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<article article-type="research-article" dtd-version="1.3" xml:lang="en" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
	<front>
		<journal-meta>
			<journal-id journal-id-type="publisher-id">FS</journal-id>
			<journal-title-group>
				<journal-title>Forest Systems</journal-title>
				<abbrev-journal-title abbrev-type="publisher">For. syst.</abbrev-journal-title>
			</journal-title-group>
			<issn publication-format="print">2171-5068</issn>
			<issn publication-format="electronic">2171-9845</issn>
			<publisher>
				<publisher-name>Consejo Superior de Investigaciones Cient&#xed;ficas</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="publisher-id">fs/2025341-20913</article-id>
			<article-id pub-id-type="doi">10.5424/fs/2025341-20913</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Research article</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Short-term impacts of soil enzyme activity in rhizosphere and bulk soils by forest gap size of a <italic>Platycladus orientalis</italic> plantation</article-title>
				<trans-title-group xml:lang="es">
					<trans-title>Impacto a corto plazo de la actividad enzim&#xe1;tica del suelo en suelos rizosf&#xe9;ricos y no rizosf&#xe9;ricos seg&#xfa;n el tama&#xf1;o de los claros en una plantaci&#xf3;n de <italic>Platycladus orientalis</italic>
					</trans-title>
				</trans-title-group>
				<alt-title alt-title-type="short">Soil enzyme dynamics in forest gaps</alt-title>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5785-438X</contrib-id>
					<name>
						<surname>Fei</surname>
						<given-names>Fei</given-names>
					</name>
					<aff id="aff-1-20913">
						<institution content-type="university">Nanjing Forestry University</institution>
						<institution content-type="center">Co-Innovation Center for the Sustainable Forestry in Southern China</institution>
						<addr-line>Nanjing 210037</addr-line>
						<country country="CN">P. R. China</country>
					</aff>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/" vocab-term="Data curation">Data curation</role>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/investigation/" vocab-term="Investigation">Investigation</role>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/" vocab-term="Writing &#x2013; original draft">Writing &#x2013; original draft</role>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/visualization/" vocab-term="Visualization">Visualization</role>
				</contrib>
				<contrib contrib-type="author" corresp="yes">
					<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4146-5731</contrib-id>
					<name>
						<surname>Guan</surname>
						<given-names>Qingwei</given-names>
					</name>
					<email xlink:href="guanjapan999@163.com">guanjapan999@163.com</email>
					<aff id="aff-2-20913">
						<institution content-type="university">Nanjing Forestry University</institution>
						<institution content-type="center">Co-Innovation Center for the Sustainable Forestry in Southern China</institution>
						<addr-line>Nanjing 210037</addr-line>
						<country country="CN">P. R. China</country>
					</aff>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/" vocab-term="Conceptualization">Conceptualization</role>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/methodology/" vocab-term="Methodology">Methodology</role>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/" vocab-term="Writing &#x2013; review &amp; editing">Writing &#x2013; review &amp; editing</role>
					<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term-identifier="https://credit.niso.org/contributor-roles/supervision/" vocab-term="Supervision">Supervision</role>
				</contrib>
			</contrib-group>
			<pub-date pub-type="epub">
				<day>30</day>
				<month>04</month>
				<year>2025</year>
			</pub-date>
			<pub-date pub-type="collection">
				<day>30</day>
				<month>04</month>
				<year>2025</year>
			</pub-date>
			<volume>34</volume>
			<issue>1</issue>
			<elocation-id>20913</elocation-id>
			<pub-history>
				<event>
					<event-desc>Received</event-desc>
					<date date-type="received">
						<day>24</day>
						<month>05</month>
						<year>2024</year>
					</date>
				</event>
				<event>
					<event-desc>Accepted</event-desc>
					<date date-type="accepted">
						<day>28</day>
						<month>11</month>
						<year>2024</year>
					</date>
				</event>
				<event>
					<event-desc>Published</event-desc>
					<date date-type="pub">
						<day>05</day>
						<month>05</month>
						<year>2025</year>
					</date>
				</event>
			</pub-history>
			<permissions>
				<copyright-statement>&#xa9; 2025 CSIC</copyright-statement>
