as the range and sophistication of grammatical resources deployed in language production (Ortega, 2015). In earlier studies, this construct was primarily conceptualized in terms of clausal subordination. Influenced by developmental patterns observed in spoken language, researchers frequently equated greater syntactic complexity with longer sentences and higher frequencies of embedded clauses (Norris & Ortega, 2009). Consequently, large-grained indices such as mean length of sentence (MLS) and clauses per sentence (C/S) became widely adopted measures, particularly through automated tools such as the L2 Syntactic Complexity Analyzer (L2SCA; Lu, 2010). Subsequent research within the register-functional tradition has questioned the adequacy of this clause-centered approach for determinating academic prose (Biber et al. , 2011; Biber & Gray, 2016). Drawing on large-scale corpus analyses, Biber and Gray (2016) demonstrated that academic writing is systematically different from daily conversation in its grammatical realization of complexity. Academic writing tends to achieve complexity through structural compression like dense noun phrases and phrasal modification rather than relying on simple clausal elaboration. From this perspective, although a text may consist of relatively short sentences with limited clausal embedding, it still can exhibit a high degree of syntactic complexity, which is referred to informational density. Empirical studies further support this distinction. Research has shown that while traditional clausal measures are sensitive to rhetorical patterns common in the soft sciences, they are less effective in capturing the complexity of registers that rely heavily on nominal and phrasal structures in the hard sciences (Lu et al. , 2021; Staples et al. , 2016). Building on this, Kyle and Crossley (2018) proposed that fine-grained phrasal indices (e.g., complex nominals per clause) often have highly potential in reflecting writing quality and proficiency in academic contexts than holistic clausal measures. In light of these findings, this study adopts a multidimensional operationalization of syntactic complexity by combining L2SCA-based indices to capture clausal elaboration with indices from the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC; Kyle, 2016) to capture phrasal density. Together, these measures operationalize complementary dimensions of syntactic complexity that are particularly relevant to the abstract genre. Disciplinary Variation in Academic Writing Disciplinary variation in academic writing reflects fundamental differences in writing conventions, rhetorical traditions, and communicative expectations. These distinctions are shaped by factors such as the nature of knowledge production, audience expectations, and genre conventions, and they can differ markedly across disciplines (Hyland, 2000). Understanding these variations is essential for both students and scholars who aim to navigate and master the diverse academic genres encountered in their respective fields. Swales (1990) introduced a genre analysis framework for academic discourse, indicating that the organizational structures and communicative purposes of writing genres vary across disciplines. Complementing this, Bhatia (1993) proposed the concept of contextualized writing, asserting that academic writing must be understood within the specific disciplinary contexts in which it is produced. According to Bhatia (1993), academic writing cannot be fully grasped outside of the discourse communities that shape its conventions and expectations. Distinct strategies for argument construction and evidence presentation result in divergent linguistic patterns. Hyland (1998) noted that disciplines within the humanities and social sciences tend to rely more heavily on hedging strategies to express uncertainty and mitigate claims, while the natural sciences typically favor more assertive, direct language. This distinction highlights the varying nature of knowledge claims across disciplines: whereas the natural sciences often present findings as objective facts, the humanities and social sciences are more inclined to acknowledge the subjective and interpretive nature of knowledge production. Similarly, rhetorical strategies employed in academic writing also reflect disciplinary differences. Hyland (2000) suggests that fields such as engineering and medicine prioritize clarity and precision, often focusing on providing practical solutions or detailed methodologies. In contrast, disciplines like philosophy and literature value abstract reasoning and the exploration of multiple interpretations, leading to a more discursive and less linear style of writing. Corpus-based research has further illuminated these differences. Lu et al . (2021) documented substantial variations in syntactic complexity between the social sciences and engineering, affirming that stylistic norms are discipline-sensitive. Researchers have leveraged large-scale corpora of academic texts to identify language patterns across different fields (Biber & Gray, 2016) and to explore the use of metadiscourse in academic writing across disciplines (Hyland, 2008). From an educational perspective, students who are familiar with the genre-specific conventions and disciplinary expectations tend to perform better in academic writing tasks (Hyland, 2000). Therefore, examining disciplinary variation is vital for writing instruction, as it aids students in better understanding the complex dynamics of academic writing. This study focuses on the contrast between linguistics, as part of the humanities, and ecology, within the natural sciences, in order to address the gap in research regarding disciplinary differences in the syntactic complexity of academic journal abstracts. Diachronic Research in Academic Writing Diachronic research in academic writing has increasingly been deployed as an important means of examining the evolution of disciplinary writing practices and communicative norms. This type of research features academic writing as a dynamic system that evolves in response to broader changes in disciplinary professionalization and the globalization of scholar communication rather than a stable ad homogeneous phenomenon (Wang et al. , 2023). From this perspective, diachronic analyses of academic texts frequently trace how linguistic features such as syntactic and lexical complexity become routinized, redistributed, or refunctionalized within specific period (Yang & Pan, 2024; Zhou et al. , 2025). Such analyses contribute not only to a more nuanced understanding of the historical development of academic genres (Swales, 1990; Hyland, 2004), but also to pedagogical discussions in English for Specific Purposes (ESP), particularly in contexts characterized by increasing information density and publication pressure (Lillis & Curry, 2010). Building on this, a growing body of empirical research has investigated diachronic changes in linguistic complexity across disciplines and genres. For example, Wang et al. (2023) proposed that early scientific writing has undergone substantial restructuring over time. They documented a prominent increase in lexical and morphological complexity and a decline in syntactic complexity in scientific texts between 1821 and 1920, which suggests a redistribution of complexity across linguistic domains. Focusing on more recent decades, several studies show differentiated diachronic trajectories for clausal and phrasal complexity. Comparative analyses conducted by Yang and Pan (2024) and Pan and Yang (2024) across disciplines such as Medicine and Mechanical Engineering indicate a general increase in phrasal complexity while clausal complexity has remained relatively stable or shown a downward trend. Evidence from lexical analyses from Pan and Yang (2025) and Zhou et al. (2023) further supports this pattern. They found consistent increases in lexical richness in high-impact journals such as Nature, revealing a broader tendency toward informational compression and specialization in contemporary academic context. Despite these advances, current diachronic research in academic writing has extensively examined broad disciplinary contrasts and long time spans, with comparatively limited attention to fine-grained variation in academic journal abstracts during recent periods. To address this gap, this study aims to focus on both large-grained and fine-grained diachronic variation of abstracts, particularly in relation to the most recent periods (e.g., 2019–2024) characterized by intensified digital dissemination and publication competition. Research Questions In light of the research gaps discussed above, this study provides an in-depth analysis of disciplinary variation in syntactic complexity of academic journal abstracts and their changes over time, using ecology and linguistics academic journal abstracts as examples. The specific research questions designed for this study are as follows: (1) What features do the syntactic complexity of academic journal abstracts in ecology and linguistics exhibit? (2) Are there variations in the syntactic complexity of academic journal abstracts between the disciplines of ecology and linguistics? If yes, what are the distinctions? (3) Has there been any alteration in the syntactic complexity of academic journal abstracts over the past decade? If yes, what are the prevailing trends? Research Methodology Corpus Design The research corpus was curated from Nature and its sub-journals to ensure editorial consistency and disciplinary representativeness. Ecology and Linguistics were selected to represent two distinct academic traditions. The former exemplifies the natural science disciplines, which emphasis on empirical precision and brevity, while the latter reflects the humanities discipline, focusing on theoretical argumentation and conceptual definition (Hyland, 2000). These two disciplines are rarely compared in previous studies (Nesi & Gardner, 2012). A total of 536 ecology abstracts and 469 linguistics abstracts were collected. This study specifically targeted the year 2019 and 2024 based on the data availability and trend relevance. Preliminary searches indicated that the volume of relevant research articles in the target journals was limited before 2018, which means that they cannot provide a representative corpus. However, publication output experienced a significant and stable increase since 2019, reaching its peak in 2024. Therefore, 2019 and 2024 were selected as critical time points to capture the most recent shifts in academic writing practices. The distribution of abstracts across disciplines and years is summarized in Table 2. Ecology abstracts were predominantly sourced from Nature Ecology & Evolution , while linguistics abstracts were drawn from Nature Human Behaviour , reflecting the journals’ alignment with their respective disciplinary (Hyland, 2005). This design enables a balanced comparison of syntactic complexity across disciplines and time periods, addressing the dual forces of globalization and disciplinary tradition (Hyland, 2000; Canagarajah, 2002). Table 2 Corpus Composition by Discipline and Year Discipline Number (2019) Number (2020) Number (2021) Number (2022) Number (2023) Number (2024) Total Number Words Ecology 85 72 75 86 102 116 536 98,726 Linguistics 45 42 47 55 108 172 469 95,489 As shown in Table 2, the sample sizes for the sub-corpora (e.g., Linguistics-2019 vs. Linguistics-2024) are not perfectly equal. This discrepancy reflects the objective reality of publication volumes in the selected journals across different years and disciplines. To ensure the authenticity and representativeness of the dataset, this study included all eligible empirical abstracts within the timeframe rather than artificially reducing the sample size to achieve numerical balance, which might compromise the statistical reliability of the corpus. The selection criteria strictly prioritized empirical research articles to minimize rhetorical variability, excluding reviews, editorials, and commentaries (Swales & Feak, 2009). Syntactic Complexity Indices Syntactic complexity was operationalized using 18 indices derived from two computational tools: the L2 Syntactic Complexity Analyzer (L2SCA; Lu, 2010) and the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC; Kyle & Crossley, 2018). These tools were selected for their complementary strengths in quantifying global, clausal, and phrasal complexity. From L2SCA, key metrics included mean sentence length (MLS), reflecting overall textual density; subordinate clauses per sentence (C/S), indicative of hierarchical syntactic structures; and complex nominals per clause (CN/C), capturing noun phrase elaboration (Lu, 2010). TAASSC complemented the clause- and sentence-level measures by providing a range of dependency-based, fine-grained syntactic indices that capture structural variation within nominal phrases. Metrics such as determiners per nominal (Det/N), adjectival modifiers per nominal (Adj/N), and nouns as nominal dependents (NN/N) quantify the density and complexity of noun phrase constructions, revealing how disciplines differ in referential clarity and descriptive elaboration. Additional features like possessives per nominal (Poss/N) and relative clause modifiers (RCmod/N) offer insight into the extent of conceptual embedding and syntactic expansion strategies employed in academic abstracts (Kyle & Crossley, 2018). All indices utilized in this study are illustrated in table 3 and table 4. Table 3 L2SCA Syntactic Complexity Indices and Descriptions Index Full Name Description MLS Mean Length of Sentence Average number of words per sentence, indicating overall textual density. MLT Mean Length of T-unit Average number of words per T-unit, reflecting the expansion of independent clauses. MLC Mean Length of Clause Average number of words per clause, indicating elaboration within clauses. T/S T-units per Sentence Average number of T-units per sentence, revealing syntactic density. C/S Clauses per Sentence Average number of clauses per sentence, showing clause complexity. C/T Clauses per T-unit Average number of clauses per T-unit, measuring structural expansion. DC/C Dependent Clauses per Clause Average number of dependent clauses per clause, reflecting syntactic subordination. DC/T Dependent Clauses per T-unit Average number of dependent clauses per T-unit, showing embedding density. CT/T Complex T-units per T-unit Proportion of T-units that are complex, reflecting structural sophistication. CN/T Complex Nominals per T-unit Average number of complex nominals per T-unit, indicating nominal elaboration. CN/C Complex Nominals per Clause Average number of complex nominals per clause, reflecting dense noun structures. CP/T Coordinate Phrases per T-unit Average number of coordinate phrases per T-unit, showing syntactic extension. CP/C Coordinate Phrases per Clause Average number of coordinate phrases per clause. VP/T Verb Phrases per T-unit Average number of verb phrases per T-unit, reflecting verbal complexity. Table 4 Selected TAASSC Metrics and Descriptions Index Full Name Description Dep/N Dependents per Nominal Average number of dependents per nominal (excluding pronouns), indicating syntactic density. Det/N Determiners per Nominal Proportion of determiners modifying nominals, reflecting definiteness and referential clarity. Adj/N Adjectival Modifiers per Nominal Proportion of adjectives used to modify nominals, signaling descriptive elaboration. Prep/N Prepositions per Nominal Proportion of prepositional modifiers per nominal, reflecting complex relational structures. Poss/N Possessives per Nominal Frequency of possessive constructions modifying nominals, indicating possession relationships. Vmod/N Verbal Modifiers per Nominal Proportion of verb-based modifiers (e.g., participles) per nominal, reflecting embedded verbal structures. NN/N Nouns as Nominal Dependents Frequency of nouns modifying other nouns, indicating compound or dense noun phrase structures. RCmod/N Relative Clause Modifiers per Nominal Proportion of relative clauses used to modify nominals, showing syntactic complexity. Statistical Analysis To ensure the robustness of the quantitative analysis, all syntactic complexity indices generated by L2SCA and TAASSC were imported into SPSS 27.0 for statistical processing. Prior to inferential analysis, the data underwent a systematic screening procedure. Outliers were identified using Z-scores, with values