				<copyright-year>2025</copyright-year>
				<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
					<license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.</license-p>
				</license>
			</permissions>
			<self-uri xlink:href="https://fs.revistas.csic.es/index.php/fs/article/view/XXXX/XXXX"/>
			<abstract>
				<title>Abstract</title>
				<sec>
					<title>Aim of study</title>
					<p> We investigated how changes in forest gap size influence soil enzyme activities in both the rhizosphere and bulk soils, mediated by rhizosphere effects (RE).</p>
				</sec>
				<sec>
					<title>Area of study</title>
					<p> Xuzhou, Jiangsu Province, China.</p>
				</sec>
				<sec>
					<title>Material and methods</title>
					<p> The study was conducted in a 46-year-old <italic>Platycladus orientalis</italic> (L.) Franco, 1950 plantation in Xuzhou and its aim was accomplished by sampling soils, one year after forest gaps establishment, from three levels of forest gap sizes: small (S, with a radius of 4 m), medium (M, with a radius of 8 m), and large (L, with a radius of 12 m), as well as control plots (CK, without any gaps). Soil enzyme activities, including peroxidase (PER), dehydrogenase (DEH), urease (URE), and invertase (INV), were quantified.</p>
				</sec>
				<sec>
					<title>Main results</title>
					<p> Gap size and season strongly affected the RE on DEH and URE. The interaction of gap size, location and season significantly affected the RE on DEH, URE, and INV. Enzyme activities in bulk soils were more sensitive to changes in nutrient availability than in rhizosphere soils, and microbial biomass played a crucial role in modulating enzyme activity. Furthermore, both L and S forest gaps exerted an influence on the RE of enzyme activity, with L gaps exhibiting the most extensive impact.</p>
				</sec>
				<sec>
					<title>Research highlights</title>
					<p> This study observed that the RE of soil enzyme activity did not increase with the enlargement of forest gap size. However, in L forest gaps, its impact range was more extensive.</p>
				</sec>
			</abstract>
			<trans-abstract xml:lang="es">
				<title>Resumen</title>
				<sec>
					<title>Objetivo del estudio</title>
					<p> Investigamos c&#xf3;mo los cambios en el tama&#xf1;o de los claros en el bosque influyen en las actividades enzim&#xe1;ticas del suelo, tanto en suelos rizosf&#xe9;ricos como en los no rizosf&#xe9;ricos, mediados por los efectos de la rizosfera (ER).</p>
				</sec>
				<sec>
					<title>&#xc1;rea de estudio</title>
					<p> Xuzhou, provincia de Jiangsu, China.</p>
				</sec>
				<sec>
					<title>Material y m&#xe9;todos</title>
					<p> El estudio se llev&#xf3; a cabo en una plantaci&#xf3;n de <italic>Platycladus orientalis</italic> (L.) Franco, 1950 de 46 a&#xf1;os en Xuzhou. Para ello, se tomaron muestras de suelo un a&#xf1;o despu&#xe9;s de la apertura de claros en el bosque, considerando tres tama&#xf1;os de claros: peque&#xf1;os (S, con un radio de 4 m), medianos (M, con un radio de 8 m) y grandes (L, con un radio de 12 m), adem&#xe1;s de parcelas de control (CK, sin claros). Se cuantificaron las actividades de las enzimas del suelo, incluyendo peroxidasa (PER), deshidrogenasa (DEH), ureasa (URE) e invertasa (INV).</p>
				</sec>
				<sec>
					<title>Principales resultados</title>
					<p> El tama&#xf1;o del claro y la estaci&#xf3;n del a&#xf1;o afectaron significativamente en los ER sobre la DEH y la URE. La interacci&#xf3;n entre el tama&#xf1;o del claro, la ubicaci&#xf3;n y la estaci&#xf3;n influy&#xf3; significativamente en los ER de la DEH, URE e INV. Las actividades enzim&#xe1;ticas en los suelos no rizosf&#xe9;ricos fueron m&#xe1;s sensibles a los cambios en la disponibilidad de nutrientes que en los suelos rizosf&#xe9;ricos, y la biomasa microbiana desempe&#xf1;&#xf3; un papel crucial en la modulaci&#xf3;n de la actividad enzim&#xe1;tica. Adem&#xe1;s, los claros de bosque grandes (L) y peque&#xf1;os (S) influyeron en los ER de la actividad enzim&#xe1;tica, siendo los claros grandes (L) los que mostraron el efecto m&#xe1;s amplio.</p>
				</sec>
				<sec>
					<title>Conclusiones</title>
					<p> Se observ&#xf3; que los ER de la actividad enzim&#xe1;tica del suelo no aumentaron con la ampliaci&#xf3;n del tama&#xf1;o del claro. Sin embargo, en los claros grandes (L), su impacto tuvo un alcance m&#xe1;s extenso.</p>
				</sec>
			</trans-abstract>
			<kwd-group>
				<kwd>enzyme activity</kwd>
				<kwd>forest gap size</kwd>
				<kwd>forest management</kwd>
				<kwd>rhizosphere effect</kwd>
			</kwd-group>
			<kwd-group xml:lang="es">