exceeding ±3.0 excluded to reduce the influence of extreme observations on the distribution of the data (Field, 2018). Subsequently, assumptions underlying parametric analyses were examined. Data normality was assessed via the Shapiro–Wilk test, and homogeneity of variance across groups was evaluated using Levene’s test. Based on these diagnostics, independent-samples t-tests were conducted to examine differences in syntactic complexity indices across disciplines (Ecology vs. Linguistics) and across time periods (2019 vs. 2024). In cases where the assumption of equal variances was not met, Welch-adjusted t-tests were applied. In addition to null-hypothesis significance testing (set at p < .05), the magnitude of the observed differences was quantified by calculating Cohen’s d effect sizes (Cohen, 1988). This combination of significance testing and effect size estimation follows established methodological recommendations in applied linguistics research and facilitates a more informative interpretation of disciplinary and diachronic variation in syntactic complexity (Biber et al. , 2011; Larson-Hall, 2016). Results This section reports the results of the syntactic complexity analysis of academic journal abstracts in ecology and linguistics. The results are organized in accordance with the three research questions. Overall Syntactic Features of Academic Abstracts To address the first research question regarding the overall features of syntactic complexity in ecology and linguistics, descriptive statistics were calculated for the entire dataset. As shown in Figure 1, although linguistics and ecology abstracts exhibit broadly comparable overall syntactic density, they diverge in how complexity is structurally realized, particularly in fine-grained nominal modification. Table 5 and Table 6 present the large-grained and fine-grained profiles of both disciplines. Table 5 Large-Grained Syntactic Complexity by Discipline (All Abstracts) Index Linguistics-All Ecology-All MLS 25.89 25.34 MLT 24.5 23.58 MLC 15.93 14.88 C/S 1.63 1.7 T/S 1.06 1.07 C/T 1.54 1.58 DC/C 0.35 0.37 DC/T 0.54 0.59 CT/T 0.41 0.47 CN/T 3.88 3.95 CN/C 2.52 2.5 CP/T 0.89 0.79 CP/C 0.58 0.5 VP/T 2.28 2.2 Table 6 Fine-Grained Syntactic Complexity by Discipline (All Abstracts) Metric Linguistics-All Ecology-All Dep/N 1.57 1.57 Det/N 0.32 0.23 Adj/N 0.4 0.46 Prep/N 0.31 0.31 Poss/N 0.046 0.032 Vmod/N 0.038 0.039 NN/N 0.19 0.24 RCmod/N 0.026 0.026 Table 5 presents the large-grained syntactic complexity indices. Overall, the two disciplines display comparable levels of global syntactic complexity. Mean Length of Sentence (MLS) is similar across disciplines, with linguistics abstracts averaging 25.89 words per sentence and ecology abstracts averaging 25.34 words. Comparable patterns are also observed for Mean Length of T-unit (MLT: 24.50 vs. 23.58) and Mean Length of Clause (MLC: 15.93 vs. 14.88). Measures related to clausal organization show only minor differences. Clauses per sentence (C/S) and T-units per sentence (T/S) are nearly identical across the two disciplines. Similarly, indices of clausal subordination, including dependent clauses per clause (DC/C) and dependent clauses per T-unit (DC/T), remain comparable in both corpora. Complex nominal density is relatively high in both disciplines. Ecology abstracts show slightly higher values for complex nominals per T-unit (CN/T = 3.95), whereas linguistics abstracts demonstrate marginally higher values for verb phrases per T-unit (VP/T = 2.28). Table 6 reports the fine-grained syntactic complexity indices. Both disciplines exhibit identical values for dependents per nominal (Dep/N = 1.57) and relative clause modifiers per nominal (RCmod/N = 0.026). Differences are observed in the distribution of nominal modifiers. Linguistics abstracts show higher values for determiners per nominal (Det/N = 0.32) and possessives per nominal (Poss/N = 0.046), while ecology abstracts demonstrate higher levels of adjectival modification (Adj/N = 0.46) and noun–noun modification (NN/N = 0.24). Other indices, including prepositional modifiers (Prep/N) and verbal modifiers (Vmod/N), remain similar across the two disciplines. Taken together, the results in Tables 5 and 6 indicate that ecology and linguistics abstracts share broadly similar overall syntactic density, while differences emerge primarily in the distribution of fine-grained nominal modification strategies. Disciplinary Variation in Syntactic Complexity To address RQ2, independent-samples t-tests were conducted to examine whether statistically significant differences exist between ecology and linguistics abstracts. Figure 2 illustrates the distributional differences in selected large-grained and fine-grained syntactic complexity indices between ecology and linguistics abstracts. The results of the large-grained and fine-grained comparisons are presented in Tables 7 and 8, respectively. Table 7 Large-Grained Syntactic Complexity Comparison Metric F (Levene) Sig. (Levene) t df Sig. (2-tailed) MLS 1.5205 0.2851 0.8131 2.09 0.4983 MLT 2.059 0.2246 1.4073 2.05 0.2921 MLC 0.9466 0.3857 1.7531 2.8 0.1844 C_S 1.7576 0.2556 -0.1053 2.13 0.9252 VP_T 1.3821 0.3049 2.0504 2.21 0.1646 C_T 2.2361 0.2091 0.1112 2.07 0.9213 DC_C 2.3355 0.2012 -0.0041 2.1 0.9971 DC_T 2.1418 0.2172 0.1099 2.09 0.9221 T_S 0.0008 0.9786 -1.2094 4.0 0.2931 CT_T 1.9526 0.2348 -0.2836 2.07 0.8026 CP_T 0.1251 0.7415 1.7503 3.8 0.1587 CP_C 0.5055 0.5164 0.9092 2.89 0.4327 CN_T 2.3202 0.2024 0.0202 2.0 0.9857 CN_C 0.6234 0.474 -0.2037 3.1 0.8512 Table 8 Fine-Grained Syntactic Complexity Comparison Metric F (Levene) Sig. (Levene) t df Sig. (2-tailed) Dep/N 2.756 0.1722 -0.1212 2.05 0.9144 Det/N 0.554 0.498 10.0892 3.11 0.0018 Adj/N 2.9508 0.161 -8.2868 2.03 0.0137 Prep/N 0.323 0.6002 0.4698 2.8 0.6726 Poss/N 0.483 0.5253 3.9617 2.59 0.0375 Vmod/N 1.1304 0.3476 -1.1352 2.65 0.3484 NN/N 2.6286 0.1803 -3.7348 2.03 0.0632 RCmod/N 1.6312 0.2706 0.6411 2.27 0.5801 As for Table 7, in the large-grained comparison, which includes sentence- and clause-level measures, no statistically significant differences were observed between the two disciplines (p > .05). However, descriptive statistics indicate that linguistics abstracts generally yielded higher scores in Mean Length of Sentence (MLS) and Verb Phrases per T-unit (VP/T). A more nuanced pattern emerged from the fine-grained syntactic metrics analyzed via TAASSC. Linguistics abstracts displayed significantly higher use of determiners (Det/N: p = 0.0018) and possessives (Poss/N: p = 0.0375). Conversely, ecology abstracts exhibited a significantly higher frequency of adjectival modification (Adj/N, p = 0.0137). As visualized in Figure 2, the violin plot for Adj/N in ecology displays a wider density distribution positioned at a higher value range compared to linguistics. This shape indicates that the reliance on adjectival modification is not merely an artifact of outliers, but a consistent disciplinary norm adhered to by the majority of ecology abstracts. In contrast, the linguistics distribution is concentrated at lower values, further confirming that dense pre-modification is less characteristic of the field. No significant differences were found in the density of nominal dependents (Dep/N), prepositions (Prep/N), or verbal structures (Vmod/N). Diachronic Changes in Syntactic Complexity To examine temporal shifts in syntactic complexity, a diachronic analysis was conducted by comparing academic abstracts from two time points: 2019 and 2024. Both large-grained and fine-grained syntactic indices were assessed across the ecology and linguistics subcorpora to identify evolving stylistic patterns. To further illustrate the diachronic patterns identified in Tables 9 and 10, Figures 3 and 4 visualize the changes in selected large-grained and fine-grained syntactic complexity indices between 2019 and 2024. Table 9 Large-Grained Syntactic Complexity by Subcorpus Index Linguistics-2019 Linguistics-2024 Ecology-2019 Ecology-2024 MLS 28.91 24.97 25.95 25.56 MLT 26.64 23.64 23.56 23.86 MLC 15.21 16.55 15.10 15.48 C/S 1.90 1.51 1.72 1.65 T/S 1.09 1.06 1.10 1.07 C/T 1.75 1.43 1.56 1.54 DC/C 0.43 0.31 0.36 0.36 DC/T 0.75 0.44 0.56 0.55 CT/T 0.56 0.35 0.46 0.44 CN/T 4.29 3.72 3.96 3.97 CN/C 2.45 2.60 2.54 2.58 CP/T 0.81 0.88 0.73 0.84 CP/C 0.46 0.62 0.47 0.55 VP/T 2.44 2.22 2.15 2.18 Table 10 Fine-Grained Syntactic Complexity by Subcorpus Metric Linguistics-2019 Linguistics-2024 Ecology-2019 Ecology-2024 Dep/N 1.51 1.61 1.57 1.58 Det/N 0.34 0.32 0.24 0.21 Adj/N 0.39 0.42 0.46 0.46 Prep/N 0.32 0.31 0.31 0.32 Poss/N 0.038 0.047 0.030 0.034 Vmod/N 0.034 0.041 0.040 0.042 NN/N 0.15 0.21 0.24 0.25 RCmod/N 0.039 0.019 0.026 0.021 As shown in Table 9, ecology abstracts display relatively stable sentence-level complexity across the two time points. Mean Length of Sentence (MLS) shows a slight decrease from 25.95 in 2019 to 25.56 in 2024, while measures of clausal density such as clauses per sentence (C/S) and clauses per T-unit (C/T) remain largely unchanged. Indices of subordination, including DC/C and DC/T, also show minimal variation. At the phrasal level, modest increases are observed in nominal complexity. Complex nominals per clause (CN/C) increase from 2.54 to 2.58, and noun–noun modification (NN/N) rises slightly from 0.24 to 0.25. Adjectival modification (Adj/N) remains stable across the two time points, while prepositional modifiers (Prep/N) show a slight increase. Table 9 also shows that linguistics abstracts exhibit noticeable changes in several large-grained indices over time. Mean Length of Sentence (MLS) decreases from 28.91 in 2019 to 24.97 in 2024. Measures of clausal density and subordination, including clauses per sentence (C/S), clauses per T-unit (C/T), and dependent clauses per T-unit (DC/T), also show declines across the two periods. Fine-grained indices reported in Table 10 reveal increases in several nominal and verbal modification measures. Verbal modifiers per nominal (Vmod/N) increase from 0.034 to 0.041, and noun–noun modification (NN/N) rises from 0.15 to 0.21. Determiner use (Det/N) shows a slight decrease, while possessive constructions (Poss/N) increase from 0.038 to 0.047. Relative clause modifiers per nominal (RCmod/N) decrease over time. Discussion Overall Syntactic Profiles of Academic Abstracts The results show that although Ecology and Linguistics exhibit comparable levels of overall syntactic complexity, they differ significantly in the ways complexity is structurally realized at the phrasal level. These results are consistent with prior findings that humanities disciplines favor clausal elaboration for argumentative purposes, while natural sciences rely more on nominal constructions to convey dense information concisely (Hyland, 2005; Biber et al ., 2016; Pan & Yang, 2024; Yang & Pan, 2024). More specifically, Linguistics abstracts display a substantially higher frequency of determiners and possessive constructions. This pattern can be interpreted as reflecting the discipline’s epistemological orientation toward referential precision. In humanities-oriented inquiry, abstract entities and theoretical constructs often require explicit specification and attribution (e.g., the speaker’s pragmatic intent ) in order to support fine-grained conceptual argumentation. Ecology abstracts, by contrast, show a stronger preference for adjectival modification and noun–noun compounding. Such structures align with scientific conventions of lexical density, whereby complex empirical phenomena are categorized and differentiated through pre-modification rather than through extended clausal elaboration (Pan & Yang, 2024). Example 1 Hunting can fundamentally alter wildlife population dynamics but the consequences of hunting on pathogen transmission and evolution remain poorly understood (Fountain-Jones et al ., 2019). Example 2 Using a Bayesian framework for modelling spoken word recognition, we find that computational models can replicate adult interpretations of children’s speech only when they include strong, context-specific prior expectations about the messages that children will want to communicate (Meylan et al. , 2023). Example 1 balances coordination and subordination, presenting two coordinated clauses followed by a relative clause. The nominal construction “the consequences of hunting on pathogen transmission and evolution” exemplifies moderate non phrase complexity. The syntactic structure supports an informative yet concise style typical of ecological abstracts. In contrast, Example 2 illustrates conceptual layering. This sentence uses a participial phrase “using a bayesian framework...” and the reporting verb “find that” to introduce a complex complement clause. And this is further elaborated by a conditional subordinate clause “only when...” and a nested relative clause “that children will want to communicate”. Such utilization of multiple levels of embedding indicates a preference for hypotactic structures in linguistics abstracts to preciesely qualify theoretical mechanisms. In summary, linguistic abstracts are syntactically more elaborate at the noun and clause levels, employing greater referential detail and subordination. Ecology abstracts, meanwhile, favor lexical precision and dense nominalization. These results underscore how disciplinary communicative norms shape syntactic complexity in academic abstracts. Interpreting Disciplinary Variation The comparative analysis of syntactic complexity in academic journal abstracts from ecology and linguistics reveals significant disciplinary variations, shaped by distinct communicative norms and epistemic objectives. These differences are evident across both fine-grained grammatical structures and large-grained textual patterns, as outlined below. Comparison of Large-Grained Indices Although large-grained indices such as sentence length indicate broad similarities across the two disciplines, closer inspection reveals systematic differences in how complex structures are deployed. Ecology abstracts make extensive use of nominal compounds to efficiently package technical information. Expressions such as “carbon-sequestration” potential condense entire processes into single conceptual units, reflecting the economy of expression valued in the natural sciences (Wang et al. , 2023; Zhou et al. , 2023). Example 3 Atmospheric phosphorus supply exceeds demand along forest succession, whereas forests rely on soil stocks to meet their base cation demands (Bauters et al., 2022). Example 4 The translator’s individuation process is modeled to show how a translator mobilizes the meaning resources in the repertoire, which is constrained by the allocation of the cultural reservoir (Yu & Chang, 2024). Example 3’s two independent clauses are coordinated, but each contains embedded noun phrases (e.g., “Atmospheric phosphorus supply exceeds demand..., whereas forests rely on soil stocks...”) that extend its length. This structure, while readable, shows information-dense yet syntactically linear construction, typical of empirical disciplines. However, Example 4 illustrates greater clausal nesting. The post-modifying phrase “constrained by” illustrates heavy use of passive participial clauses modifying complex noun phrases, demonstrating syntactic elaboration common in linguistics (Pan & Yang, 2024). Although statistical differences in clause-level subordination measures such as C/S and DC/C were not significant (p > 0.05), qualitative distinctions in clause usage were observed. Linguistics abstracts employed subordinate clauses primarily to articulate conceptual complexity and theoretical nuance, while ecology used them to contextualize empirical observations. These contrasting syntactic applications underscore Swales’ (1990) notion that academic discourse is genre- and discipline- sensitive, adapting linguistic resources to fulfill divergent communicative purposes—argumentative elaboration in linguistics and empirical description in ecology. Comparison of Fine-Grained Indices At the micro-grammatical level, distinct syntactic preferences emerge between the disciplines of linguistics and ecology. Linguistics abstracts make more frequent use of determiners and possessive constructions, reinforcing referential precision and conceptual boundary marking. For example, phrases such as “the speaker’s pragmatic intent” illustrate how possessives articulate theoretical boundaries, a rhetorical hallmark of linguistic inquiry (Biber & Gray, 2016). This syntactic strategy supports the field’s broader orientation toward analytic elaboration and abstract theorization, often manifested through embedded and referentially rich structures (Ortega, 2015). In ecology, adjectival modification foregrounds measurable attributes and categorical distinctions essential to empirical description. This lexical tendency corresponds with the communicative goals of natural sciences, where the quantification and specification of phenomena are paramount (Hyland, 2005). While differences in prepositional phrases (Prep/N) and verbal modifiers (Vmod/N) were not statistically significant, ecology abstracts nonetheless exhibited a slightly higher incidence of prepositional structures, such as “under drought conditions,” pointing to the field’s need to spatially and temporally contextualize variables. These patterns align with Biber and Gray’s (2016) disciplinary framework, in which humanities disciplines like linguistics favor syntactic elaboration through clausal subordination and referential markers, while STEM fields such as ecology tend toward noun phrase density and information compression. The significantly higher Det/N ratio in linguistics (0.32 vs. 0.23 in ecology) underscores its prioritization of definitional precision and theoretical specificity. Conversely, ecology’s higher Adj/N ratio (0.46 vs. 0.40 in linguistics) reflects a lexical style attuned to the measurement and description of observable data. Example 5 While the contribution of biodiversity to supporting multiple ecosystem functions is well established in natural ecosystems, the relationship of the above- and below-ground diversity with ecosystem multifunctionality remains virtually unknown in urban greenspaces (Fan et al ., 2023). Example 6 People’s implicit gender associations are strongly predicted by gender associations encoded in the statistics of the language they speak (Lewis & Lupyan, 2020). In Example 5, the complex noun phrase “the relationship of the above- and below-ground diversity with ecosystem multifunctionality” and the subordinate clause “While the contribution... is well established...” show moderate non phrase elaboration and controlled use of subordination, balancing empirical clarity with brevity. On the contrary, Example 6 highlights embedded referential structures. The use of noun phrase “gender associations encoded in the statistics of the language they speak” reflects fine-grained nominal elaboration. This supports theoretical exposition by clearly specifying referents and semantic nuance. In sum, syntactic complexity is not distributed uniformly across disciplines but is closely tied to their respective epistemologies and rhetorical traditions. Linguistics favors referential elaboration and theoretical clarity, whereas ecology emphasizes descriptive economy and nominal precision—distinct yet equally strategic modes of scholarly communication. Explaining Diachronic Trends Ecology abstracts show an increasing reliance on nominal complexity, particularly through compound noun phrases and adjectival modification. These patterns suggest a diachronic trend toward syntactic compression and technical density (Yang & Pan, 2024; Zhou et al. , 2023.) Example 7 We find that structural overshoot contributed to around 11% of drought events during 1981–2015 and is often associated with compound extreme drought and heat, causing faster vegetation declines and greater drought impacts compared to non-overshoot related droughts (Zhang et al. , 2021). The compact construction “structural overshoot” and the causative gerund clause “causing...” show how newer ecological abstracts embed causal relationships in noun phrase-heavy constructions. This syntactic compression facilitates high-density scientific reporting. This trend also reflects a broader disciplinary shift toward information density and communicative efficiency. Rather than expanding syntactic structures through subordination, ecology abstracts increasingly opt for nominal groups that compress entire conceptual relations (e.g., “structural overshoot”) into single constituents. This reflects the discipline’s need to quickly convey results and implications within strict space limitations, particularly in high-impact journals like Nature. Such constructions enable dense lexical packing, which supports ecological writing’s emphasis on empirical exactitude and observational generalizability. Additionally, the rise of environmental modeling and data-intensive research in ecology coincides with increased use of compound noun phrases, participial constructions, and technical modifiers. These stylistic features parallel the growing epistemological emphasis on systems thinking, algorithmic interpretation, and network-based ecological modeling, all of which favor syntactic compaction over interpretive elaboration (Wang et al. , 2023). Example 8 Here we provide evidence for a novel psychological pathway for the expression of such biases, in which they arise as a consequence of the automatized mechanisms by which humans retrieve words to produce sentences (Brough et al. , 2024). Linguistics abstracts exhibit stable or slightly increasing levels of clausal subordination and verbal complexity. Example 8 showcases a central noun (“novel psychological pathway”) post-modified by a relative clause (“in which they arise as a consequence...”), adding specificity and hierarchical syntactic layering, which is a characteristic feature that appears to increase in recent years. These increases in subordinate clause use and verbal modification may be tied to the discipline’s ongoing concern with interpretive nuance, methodological reflexivity, and metadiscursive framing. For example, the frequent use of relative clauses not only adds syntactic complexity but also serves a clarificatory and argumentative function in establishing fine-grained conceptual relationships. This is evident in constructions such as “mechanisms by which humans retrieve words to produce sentences”, where the clause delineates scope and intent with rhetorical precision. Moreover, the enduring reliance on hypotactic structures in linguistics may reflect broader epistemological preferences for layered explanation, definitional precision, and theoretical specification. In contemporary discourse-oriented studies, this trend is reinforced by critical approaches that emphasize multi-level interpretation and ideological positioning, which are often syntactically realized through elaborate embedding, hedging, and thematic fronting. These features underscore the discipline’s continuing prioritization of analytical depth and theoretical clarity in scholarly communication. Cross-Disciplinary Comparison of Diachronic Patterns Cross-disciplinary comparisons highlight diverging responses to evolving academic communication pressures. Ecology increasingly adopts compact, noun-heavy constructions, indicating a drift toward lexical condensation. Linguistics maintains hypotactic syntactic structures, emphasizing analytical clarity. Example 9 Household income was positively associated with pollinator abundance in gardens, highlighting the influence of socioeconomic factors (Baldock et al. , 2019). Example 10 Psycholinguistic manipulations pinpoint that the bias is caused by greater accessibility in memory of words that refer to in-group than out-group members (Brough et al. , 2024). In Example 9, the participial phrase “highlighting the influence of socioeconomic factors” acts as a modifier, compressing evaluative criteria into a nominal adjunct. This structure exemplifies the trend toward nominalization and evaluative succinctness in contemporary ecological writing. By contrast, in Example 10, the structure includes both abstract noun phrases with heavy post-modification (“accessibility in memory of words”) and a defining relative clause (“that refer to in-group than out-group members”). These contrasting trajectories reflect different disciplinary responses to globalization, standardization, and digital publishing environments. While both fields adapt to evolving expectations, they preserve core stylistic identities, underscoring the resilience of disciplinary writing conventions in the face of broader communicative change. Quantitative comparisons between the 2019 and 2024 subcorpora further underscore these stylistic bifurcations. Although many metrics remained statistically stable, notable diachronic shifts emerged. In ecology, compound nominal density (CN/T) increased from 3.62 to 3.98, dependents per noun (Dep/N) and adjectival modifiers (Adj/N) both rose, while prepositional phrase use (Prep/N) declined from 0.321 to 0.298. These changes reflect a strategic syntactic shift from analytic to synthetic structures, wherein phrases such as “mortality caused by climate change” are increasingly replaced by “climate-change-induced mortality”—a compressed form that satisfies both disciplinary precision and editorial economy (Biber & Gray, 2016; Pan & Yang, 2024; Wang et al. , 2023). Conversely, linguistics abstracts exhibit signs of expanded clausal development. Relative clause frequency (RCmod/N) doubled from 0.004 to 0.008, and verbal modifiers (Vmod/N) rose from 0.035 to 0.047. These increases signal a growing reliance on clausal subordination to accommodate disciplinary pluralism and interdisciplinary integration, especially in areas intersecting with computational linguistics, sociolinguistics, and discourse analysis. Sentences like “the model, which incorporates diachronic corpus data...” exemplify syntactic elaboration as a strategy for conceptual accommodation and epistemic transparency. Macro-level structural features further differentiate the disciplines. Ecology abstracts show a modest decline in mean sentence length (MLS: from 26.2 to 24.8 words) while maintaining stable clause density (C/S), reinforcing their shift toward nominal compaction. Linguistics abstracts, in contrast, maintain long sentence structures (MLS: from 27.9 to 28.7 words) and show increased verb phrase density (VP/T: from 2.32 to 2.48), consistent with an enduring preference for multi-clausal exposition. Both disciplines demonstrate slight reductions in lexical diversity, measured by T-units per sentence (T/S), likely reflecting increased standardization of disciplinary jargon (Pan & Yang, 2025). Notably, ecology's compound nominal complexity rose more sharply than that of linguistics, suggesting discipline-specific adaptation to constraints such as peer-review norms, editorial guidelines, and audience expectations. In sum, these diachronic syntactic developments illustrate how each discipline negotiates the balance between innovation and convention. Ecology leans toward syntactic economy through lexical compression, mirroring broader STEM discourse norms, while linguistics preserves its interpretive ethos through hypotactic elaboration (Pan & Yang, 2024; Yang & Pan, 2024). Despite converging pressures toward clarity and efficiency, each field sustains a resilient syntactic identity, modulated by its communicative goals and epistemic cultures. Further longitudinal studies over extended temporal ranges and across additional disciplines would offer deeper insights into whether these patterns represent transient stylistic fluctuations or sustained disciplinary evolution. Implications These findings hold significant implications for academic writing instruction, disciplinary discourse analysis, and scholarly communication practices. Genre-based pedagogy should reflect the syntactic expectations of specific fields. Linguistics students may benefit from instruction in structuring hypotactic and recursive constructions to enhance conceptual clarity, while ecology students might focus on mastering compact noun phrase constructions that increase terminological precision and reduce syntactic load (Swales, 2018). For journal editors and publishers, awareness of discipline-specific syntactic conventions could inform editorial policy and peer review criteria. For example, ecology journals may reconsider stringent word limits that discourage necessary syntactic elaboration, while linguistics journals might promote clearer lexical cohesion to aid interdisciplinary comprehension (Hyland & Jiang, 2021). More broadly, these trends reflect the pressures of digital-era communication. Ecology’s decreasing sentence length and rising noun density reflect adaptation to fast-reading audiences, whereas linguistics’ syntactic elaboration may limit accessibility. Striking a balance between rigor and readability is essential, particularly in interdisciplinary and international publishing contexts (Biber & Gray, 2016). Conclusion Major Findings This study examined the manifestation of syntactic complexity across disciplines and over time through the application of a dual-level analytical framework to a corpus of academic abstracts. By integrating cross-disciplinary comparison with diachronic analysis, the study aimed to explore how different syntactic configurations are mobilized as functional resources for disciplinary knowledge construction. The analysis revealed systematic variations in the preferred syntactic strategies of the two disciplines. Abstracts in linguistics were characterized by a greater reliance on clausal elaboration, with hypotactic structures functioning as key resources for analytical exposition. Such structures facilitate the elaboration of arguments and the integration of prior scholarship, all of which are central to the interpretive and theory-driven orientation of the humanities. In this sense, clausal complexity in linguistics abstracts serves clear discourse-functional purposes rather than reflecting stylistic variation alone. In contrast, ecology abstracts predominantly employed strategies of nominal compression. Through extensive noun modification and compact nominal constructions, writers were able to achieve high informational density and referential efficiency. These syntactic choices are consistent with the communicative demands of STEM discourse, where precision and efficient representation of complex phenomena are prioritized. Thus, nominal complexity functions as a primary means of encoding empirical relationships within constrained textual space. From a diachronic perspective, the two disciplines exhibited distinct developmental tendencies. Ecology abstracts showed an increasing reliance on nominal-based complexity over time, suggesting a growing need for syntactically compressed structures to represent increasingly specialized and multi-dimensional research processes. Linguistics abstracts, by contrast, demonstrated a gradual shift toward greater clausal elaboration. This pattern indicates an expanding use of syntactic resources to support more nuanced analytical framing, potentially reflecting the field’s methodological diversification and engagement with interdisciplinary perspectives. Taken together, these findings highlight both the stability and the adaptability of disciplinary writing conventions. The results reaffirm the long-observed distinction between nominally oriented complexity in the hard sciences and clausal complexity in the soft sciences, and also demonstrate that these patterns are subject to gradual modification over time. By tracing how specific syntactic strategies are differentially deployed and developed across disciplines, this study contributes to a more functionally grounded understanding of syntactic complexity as a dynamic component of academic discourse Limitations and Future Research Directions While this study offers valuable insights into disciplinary syntactic variation, several limitations constrain its broader applicability. First, the corpus is limited to high-impact journals in ecology and linguistics. As a result, it may not fully capture stylistic diversity across other academic discourse, where different editorial pressures or linguistic norms may apply. Expanding the dataset to include multilingual and less mainstream sources could provide a more nuanced understanding of global academic writing practices. Second, the diachronic span (2019–2024) offers only a short-term analysis of evolving syntactic patterns. This temporal scope may miss slower, cyclical, or generational shifts in academic style, particularly in fields experiencing rapid methodological change, such as computational linguistics, bioinformatics, or climate modeling. Future studies could benefit from longitudinal corpora spanning decades to detect sustained syntactic trends and their alignment with epistemological transformations. Third, the present analysis focuses exclusively on quantifiable syntactic indices, as measured by tools like L2SCA and TAASSC. While these metrics are reliable and replicable, they offer a limited view of functional discourse. Crucial rhetorical dimensions remain underexplored. A richer picture of disciplinary discourse could be obtained by incorporating qualitative approaches, such as rhetorical move analysis, corpus-assisted discourse studies, or interviews with authors and editors. Looking forward, future research could explore interdisciplinary domains, such as ecolinguistics, cognitive semiotics, or science communication, to examine whether syntactic hybridity is emerging in response to cross-field dialogue. Additionally, investigating how syntactic preferences interact with author identity (e.g., L1 background, career stage, gender) or with technological factors (e.g., AI-assisted writing tools) would offer new dimensions to academic discourse analysis. Ultimately, more integrative, cross-disciplinary, and multimodal research agendas may help uncover the deeper cognitive, social, and institutional forces that shape how knowledge is syntactically constructed and communicated. Declarations Acknowledgements We would like to express our sincere gratitude to the editors and anonymous reviewers for their constructive comments and suggestions. 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Nature Ecology & Evolution, 5 (11), 1490–1498. Zhou, X., Gao, Y., & Lu, X. (2023). Lexical complexity changes in 100 years’ academic writing: Evidence from Nature biology letters. Journal of English for Academic Purposes, 64 , Article 101262. Additional Declarations No competing interests reported. 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-8803809","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592870984,"identity":"84923bda-301b-421a-997f-180c04fe1325","order_by":0,"name":"Hua Lu","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Lu","suffix":""},{"id":592870985,"identity":"4435fb61-9fdf-4735-af9b-2cb9fe779789","order_by":1,"name":"Zhi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIie3RMQrCMBSA4RcCzRL3iEOuYCcX0aukdPAG4uAQKeQMgtUzOAlurxScxBN0MIuTgqPgYlpwTkbB/FsgH4/kAcRiv5ggK1QLpEy7Aw8kGtUZKcdwAoDEIIQTuSk02l3DuFDk+jAgRz5CykpjdrhRR2i6NZAetYdQkbWkplOhkkHPgBqihyQdKet2CnsHEd4R3ZGEBhHREnVyb+G26JcXke59RK5n1r6WTc5ZXj3v87H0TvmWu8/T0K0ptEn41VgsFvu7PlnjQeTSYkH6AAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-06 07:23:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8803809/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8803809/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103098749,"identity":"9544767b-68dd-4d9f-878f-03a028aac876","added_by":"auto","created_at":"2026-02-20 19:15:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadar plot of normalized syntactic profiles in linguistics and ecology abstracts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Note: Metrics are min–max scaled across the two disciplines to visualize relative differences in structural strategies rather than absolute metric magnitudes. Raw mean values are reported in Tables 5–6.)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8803809/v1/244ac3b1261f67758991c180.jpeg"},{"id":103098750,"identity":"e8b59ccb-61e0-47ab-9b55-0b6387fcac13","added_by":"auto","created_at":"2026-02-20 19:16:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisciplinary distributions of selected syntactic complexity indices in ecology and linguistics abstracts\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8803809/v1/7d01d34d46fe3d4f4bf1aa12.jpeg"},{"id":103098752,"identity":"c0794729-3221-4dce-bb65-a14ad2083dfa","added_by":"auto","created_at":"2026-02-20 19:16:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiachronic trends of selected large-grained syntactic complexity indices in ecology and linguistics abstracts (2019–2024)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8803809/v1/7340132e4dc1819b31a01bf3.jpeg"},{"id":103504778,"identity":"035eef30-ba63-4516-8fb2-dededc6874cf","added_by":"auto","created_at":"2026-02-26 13:21:22","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiachronic trends of selected fine-grained nominal complexity indices in ecology and linguistics abstracts (2019–2024)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8803809/v1/9879abcb1a7c5890523137bf.jpeg"},{"id":105564666,"identity":"11fb0fb1-6c06-4484-87db-34b41e9480b5","added_by":"auto","created_at":"2026-03-27 12:50:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2003080,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8803809/v1/242c2862-681c-4b9e-9357-d0a4ee0135d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Diachronic Study of Syntactic Complexity in Academic Journal Abstracts: Disciplinary Variations Between Ecology and Linguistics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAn abstract serves as a concise summary of the content of a research article, playing a pivotal role in academic writing. It functions as a brief yet comprehensive tool that enables researchers to present their ongoing work while assisting readers in quickly assessing the relevance and quality of a paper, particularly in the context of disseminating academic findings (Swales \u0026amp; Feak, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Early studies on academic journal abstracts primarily focused on their rhetorical structure, examining the use of moves and steps within the abstract (Khedri et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tank\u0026oacute;, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These studies also extended to cross-disciplinary and cross-linguistic comparisons, exploring how rhetorical structures vary across different fields and languages (Khedri et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More recently, a smaller body of research has emerged focusing on the syntactic complexity of academic journal abstracts (Liu \u0026amp; Li, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), particularly in the context of the increasing internationalization of academic journals and the standardization of academic writing conventions, which have led to greater uniformity in the norms and stylistic features of abstracts. Despite these standardizing trends, scholars across disciplines continue to tailor their abstracts to reflect the unique characteristics of their fields. Academic writing varies greatly in style depending on the disciplines, especially in the natural sciences and humanities (Hyland, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). According to Hyland (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), writing in the natural sciences tends to prioritize objectivity and accuracy, whereas writing in the humanities may place greater emphasis on argumentation and critical thinking. More recent research on disciplinary variation has increasingly utilized syntactic complexity to illustrate disciplinary differences in academic writing (Lu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), especially within the abstracts (Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By extension, the syntactic complexity of abstracts in the natural sciences and the humanities tends to exhibit very different writing characteristics. However, this contrast still remains underexplored.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, this study aims to investigate the syntactic complexity of academic journal abstracts in ecology and linguistics, focusing on both discipline variations and diachronic trend. Employing a corpus of 1,005 abstracts collected from \u003cem\u003eNature\u003c/em\u003e, this study utilized computational tools incorporating the Second Language Syntactic Complexity Analyzer (L2SCA) and the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC). It first examined whether syntactic complexity varies significantly between the two disciplines through large-grained and fine-grained indices, and then assessed the diachronic evolution of these syntactic features from 2019 to 2024. Through these analyses, this study seeks to deepen understanding of how disciplinary norms and globalization shape syntactic preferences, and provide pedagogically useful insights for academic writing instruction, thereby enhancing the efficiency of disseminating academic accomplishments.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003e\u003cstrong\u003ePrevious Studies on Academic Journal Abstracts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumerous studies have analyzed abstracts as an integral component of discourse, often grounding their analysis in genre analysis theory and systemic functional linguistics. Swales (1990) introduced the Create a Research Space (CARS) model to examine the introduction section of an academic paper through the functional lenses of discourse moves and steps. This move-based framework then informed subsequent studies on abstract structure. Regarding abstracts in applied linguistics, Santos (1996) examined 94 abstracts and proposed a five-move pattern: Situating the research, Presenting the research, Describing the methodology, Summarizing the results, and Discussing the research. Additional researchers have suggested alternative discourse step models for abstracts, as summarized in Table 1, which shows representative abstract move models proposed between 1985 and 1996.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Discourse Step Models for Abstracts\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"389\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 226px;\"\u003e\n \u003cp\u003eAuthor (Year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eMove Structure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGraetz (1985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePurpose\u0026ndash;Method\u0026ndash;Results\u0026ndash;Conclusion (PMRC)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSwales (1990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCreate A Research Space\u0026nbsp;(CARS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBhatia (1993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIntroduction\u0026ndash;Method\u0026ndash;Results\u0026ndash;Discussion (IMRD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSantos (1996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSituating the research\u0026ndash;Presenting the research\u0026ndash;Describing the methodology\u0026ndash;Summarizing the results\u0026ndash;Discussing the research\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\u003eThe discourse moves models proposed during this period are grounded in genre analysis theory and have become the primary reference for subsequent scholars. Macro-level studies have further examined the internal characteristics of the abstract move structures and their relationship to the rest of the paper (Samraj, 2005; Swales, 1993).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlongside studies of macro-level abstract structures, researchers have also examined the micro-linguistic features such as lexical, grammatical, and discourse chunking elements. Lexical aspects include the prevalence of pronouns, self-referential verbs and modal verbs, while grammatical aspects exam the distribution of tenses and inflections. For instance, Ebrahimi \u0026amp; Chan (2015) classified the discourse roles of research article abstracts by developing a corpus to investigate grammatical variations in academic writing. Recent research has primarily focused on metadiscourse analysis within abstracts, particularly the examination of interactive metadiscourse markers (Khedri\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2015), and how authors align arguments with readers, clarify abstract concepts, and enhance academic communication efficacy (Khedri\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2013). Another area of interest is the examination of stance markers in abstract discourse, with studies such as Alghazo\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e. (2021) investigating grammatical mechanisms and the semantic nuances of stance expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, there is a paucity of research regarding the trajectory of syntactic complexity in abstracts (Nasseri, 2021; Biber\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2016), which this study aims to address. Although syntactic complexity has been increasingly recognized as a salient indicator of linguistic and disciplinary sophistication, especially in L2 writing research, its role in shaping the abstract genre remains insufficiently examined, particularly in relation to disciplinary variation and diachronic change.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualizing and Measuring Syntactic Complexity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSyntactic complexity is commonly defined as the range and sophistication of grammatical resources deployed in language production (Ortega, 2015). In earlier studies, this construct was primarily conceptualized in terms of clausal subordination. Influenced by developmental patterns observed in spoken language, researchers frequently equated greater syntactic complexity with longer sentences and higher frequencies of embedded clauses (Norris \u0026amp; Ortega, 2009). Consequently, large-grained indices such as mean length of sentence (MLS) and clauses per sentence (C/S) became widely adopted measures, particularly through automated tools such as the L2 Syntactic Complexity Analyzer (L2SCA; Lu, 2010).\u003c/p\u003e\n\u003cp\u003eSubsequent research within the register-functional tradition has questioned the adequacy of this clause-centered approach for determinating academic prose (Biber\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2011; Biber \u0026amp; Gray, 2016). Drawing on large-scale corpus analyses, Biber and Gray (2016) demonstrated that academic writing is systematically different from daily conversation in its grammatical realization of complexity. Academic writing tends to achieve complexity through structural compression like dense noun phrases and phrasal modification rather than relying on simple clausal elaboration. From this perspective, although a text may consist of relatively short sentences with limited clausal embedding, it still can exhibit a high degree of syntactic complexity, which is referred to informational density.\u003c/p\u003e\n\u003cp\u003eEmpirical studies further support this distinction. Research has shown that while traditional clausal measures are sensitive to rhetorical patterns common in the soft sciences, they are less effective in capturing the complexity of registers that rely heavily on nominal and phrasal structures in the hard sciences (Lu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2021; Staples\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2016). Building on this, Kyle and Crossley (2018) proposed that fine-grained phrasal indices (e.g., complex nominals per clause) often have highly potential in reflecting writing quality and proficiency in academic contexts than holistic clausal measures. In light of these findings, this study adopts a multidimensional operationalization of syntactic complexity by combining L2SCA-based indices to capture clausal elaboration with indices from the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC; Kyle, 2016) to capture phrasal density. Together, these measures operationalize complementary dimensions of syntactic complexity that are particularly relevant to the abstract genre.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisciplinary Variation in Academic Writing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDisciplinary variation in academic writing reflects fundamental differences in writing conventions, rhetorical traditions, and communicative expectations. These distinctions are shaped by factors such as the nature of knowledge production, audience expectations, and genre conventions, and they can differ markedly across disciplines (Hyland, 2000). Understanding these variations is essential for both students and scholars who aim to navigate and master the diverse academic genres encountered in their respective fields.\u003c/p\u003e\n\u003cp\u003eSwales (1990) introduced a genre analysis framework for academic discourse, indicating that the organizational structures and communicative purposes of writing genres vary across disciplines. Complementing this, Bhatia (1993) proposed the concept of contextualized writing, asserting that academic writing must be understood within the specific disciplinary contexts in which it is produced. According to Bhatia (1993), academic writing cannot be fully grasped outside of the discourse communities that shape its conventions and expectations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDistinct strategies for argument construction and evidence presentation result in divergent linguistic patterns. Hyland (1998) noted that disciplines within the humanities and social sciences tend to rely more heavily on hedging strategies to express uncertainty and mitigate claims, while the natural sciences typically favor more assertive, direct language. This distinction highlights the varying nature of knowledge claims across disciplines: whereas the natural sciences often present findings as objective facts, the humanities and social sciences are more inclined to acknowledge the subjective and interpretive nature of knowledge production. Similarly, rhetorical strategies employed in academic writing also reflect disciplinary differences. Hyland (2000) suggests that fields such as engineering and medicine prioritize clarity and precision, often focusing on providing practical solutions or detailed methodologies. In contrast, disciplines like philosophy and literature value abstract reasoning and the exploration of multiple interpretations, leading to a more discursive and less linear style of writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorpus-based research has further illuminated these differences. Lu\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e. (2021) documented substantial variations in syntactic complexity between the social sciences and engineering, affirming that stylistic norms are discipline-sensitive. Researchers have leveraged large-scale corpora of academic texts to identify language patterns across different fields (Biber \u0026amp; Gray, 2016) and to explore the use of metadiscourse in academic writing across disciplines (Hyland, 2008). From an educational perspective, students who are familiar with the genre-specific conventions and disciplinary expectations tend to perform better in academic writing tasks (Hyland, 2000).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, examining disciplinary variation is vital for writing instruction, as it aids students in better understanding the complex dynamics of academic writing. This study focuses on the contrast between linguistics, as part of the humanities, and ecology, within the natural sciences, in order to address the gap in research regarding disciplinary differences in the syntactic complexity of academic journal abstracts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiachronic Research in Academic Writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiachronic research in academic writing has increasingly been deployed as an important means of examining the evolution of disciplinary writing practices and communicative norms. This type of research features academic writing as a dynamic system that evolves in response to broader changes in disciplinary professionalization and the globalization of scholar communication rather than a stable ad homogeneous phenomenon (Wang \u003cem\u003eet al.\u003c/em\u003e, 2023). From this perspective, diachronic analyses of academic texts frequently trace how linguistic features such as syntactic and lexical complexity become routinized, redistributed, or refunctionalized within specific period (Yang \u0026amp; Pan, 2024; Zhou\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2025). Such analyses contribute not only to a more nuanced understanding of the historical development of academic genres (Swales, 1990; Hyland, 2004), but also to pedagogical discussions in English for Specific Purposes (ESP), particularly in contexts characterized by increasing information density and publication pressure (Lillis \u0026amp; Curry, 2010).\u003c/p\u003e\n\u003cp\u003eBuilding on this, a growing body of empirical research has investigated diachronic changes in linguistic complexity across disciplines and genres. For example, Wang \u003cem\u003eet al.\u003c/em\u003e (2023) proposed that early scientific writing has undergone substantial restructuring over time. They documented a prominent increase in lexical and morphological complexity and a decline in syntactic complexity in scientific texts between 1821 and 1920, which suggests a redistribution of complexity across linguistic domains. Focusing on more recent decades, several studies show differentiated diachronic trajectories for clausal and phrasal complexity. Comparative analyses conducted by Yang and Pan (2024) and Pan and Yang (2024) across disciplines such as Medicine and Mechanical Engineering indicate a general increase in phrasal complexity while clausal complexity has remained relatively stable or shown a downward trend. Evidence from lexical analyses from Pan and Yang (2025) and Zhou \u003cem\u003eet al.\u003c/em\u003e (2023) further supports this pattern. They found consistent increases in lexical richness in high-impact journals such as Nature, revealing a broader tendency toward informational compression and specialization in contemporary academic context.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these advances, current diachronic research in academic writing has extensively examined broad disciplinary contrasts and long time spans, with comparatively limited attention to fine-grained variation in academic journal abstracts during recent periods. To address this gap, this study aims to focus on both large-grained and fine-grained diachronic variation of abstracts, particularly in relation to the most recent periods (e.g., 2019\u0026ndash;2024) characterized by intensified digital dissemination and publication competition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn light of the research gaps discussed above, this study provides an in-depth analysis of disciplinary variation in syntactic complexity of academic journal abstracts and their changes over time, using ecology and linguistics academic journal abstracts as examples. The specific research questions designed for this study are as follows:\u003c/p\u003e\n\u003cp\u003e(1) What features do the syntactic complexity of academic journal abstracts in ecology and linguistics exhibit?\u003c/p\u003e\n\u003cp\u003e(2) Are there variations in the syntactic complexity of academic journal abstracts between the disciplines of ecology and linguistics? If yes, what are the distinctions?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(3) Has there been any alteration in the syntactic complexity of academic journal abstracts over the past decade? If yes, what are the prevailing trends?\u003c/p\u003e"},{"header":"Research Methodology","content":"\u003cp\u003e\u003cstrong\u003eCorpus Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research corpus was curated from Nature and its sub-journals to ensure editorial consistency and disciplinary representativeness. Ecology and Linguistics were selected to represent two distinct academic traditions. The former exemplifies the natural science disciplines, which emphasis on empirical precision and brevity, while the latter reflects the humanities discipline, focusing on theoretical argumentation and conceptual definition (Hyland, 2000). These two disciplines are rarely compared in previous studies (Nesi \u0026amp; Gardner, 2012).\u003c/p\u003e\n\u003cp\u003eA total of 536 ecology abstracts and 469 linguistics abstracts were collected. This study specifically targeted the year 2019 and 2024 based on the data availability and trend relevance. Preliminary searches indicated that the volume of relevant research articles in the target journals was limited before 2018, which means that they cannot provide a representative corpus. However, publication output experienced a significant and stable increase since 2019, reaching its peak in 2024. Therefore, 2019 and 2024 were selected as critical time points to capture the most recent shifts in academic writing practices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of abstracts across disciplines and years is summarized in Table 2. Ecology abstracts were predominantly sourced from \u003cem\u003eNature Ecology \u0026amp; Evolution\u003c/em\u003e, while linguistics abstracts were drawn from \u003cem\u003eNature Human Behaviour\u003c/em\u003e, reflecting the journals\u0026rsquo; alignment with their respective disciplinary (Hyland, 2005). This design enables a balanced comparison of syntactic complexity across disciplines and time periods, addressing the dual forces of globalization and disciplinary tradition (Hyland, 2000; Canagarajah, 2002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Corpus Composition by Discipline and Year\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiscipline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWords\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98,726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLinguistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95,489\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\u003eAs shown in Table 2, the sample sizes for the sub-corpora (e.g., Linguistics-2019 vs. Linguistics-2024) are not perfectly equal. This discrepancy reflects the objective reality of publication volumes in the selected journals across different years and disciplines. To ensure the authenticity and representativeness of the dataset, this study included all eligible empirical abstracts within the timeframe rather than artificially reducing the sample size to achieve numerical balance, which might compromise the statistical reliability of the corpus. The selection criteria strictly prioritized empirical research articles to minimize rhetorical variability, excluding reviews, editorials, and commentaries (Swales \u0026amp; Feak, 2009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSyntactic Complexity Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSyntactic complexity was operationalized using 18 indices derived from two computational tools: the L2 Syntactic Complexity Analyzer (L2SCA; Lu, 2010) and the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC; Kyle \u0026amp; Crossley, 2018). These tools were selected for their complementary strengths in quantifying global, clausal, and phrasal complexity. From L2SCA, key metrics included mean sentence length (MLS), reflecting overall textual density; subordinate clauses per sentence (C/S), indicative of hierarchical syntactic structures; and complex nominals per clause (CN/C), capturing noun phrase elaboration (Lu, 2010). TAASSC complemented the clause- and sentence-level measures by providing a range of dependency-based, fine-grained syntactic indices that capture structural variation within nominal phrases. Metrics such as determiners per nominal (Det/N), adjectival modifiers per nominal (Adj/N), and nouns as nominal dependents (NN/N) quantify the density and complexity of noun phrase constructions, revealing how disciplines differ in referential clarity and descriptive elaboration. Additional features like possessives per nominal (Poss/N) and relative clause modifiers (RCmod/N) offer insight into the extent of conceptual embedding and syntactic expansion strategies employed in academic abstracts (Kyle \u0026amp; Crossley, 2018). All indices utilized in this study are illustrated in table 3 and table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 L2SCA Syntactic Complexity Indices and Descriptions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eFull Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eMLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMean Length of Sentence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of words per sentence, indicating overall textual density.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eMLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMean Length of T-unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of words per T-unit, reflecting the expansion of independent clauses.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eMLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMean Length of Clause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of words per clause, indicating elaboration within clauses.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eT/S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eT-units per Sentence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of T-units per sentence, revealing syntactic density.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC/S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eClauses per Sentence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of clauses per sentence, showing clause complexity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eClauses per T-unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of clauses per T-unit, measuring structural expansion.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eDependent Clauses per Clause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of dependent clauses per clause, reflecting syntactic subordination.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eDependent Clauses per T-unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of dependent clauses per T-unit, showing embedding density.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCT/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eComplex T-units per T-unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eProportion of T-units that are complex, reflecting structural sophistication.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCN/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eComplex Nominals per T-unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of complex nominals per T-unit, indicating nominal elaboration.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCN/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eComplex Nominals per Clause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of complex nominals per clause, reflecting dense noun structures.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCP/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eCoordinate Phrases per T-unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of coordinate phrases per T-unit, showing syntactic extension.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCP/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eCoordinate Phrases per Clause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of coordinate phrases per clause.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eVP/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eVerb Phrases per T-unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of verb phrases per T-unit, reflecting verbal complexity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Selected TAASSC Metrics and Descriptions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eFull Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eDep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDependents per Nominal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eAverage number of dependents per nominal (excluding pronouns), indicating syntactic density.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eDet/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDeterminers per Nominal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eProportion of determiners modifying nominals, reflecting definiteness and referential clarity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAdj/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAdjectival Modifiers per Nominal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eProportion of adjectives used to modify nominals, signaling descriptive elaboration.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003ePrep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003ePrepositions per Nominal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eProportion of prepositional modifiers per nominal, reflecting complex relational structures.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;Poss/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003ePossessives per Nominal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eFrequency of possessive constructions modifying nominals, indicating possession relationships.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eVmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eVerbal Modifiers per Nominal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eProportion of verb-based modifiers (e.g., participles) per nominal, reflecting embedded verbal structures.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eNN/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eNouns as Nominal Dependents\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eFrequency of nouns modifying other nouns, indicating compound or dense noun phrase structures.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;RCmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eRelative Clause Modifiers per Nominal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003eProportion of relative clauses used to modify nominals, showing syntactic complexity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the robustness of the quantitative analysis, all syntactic complexity indices generated by L2SCA and TAASSC were imported into SPSS 27.0 for statistical processing. Prior to inferential analysis, the data underwent a systematic screening procedure. Outliers were identified using Z-scores, with values exceeding\u0026nbsp;\u0026plusmn;3.0 excluded to reduce the influence of extreme observations on the distribution of the data (Field, 2018).\u003c/p\u003e\n\u003cp\u003eSubsequently, assumptions underlying parametric analyses were examined. Data normality was assessed via the Shapiro\u0026ndash;Wilk test, and homogeneity of variance across groups was evaluated using Levene\u0026rsquo;s test. Based on these diagnostics, independent-samples t-tests were conducted to examine differences in syntactic complexity indices across disciplines (Ecology vs. Linguistics) and across time periods (2019 vs. 2024). In cases where the assumption of equal variances was not met, Welch-adjusted t-tests were applied.\u003c/p\u003e\n\u003cp\u003eIn addition to null-hypothesis significance testing (set at p \u0026lt; .05), the magnitude of the observed differences was quantified by calculating Cohen\u0026rsquo;s d effect sizes (Cohen, 1988). This combination of significance testing and effect size estimation follows established methodological recommendations in applied linguistics research and facilitates a more informative interpretation of disciplinary and diachronic variation in syntactic complexity (Biber\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2011; Larson-Hall, 2016).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section reports the results of the syntactic complexity analysis of academic journal abstracts in ecology and linguistics. The results are organized in accordance with the three research questions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall Syntactic Features of Academic Abstracts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the first research question regarding the overall features of syntactic complexity in ecology and linguistics, descriptive statistics were calculated for the entire dataset. As shown in Figure 1, although linguistics and ecology abstracts exhibit broadly comparable overall syntactic density, they diverge in how complexity is structurally realized, particularly in fine-grained nominal modification. Table 5 and Table 6 present the large-grained and fine-grained profiles of both disciplines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Large-Grained Syntactic Complexity by Discipline (All Abstracts)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"303\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eLinguistics-All\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eEcology-All\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eMLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e25.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eMLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e23.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eMLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e14.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eC/S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eT/S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCT/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCN/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCN/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCP/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCP/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eVP/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2.2\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\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 Fine-Grained Syntactic Complexity by Discipline (All Abstracts)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"319\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eLinguistics-All\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eEcology-All\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eDep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eDet/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eAdj/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePrep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ePoss/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eVmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eNN/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eRCmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.026\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\u003eTable 5 presents the large-grained syntactic complexity indices. Overall, the two disciplines display comparable levels of global syntactic complexity. Mean Length of Sentence (MLS) is similar across disciplines, with linguistics abstracts averaging 25.89 words per sentence and ecology abstracts averaging 25.34 words. Comparable patterns are also observed for Mean Length of T-unit (MLT: 24.50 vs. 23.58) and Mean Length of Clause (MLC: 15.93 vs. 14.88).\u003c/p\u003e\n\u003cp\u003eMeasures related to clausal organization show only minor differences. Clauses per sentence (C/S) and T-units per sentence (T/S) are nearly identical across the two disciplines. Similarly, indices of clausal subordination, including dependent clauses per clause (DC/C) and dependent clauses per T-unit (DC/T), remain comparable in both corpora. Complex nominal density is relatively high in both disciplines. Ecology abstracts show slightly higher values for complex nominals per T-unit (CN/T = 3.95), whereas linguistics abstracts demonstrate marginally higher values for verb phrases per T-unit (VP/T = 2.28).\u003c/p\u003e\n\u003cp\u003eTable 6 reports the fine-grained syntactic complexity indices. Both disciplines exhibit identical values for dependents per nominal (Dep/N = 1.57) and relative clause modifiers per nominal (RCmod/N = 0.026). Differences are observed in the distribution of nominal modifiers. Linguistics abstracts show higher values for determiners per nominal (Det/N = 0.32) and possessives per nominal (Poss/N = 0.046), while ecology abstracts demonstrate higher levels of adjectival modification (Adj/N = 0.46) and noun\u0026ndash;noun modification (NN/N = 0.24). Other indices, including prepositional modifiers (Prep/N) and verbal modifiers (Vmod/N), remain similar across the two disciplines.\u003c/p\u003e\n\u003cp\u003eTaken together, the results in Tables 5 and 6 indicate that ecology and linguistics abstracts share broadly similar overall syntactic density, while differences emerge primarily in the distribution of fine-grained nominal modification strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisciplinary Variation in Syntactic Complexity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address RQ2, independent-samples t-tests were conducted to examine whether statistically significant differences exist between ecology and linguistics abstracts. Figure 2 illustrates the distributional differences in selected large-grained and fine-grained syntactic complexity indices between ecology and linguistics abstracts. The results of the large-grained and fine-grained comparisons are presented in Tables 7 and 8, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 Large-Grained Syntactic Complexity Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eF (Levene)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSig. (Levene)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eMLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.5205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.8131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.4983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eMLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.4073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eMLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.7531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eC_S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.7576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eVP_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.3821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.0504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eC_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.2361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eDC_C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.3355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eDC_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.1418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eT_S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-1.2094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCT_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.9526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.2836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.8026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCP_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.7415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.7503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCP_C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.5055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.5164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.4327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCN_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.3202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCN_C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.6234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.2037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.8512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8 Fine-Grained Syntactic Complexity Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eF (Levene)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSig. (Levene)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eDep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.1212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eDet/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e10.0892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAdj/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.9508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-8.2868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePrep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.6002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.4698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.6726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePoss/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.5253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.9617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eVmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.1304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-1.1352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNN/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.6286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-3.7348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eRCmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.6312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.6411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.5801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs for Table 7, in the large-grained comparison, which includes sentence- and clause-level measures, no statistically significant differences were observed between the two disciplines (p \u0026gt; .05). However, descriptive statistics indicate that linguistics abstracts generally yielded higher scores in Mean Length of Sentence (MLS) and Verb Phrases per T-unit (VP/T). A more nuanced pattern emerged from the fine-grained syntactic metrics analyzed via TAASSC. Linguistics abstracts displayed significantly higher use of determiners (Det/N: p = 0.0018) and possessives (Poss/N: p = 0.0375). Conversely, ecology abstracts exhibited a significantly higher frequency of adjectival modification (Adj/N, p = 0.0137). As visualized in Figure 2, the violin plot for Adj/N in ecology displays a wider density distribution positioned at a higher value range compared to linguistics. This shape indicates that the reliance on adjectival modification is not merely an artifact of outliers, but a consistent disciplinary norm adhered to by the majority of ecology abstracts. In contrast, the linguistics distribution is concentrated at lower values, further confirming that dense pre-modification is less characteristic of the field. No significant differences were found in the density of nominal dependents (Dep/N), prepositions (Prep/N), or verbal structures (Vmod/N).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiachronic Changes in Syntactic Complexity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine temporal shifts in syntactic complexity, a diachronic analysis was conducted by comparing academic abstracts from two time points: 2019 and 2024. Both large-grained and fine-grained syntactic indices were assessed across the ecology and linguistics subcorpora to identify evolving stylistic patterns. To further illustrate the diachronic patterns identified in Tables 9 and 10, Figures 3 and 4 visualize the changes in selected large-grained and fine-grained syntactic complexity indices between 2019 and 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9 Large-Grained Syntactic Complexity by Subcorpus\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eLinguistics-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eLinguistics-2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eEcology-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eEcology-2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e28.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e24.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e25.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e25.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e26.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e23.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e23.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e23.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e15.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e16.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e15.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eC/S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eT/S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCT/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCN/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCN/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCP/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCP/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVP/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10 Fine-Grained Syntactic Complexity by Subcorpus\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eLinguistics-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eLinguistics-2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eEcology-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eEcology-2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDet/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAdj/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePrep/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePoss/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eNN/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eRCmod/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs shown in Table 9, ecology abstracts display relatively stable sentence-level complexity across the two time points. Mean Length of Sentence (MLS) shows a slight decrease from 25.95 in 2019 to 25.56 in 2024, while measures of clausal density such as clauses per sentence (C/S) and clauses per T-unit (C/T) remain largely unchanged. Indices of subordination, including DC/C and DC/T, also show minimal variation. At the phrasal level, modest increases are observed in nominal complexity. Complex nominals per clause (CN/C) increase from 2.54 to 2.58, and noun\u0026ndash;noun modification (NN/N) rises slightly from 0.24 to 0.25. Adjectival modification (Adj/N) remains stable across the two time points, while prepositional modifiers (Prep/N) show a slight increase.\u003c/p\u003e\n\u003cp\u003eTable 9 also shows that linguistics abstracts exhibit noticeable changes in several large-grained indices over time. Mean Length of Sentence (MLS) decreases from 28.91 in 2019 to 24.97 in 2024. Measures of clausal density and subordination, including clauses per sentence (C/S), clauses per T-unit (C/T), and dependent clauses per T-unit (DC/T), also show declines across the two periods. Fine-grained indices reported in Table 10 reveal increases in several nominal and verbal modification measures. Verbal modifiers per nominal (Vmod/N) increase from 0.034 to 0.041, and noun\u0026ndash;noun modification (NN/N) rises from 0.15 to 0.21. Determiner use (Det/N) shows a slight decrease, while possessive constructions (Poss/N) increase from 0.038 to 0.047. Relative clause modifiers per nominal (RCmod/N) decrease over time.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eOverall Syntactic Profiles of Academic Abstracts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results show that although Ecology and Linguistics exhibit comparable levels of overall syntactic complexity, they differ significantly in the ways complexity is structurally realized at the phrasal level. These results are consistent with prior findings that humanities disciplines favor clausal elaboration for argumentative purposes, while natural sciences rely more on nominal constructions to convey dense information concisely (Hyland, 2005; Biber\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2016; Pan \u0026amp; Yang, 2024; Yang \u0026amp; Pan, 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore specifically, Linguistics abstracts display a substantially higher frequency of determiners and possessive constructions. This pattern can be interpreted as reflecting the discipline\u0026rsquo;s epistemological orientation toward referential precision. In humanities-oriented inquiry, abstract entities and theoretical constructs often require explicit specification and attribution (e.g.,\u0026nbsp;\u003cem\u003ethe speaker\u0026rsquo;s pragmatic intent\u003c/em\u003e) in order to support fine-grained conceptual argumentation. Ecology abstracts, by contrast, show a stronger preference for adjectival modification and noun\u0026ndash;noun compounding. Such structures align with scientific conventions of lexical density, whereby complex empirical phenomena are categorized and differentiated through pre-modification rather than through extended clausal elaboration (Pan \u0026amp; Yang, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 1\u003c/em\u003e Hunting can fundamentally alter wildlife population dynamics but the consequences of hunting on pathogen transmission and evolution remain poorly understood (Fountain-Jones\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 2\u003c/em\u003e\u0026nbsp; Using a Bayesian framework for modelling spoken word recognition, we find that computational \u0026nbsp; models can replicate adult interpretations of children\u0026rsquo;s speech only when they include strong, context-specific prior expectations about the messages that children will want to communicate (Meylan\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e\n\u003cp\u003eExample 1 balances coordination and subordination, presenting two coordinated clauses followed by a relative clause. The nominal construction \u0026ldquo;the consequences of hunting on pathogen transmission and evolution\u0026rdquo; exemplifies moderate non phrase complexity. The syntactic structure supports an informative yet concise style typical of ecological abstracts. In contrast, Example 2 illustrates conceptual layering. This sentence uses a participial phrase \u0026ldquo;using a bayesian framework...\u0026rdquo; and the reporting verb \u0026ldquo;find that\u0026rdquo; to introduce a complex complement clause. And this is further elaborated by a conditional subordinate clause \u0026ldquo;only when...\u0026rdquo; and a nested relative clause \u0026ldquo;that children will want to communicate\u0026rdquo;. Such utilization of multiple levels of embedding indicates a preference for hypotactic structures in linguistics abstracts to preciesely qualify theoretical mechanisms. In summary, linguistic abstracts are syntactically more elaborate at the noun and clause levels, employing greater referential detail and subordination. Ecology abstracts, meanwhile, favor lexical precision and dense nominalization. These results underscore how disciplinary communicative norms shape syntactic complexity in academic abstracts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpreting Disciplinary Variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comparative analysis of syntactic complexity in academic journal abstracts from ecology and linguistics reveals significant disciplinary variations, shaped by distinct communicative norms and epistemic objectives. These differences are evident across both fine-grained grammatical structures and large-grained textual patterns, as outlined below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Large-Grained Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough large-grained indices such as sentence length indicate broad similarities across the two disciplines, closer inspection reveals systematic differences in how complex structures are deployed. Ecology abstracts make extensive use of nominal compounds to efficiently package technical information. Expressions such as \u0026ldquo;carbon-sequestration\u0026rdquo; potential condense entire processes into single conceptual units, reflecting the economy of expression valued in the natural sciences (Wang\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2023; Zhou\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 3\u003c/em\u003e\u0026nbsp; Atmospheric phosphorus supply exceeds demand along forest succession, whereas forests rely on soil stocks to meet their base cation demands (Bauters\u0026nbsp;\u003cem\u003eet al.,\u003c/em\u003e 2022).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 4\u003c/em\u003e\u0026nbsp; The translator\u0026rsquo;s individuation process is modeled to show how a translator mobilizes the meaning resources in the repertoire, which is constrained by the allocation of the cultural reservoir (Yu \u0026amp; Chang, 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExample 3\u0026rsquo;s two independent clauses are coordinated, but each contains embedded noun phrases (e.g., \u0026ldquo;Atmospheric phosphorus supply exceeds demand..., whereas forests rely on soil stocks...\u0026rdquo;) that extend its length. This structure, while readable, shows information-dense yet syntactically linear construction, typical of empirical disciplines. However, Example 4 illustrates greater clausal nesting. The post-modifying phrase \u0026ldquo;constrained by\u0026rdquo; illustrates heavy use of passive participial clauses modifying complex noun phrases, demonstrating syntactic elaboration common in linguistics (Pan \u0026amp; Yang, 2024).\u003c/p\u003e\n\u003cp\u003eAlthough statistical differences in clause-level subordination measures such as C/S and DC/C were not significant (p \u0026gt; 0.05), qualitative distinctions in clause usage were observed. Linguistics abstracts employed subordinate clauses primarily to articulate conceptual complexity and theoretical nuance, while ecology used them to contextualize empirical observations. These contrasting syntactic applications underscore Swales\u0026rsquo; (1990) notion that academic discourse is genre- and discipline- sensitive, adapting linguistic resources to fulfill divergent communicative purposes\u0026mdash;argumentative elaboration in linguistics and empirical description in ecology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Fine-Grained Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the micro-grammatical level, distinct syntactic preferences emerge between the disciplines of linguistics and ecology. Linguistics abstracts make more frequent use of determiners and possessive constructions, reinforcing referential precision and conceptual boundary marking. For example, phrases such as \u0026ldquo;the speaker\u0026rsquo;s pragmatic intent\u0026rdquo; illustrate how possessives articulate theoretical boundaries, a rhetorical hallmark of linguistic inquiry (Biber \u0026amp; Gray, 2016). This syntactic strategy supports the field\u0026rsquo;s broader orientation toward analytic elaboration and abstract theorization, often manifested through embedded and referentially rich structures (Ortega, 2015).\u003c/p\u003e\n\u003cp\u003eIn ecology, adjectival modification foregrounds measurable attributes and categorical distinctions essential to empirical description. This lexical tendency corresponds with the communicative goals of natural sciences, where the quantification and specification of phenomena are paramount (Hyland, 2005). While differences in prepositional phrases (Prep/N) and verbal modifiers (Vmod/N) were not statistically significant, ecology abstracts nonetheless exhibited a slightly higher incidence of prepositional structures, such as \u0026ldquo;under drought conditions,\u0026rdquo; pointing to the field\u0026rsquo;s need to spatially and temporally contextualize variables.\u003c/p\u003e\n\u003cp\u003eThese patterns align with Biber and Gray\u0026rsquo;s (2016) disciplinary framework, in which humanities disciplines like linguistics favor syntactic elaboration through clausal subordination and referential markers, while STEM fields such as ecology tend toward noun phrase density and information compression. The significantly higher Det/N ratio in linguistics (0.32 vs. 0.23 in ecology) underscores its prioritization of definitional precision and theoretical specificity. Conversely, ecology\u0026rsquo;s higher Adj/N ratio (0.46 vs. 0.40 in linguistics) reflects a lexical style attuned to the measurement and description of observable data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 5\u003c/em\u003e While the contribution of biodiversity to supporting multiple ecosystem functions is well established in natural ecosystems, the relationship of the above- and below-ground diversity with ecosystem multifunctionality remains virtually unknown in urban greenspaces (Fan\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2023).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 6\u003c/em\u003e People\u0026rsquo;s implicit gender associations are strongly predicted by gender associations encoded in the statistics of the language they speak (Lewis \u0026amp; Lupyan, 2020).\u003c/p\u003e\n\u003cp\u003eIn Example 5, the complex noun phrase \u0026ldquo;the relationship of the above- and below-ground diversity with ecosystem multifunctionality\u0026rdquo; and the subordinate clause \u0026ldquo;While the contribution... is well established...\u0026rdquo; show moderate non phrase elaboration and controlled use of subordination, balancing empirical clarity with brevity. On the contrary, Example 6 highlights embedded referential structures. The use of noun phrase \u0026ldquo;gender associations encoded in the statistics of the language they speak\u0026rdquo; reflects fine-grained nominal elaboration. This supports theoretical exposition by clearly specifying referents and semantic nuance.\u003c/p\u003e\n\u003cp\u003eIn sum, syntactic complexity is not distributed uniformly across disciplines but is closely tied to their respective epistemologies and rhetorical traditions. Linguistics favors referential elaboration and theoretical clarity, whereas ecology emphasizes descriptive economy and nominal precision\u0026mdash;distinct yet equally strategic modes of scholarly communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExplaining Diachronic Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEcology abstracts show an increasing reliance on nominal complexity, particularly through compound noun phrases and adjectival modification. These patterns suggest a diachronic trend toward syntactic compression and technical density (Yang \u0026amp; Pan, 2024; Zhou\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2023.)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 7\u003c/em\u003e\u0026nbsp; We find that structural overshoot contributed to around 11% of drought events during 1981\u0026ndash;2015 and is often associated with compound extreme drought and heat, causing faster vegetation declines and greater drought impacts compared to non-overshoot related droughts (Zhang\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2021).\u003c/p\u003e\n\u003cp\u003eThe compact construction \u0026ldquo;structural overshoot\u0026rdquo; and the causative gerund clause \u0026ldquo;causing...\u0026rdquo; show how newer ecological abstracts embed causal relationships in noun phrase-heavy constructions. This syntactic compression facilitates high-density scientific reporting.\u003c/p\u003e\n\u003cp\u003eThis trend also reflects a broader disciplinary shift toward information density and communicative efficiency. Rather than expanding syntactic structures through subordination, ecology abstracts increasingly opt for nominal groups that compress entire conceptual relations (e.g., \u0026ldquo;structural overshoot\u0026rdquo;) into single constituents. This reflects the discipline\u0026rsquo;s need to quickly convey results and implications within strict space limitations, particularly in high-impact journals like Nature. Such constructions enable dense lexical packing, which supports ecological writing\u0026rsquo;s emphasis on empirical exactitude and observational generalizability.\u003c/p\u003e\n\u003cp\u003eAdditionally, the rise of environmental modeling and data-intensive research in ecology coincides with increased use of compound noun phrases, participial constructions, and technical modifiers. These stylistic features parallel the growing epistemological emphasis on systems thinking, algorithmic interpretation, and network-based ecological modeling, all of which favor syntactic compaction over interpretive elaboration (Wang\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 8\u003c/em\u003e\u0026nbsp; Here we provide evidence for a novel psychological pathway for the expression of such biases, in which they arise as a consequence of the automatized mechanisms by which humans retrieve words to produce sentences (Brough\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2024).\u003c/p\u003e\n\u003cp\u003eLinguistics abstracts exhibit stable or slightly increasing levels of clausal subordination and verbal complexity. Example 8 showcases a central noun (\u0026ldquo;novel psychological pathway\u0026rdquo;) post-modified by a relative clause (\u0026ldquo;in which they arise as a consequence...\u0026rdquo;), adding specificity and hierarchical syntactic layering, which is a characteristic feature that appears to increase in recent years. These increases in subordinate clause use and verbal modification may be tied to the discipline\u0026rsquo;s ongoing concern with interpretive nuance, methodological reflexivity, and metadiscursive framing. For example, the frequent use of relative clauses not only adds syntactic complexity but also serves a clarificatory and argumentative function in establishing fine-grained conceptual relationships. This is evident in constructions such as \u0026ldquo;mechanisms by which humans retrieve words to produce sentences\u0026rdquo;, where the clause delineates scope and intent with rhetorical precision.\u003c/p\u003e\n\u003cp\u003eMoreover, the enduring reliance on hypotactic structures in linguistics may reflect broader epistemological preferences for layered explanation, definitional precision, and theoretical specification. In contemporary discourse-oriented studies, this trend is reinforced by critical approaches that emphasize multi-level interpretation and ideological positioning, which are often syntactically realized through elaborate embedding, hedging, and thematic fronting. These features underscore the discipline\u0026rsquo;s continuing prioritization of analytical depth and theoretical clarity in scholarly communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-Disciplinary Comparison of Diachronic Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCross-disciplinary comparisons highlight diverging responses to evolving academic communication pressures. Ecology increasingly adopts compact, noun-heavy constructions, indicating a drift toward lexical condensation. Linguistics maintains hypotactic syntactic structures, emphasizing analytical clarity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 9\u003c/em\u003e\u0026nbsp; Household income was positively associated with pollinator abundance in gardens, highlighting the influence of socioeconomic factors (Baldock\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2019).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample 10\u0026nbsp;\u003c/em\u003ePsycholinguistic manipulations pinpoint that the bias is caused by greater accessibility in memory of words that refer to in-group than out-group members (Brough\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2024).\u003c/p\u003e\n\u003cp\u003eIn Example 9, the participial phrase \u0026ldquo;highlighting the influence of socioeconomic factors\u0026rdquo; acts as a modifier, compressing evaluative criteria into a nominal adjunct. This structure exemplifies the trend toward nominalization and evaluative succinctness in contemporary ecological writing. By contrast, in Example 10, the structure includes both abstract noun phrases with heavy post-modification (\u0026ldquo;accessibility in memory of words\u0026rdquo;) and a defining relative clause (\u0026ldquo;that refer to in-group than out-group members\u0026rdquo;). These contrasting trajectories reflect different disciplinary responses to globalization, standardization, and digital publishing environments. While both fields adapt to evolving expectations, they preserve core stylistic identities, underscoring the resilience of disciplinary writing conventions in the face of broader communicative change.\u003c/p\u003e\n\u003cp\u003eQuantitative comparisons between the 2019 and 2024 subcorpora further underscore these stylistic bifurcations. Although many metrics remained statistically stable, notable diachronic shifts emerged. In ecology, compound nominal density (CN/T) increased from 3.62 to 3.98, dependents per noun (Dep/N) and adjectival modifiers (Adj/N) both rose, while prepositional phrase use (Prep/N) declined from 0.321 to 0.298. These changes reflect a strategic syntactic shift from analytic to synthetic structures, wherein phrases such as \u0026ldquo;mortality caused by climate change\u0026rdquo; are increasingly replaced by \u0026ldquo;climate-change-induced mortality\u0026rdquo;\u0026mdash;a compressed form that satisfies both disciplinary precision and editorial economy (Biber \u0026amp; Gray, 2016; Pan \u0026amp; Yang, 2024; Wang\u0026nbsp;\u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e\n\u003cp\u003eConversely, linguistics abstracts exhibit signs of expanded clausal development. Relative clause frequency (RCmod/N) doubled from 0.004 to 0.008, and verbal modifiers (Vmod/N) rose from 0.035 to 0.047. These increases signal a growing reliance on clausal subordination to accommodate disciplinary pluralism and interdisciplinary integration, especially in areas intersecting with computational linguistics, sociolinguistics, and discourse analysis. Sentences like \u0026ldquo;the model, which incorporates diachronic corpus data...\u0026rdquo; exemplify syntactic elaboration as a strategy for conceptual accommodation and epistemic transparency.\u003c/p\u003e\n\u003cp\u003eMacro-level structural features further differentiate the disciplines. Ecology abstracts show a modest decline in mean sentence length (MLS: from 26.2 to 24.8 words) while maintaining stable clause density (C/S), reinforcing their shift toward nominal compaction. Linguistics abstracts, in contrast, maintain long sentence structures (MLS: from 27.9 to 28.7 words) and show increased verb phrase density (VP/T: from 2.32 to 2.48), consistent with an enduring preference for multi-clausal exposition.\u003c/p\u003e\n\u003cp\u003eBoth disciplines demonstrate slight reductions in lexical diversity, measured by T-units per sentence (T/S), likely reflecting increased standardization of disciplinary jargon (Pan \u0026amp; Yang, 2025). Notably, ecology\u0026apos;s compound nominal complexity rose more sharply than that of linguistics, suggesting discipline-specific adaptation to constraints such as peer-review norms, editorial guidelines, and audience expectations.\u003c/p\u003e\n\u003cp\u003eIn sum, these diachronic syntactic developments illustrate how each discipline negotiates the balance between innovation and convention. Ecology leans toward syntactic economy through lexical compression, mirroring broader STEM discourse norms, while linguistics preserves its interpretive ethos through hypotactic elaboration (Pan \u0026amp; Yang, 2024; Yang \u0026amp; Pan, 2024). Despite converging pressures toward clarity and efficiency, each field sustains a resilient syntactic identity, modulated by its communicative goals and epistemic cultures. Further longitudinal studies over extended temporal ranges and across additional disciplines would offer deeper insights into whether these patterns represent transient stylistic fluctuations or sustained disciplinary evolution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings hold significant implications for academic writing instruction, disciplinary discourse analysis, and scholarly communication practices. Genre-based pedagogy should reflect the syntactic expectations of specific fields. Linguistics students may benefit from instruction in structuring hypotactic and recursive constructions to enhance conceptual clarity, while ecology students might focus on mastering compact noun phrase constructions that increase terminological precision and reduce syntactic load (Swales, 2018).\u003c/p\u003e\n\u003cp\u003eFor journal editors and publishers, awareness of discipline-specific syntactic conventions could inform editorial policy and peer review criteria. For example, ecology journals may reconsider stringent word limits that discourage necessary syntactic elaboration, while linguistics journals might promote clearer lexical cohesion to aid interdisciplinary comprehension (Hyland \u0026amp; Jiang, 2021).\u003c/p\u003e\n\u003cp\u003eMore broadly, these trends reflect the pressures of digital-era communication. Ecology\u0026rsquo;s decreasing sentence length and rising noun density reflect adaptation to fast-reading audiences, whereas linguistics\u0026rsquo; syntactic elaboration may limit accessibility. Striking a balance between rigor and readability is essential, particularly in interdisciplinary and international publishing contexts (Biber \u0026amp; Gray, 2016).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eMajor Findings\u003c/h2\u003e \u003cp\u003eThis study examined the manifestation of syntactic complexity across disciplines and over time through the application of a dual-level analytical framework to a corpus of academic abstracts. By integrating cross-disciplinary comparison with diachronic analysis, the study aimed to explore how different syntactic configurations are mobilized as functional resources for disciplinary knowledge construction.\u003c/p\u003e \u003cp\u003eThe analysis revealed systematic variations in the preferred syntactic strategies of the two disciplines. Abstracts in linguistics were characterized by a greater reliance on clausal elaboration, with hypotactic structures functioning as key resources for analytical exposition. Such structures facilitate the elaboration of arguments and the integration of prior scholarship, all of which are central to the interpretive and theory-driven orientation of the humanities. In this sense, clausal complexity in linguistics abstracts serves clear discourse-functional purposes rather than reflecting stylistic variation alone. In contrast, ecology abstracts predominantly employed strategies of nominal compression. Through extensive noun modification and compact nominal constructions, writers were able to achieve high informational density and referential efficiency. These syntactic choices are consistent with the communicative demands of STEM discourse, where precision and efficient representation of complex phenomena are prioritized. Thus, nominal complexity functions as a primary means of encoding empirical relationships within constrained textual space.\u003c/p\u003e \u003cp\u003eFrom a diachronic perspective, the two disciplines exhibited distinct developmental tendencies. Ecology abstracts showed an increasing reliance on nominal-based complexity over time, suggesting a growing need for syntactically compressed structures to represent increasingly specialized and multi-dimensional research processes. Linguistics abstracts, by contrast, demonstrated a gradual shift toward greater clausal elaboration. This pattern indicates an expanding use of syntactic resources to support more nuanced analytical framing, potentially reflecting the field\u0026rsquo;s methodological diversification and engagement with interdisciplinary perspectives.\u003c/p\u003e \u003cp\u003eTaken together, these findings highlight both the stability and the adaptability of disciplinary writing conventions. The results reaffirm the long-observed distinction between nominally oriented complexity in the hard sciences and clausal complexity in the soft sciences, and also demonstrate that these patterns are subject to gradual modification over time. By tracing how specific syntactic strategies are differentially deployed and developed across disciplines, this study contributes to a more functionally grounded understanding of syntactic complexity as a dynamic component of academic discourse\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eWhile this study offers valuable insights into disciplinary syntactic variation, several limitations constrain its broader applicability.\u003c/p\u003e \u003cp\u003eFirst, the corpus is limited to high-impact journals in ecology and linguistics. As a result, it may not fully capture stylistic diversity across other academic discourse, where different editorial pressures or linguistic norms may apply. Expanding the dataset to include multilingual and less mainstream sources could provide a more nuanced understanding of global academic writing practices.\u003c/p\u003e \u003cp\u003eSecond, the diachronic span (2019\u0026ndash;2024) offers only a short-term analysis of evolving syntactic patterns. This temporal scope may miss slower, cyclical, or generational shifts in academic style, particularly in fields experiencing rapid methodological change, such as computational linguistics, bioinformatics, or climate modeling. Future studies could benefit from longitudinal corpora spanning decades to detect sustained syntactic trends and their alignment with epistemological transformations.\u003c/p\u003e \u003cp\u003eThird, the present analysis focuses exclusively on quantifiable syntactic indices, as measured by tools like L2SCA and TAASSC. While these metrics are reliable and replicable, they offer a limited view of functional discourse. Crucial rhetorical dimensions remain underexplored. A richer picture of disciplinary discourse could be obtained by incorporating qualitative approaches, such as rhetorical move analysis, corpus-assisted discourse studies, or interviews with authors and editors.\u003c/p\u003e \u003cp\u003eLooking forward, future research could explore interdisciplinary domains, such as ecolinguistics, cognitive semiotics, or science communication, to examine whether syntactic hybridity is emerging in response to cross-field dialogue. Additionally, investigating how syntactic preferences interact with author identity (e.g., L1 background, career stage, gender) or with technological factors (e.g., AI-assisted writing tools) would offer new dimensions to academic discourse analysis. Ultimately, more integrative, cross-disciplinary, and multimodal research agendas may help uncover the deeper cognitive, social, and institutional forces that shape how knowledge is syntactically constructed and communicated.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe would like to express our sincere gratitude to the editors and anonymous reviewers for their constructive comments and suggestions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions:\u0026nbsp;\u003c/strong\u003eHL: Conceptualization, Methodology, Data Curation, Investigation, Formal Analysis, Software, Visualization, Writing the Original Draft. ZL: Conceptualization, Methodology, Data Curation, Investigation, Formal Analysis, Software, Visualization, Writing the Original Draft, Supervision, Project Administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This research was funded by Humanities and Social Sciences Fund of Ministry of Education of China (No. 22YJA740019) to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe data supporting the findings of this study are derived from publicly available academic journal abstracts. The processed data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlghazo, S., Al Salem, M. N., Alrashdan, I., \u0026amp; Rabab\u0026apos;ah, G. (2021). Grammatical devices of stance in written academic English. Heliyon, 7(11), e08463.\u003c/li\u003e\n\u003cli\u003eBauters, M., Janssens, I. 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Lexical complexity changes in 100 years\u0026rsquo; academic writing: Evidence from Nature biology letters. \u003cem\u003eJournal of English for Academic Purposes, 64\u003c/em\u003e, Article 101262.\u003c/li\u003e\n\u003c/ol\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":"syntactic complexity, academic writing, disciplinary variation, diachronic analysis","lastPublishedDoi":"10.21203/rs.3.rs-8803809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8803809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of increasing globalization and academic standardization, the stylistic features of academic writing are shaped by both disciplinary traditions and emerging communicative norms. This study explores the syntactic complexity of academic journal abstracts in the disciplines of ecology and linguistics, focusing on disciplinary variation and diachronic change between 2019 and 2024. Drawing on a corpus of 1,005 abstracts from high-impact journals, this study examines syntactic complexity patterns using indices generated by the Second Language Syntactic Complexity Analyzer (L2SCA) and the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC). Findings reveal that linguistics abstracts exhibit significantly greater clausal elaboration and use of determiners and possessives, aligning with the field\u0026rsquo;s analytic orientation and need for conceptual specificity. In contrast, ecology abstracts favor nominal compounding and adjectival modification, reflecting scientific conventions of brevity and empirical precision. Diachronic analysis shows a trend toward increased nominal density in ecology and greater clausal subordination in linguistics. These results suggest that disciplinary communicative norms shape syntactic preference and that globalization and digital-era communication are subtly reshaping academic writing styles. The study offers pedagogical implications for English for Specific Purposes (ESP) instruction and calls for more genre-sensitive academic writing guidance.\u003c/p\u003e","manuscriptTitle":"A Diachronic Study of Syntactic Complexity in Academic Journal Abstracts: Disciplinary Variations Between Ecology and Linguistics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 19:15:52","doi":"10.21203/rs.3.rs-8803809/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":"508fd21e-28eb-44aa-9f15-3cf653a6cf1c","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T16:11:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 19:15:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8803809","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8803809","identity":"rs-8803809","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}