				<kwd>actividad enzim&#xe1;tica</kwd>
				<kwd>efecto de la rizosfera</kwd>
				<kwd>gesti&#xf3;n forestal</kwd>
				<kwd>tama&#xf1;o del claro del bosque</kwd>
			</kwd-group>
			<funding-group id="fug-1-20913">
				<award-group id="awg-1-20913">
					<funding-source id="fus-1-20913">Priority Academic Program Development</funding-source>
					<funding-source id="fus-2-20913">Jiangsu Higher Education Institutions</funding-source>
					<award-id id="awi-1-20913">BE2022420</award-id>
				</award-group>
				<funding-statement>Priority Academic Program Development of Jiangsu Higher Education Institutions: PAPD. Jiangsu emission peak carbon neutrality technology innovation project: BE2022420</funding-statement>
			</funding-group>
			<counts>
				<fig-count count="11"/>
				<table-count count="5"/>
				<equation-count count="1"/>
				<ref-count count="40"/>
				<page-count count="0"/>
			</counts>
		</article-meta>
	</front>
	<body>
		<sec id="sec-1-20913" sec-type="intro">
			<title>Introduction</title>
			<p>Soil enzyme activity serves as a vital interface for numerous biochemical reactions within soil ecosystems, playing a pivotal role in driving ecological processes. The regulation of enzyme activity is influenced by a complex interplay of physical, chemical and biological factors, which together elucidates the mechanisms governing nutrient cycling and biochemical transformations in soils (<xref ref-type="bibr" rid="ref-5-20913">Cotrufo et al., 2015</xref>). Abiotic factors such as soil temperature and composition, alongside biotic factors like microbial community structure, collectively shape enzymes activity, making it a reliable indicator of changes within soil ecosystems (<xref ref-type="bibr" rid="ref-31-20913">Utobo &amp; Tewari, 2015</xref>).</p>
			<p>Key enzymes responsible for carbon and nitrogen cycling, including peroxidase (PER), dehydrogenase (DEH), urease (URE), and invertase (INV), exhibit high sensitivity to environmental fluctuations (<xref ref-type="bibr" rid="ref-8-20913">Feng et al., 2019</xref>). As an extracellular enzyme, PER is mainly involved in the degradation of complex organic compounds and it plays a key role in the carbon cycle, an activity that supports microbial metabolism and helps to stabilise organic carbon in the soil, while being influenced by the soil microenvironment (<xref ref-type="bibr" rid="ref-23-20913">Nakayama &amp; Tateno, 2022</xref>). Dehydrogenase, as an indicator of microbial oxidative activity, reflects the metabolic potential of soil microbial communities and is often used as a proxy for overall microbial activity, whereas extremes like drought or waterlogging inhibit its activity (<xref ref-type="bibr" rid="ref-18-20913">Makoi &amp; Ndakidemi, 2008</xref>; <xref ref-type="bibr" rid="ref-9-20913">Filipovi&#x107; et al., 2020</xref>). Urease plays an important role in the nitrogen cycle, and soil organic matter content and the availability of urea or other nitrogenous substrates greatly affect urease activity (<xref ref-type="bibr" rid="ref-29-20913">Sun et al., 2021a</xref>). Invertase provides a source of energy for soil microorganisms, is an important participant in the carbon cycle, and is closely linked to soil organic matter dynamics, with its activity influenced by root exudates, organic carbon availability and soil microbial biomass ( <xref ref-type="bibr" rid="ref-17-20913">Ma et al., 2023</xref>).</p>
			<p>Despite significant progress in understanding the individual roles of these enzymes, the intricate relationships between nutrient availability and enzyme activity within ecosystems remain inadequately explored. Gaps persist in our knowledge of how environmental factors, particularly forest management practices like gap creation, affect enzyme dynamics. Addressing these gaps is essential for advancing forest management strategies. Specifically, understanding the impact of forest gap size on soil enzyme activity will provide valuable insights into optimizing forest ecosystems for biodiversity conservation, nutrient cycling, and overall ecological stability.</p>
			<p>Within the rhizosphere, the phenomenon known as the rhizosphere effect (RE) encompasses alterations in nutrient cycling, enzyme activities, and microbial communities (<xref ref-type="bibr" rid="ref-10-20913">Finzi et al., 2015</xref>; <xref ref-type="bibr" rid="ref-3-20913">Chen et al., 2018</xref>). In soil ecosystems, enzyme and microbial activities are heightened in the rhizosphere compared to bulk soils devoid of roots (<xref ref-type="bibr" rid="ref-24-20913">Pausch &amp; Kuzyakov, 2011</xref>). Numerous studies have consistently demonstrated that rhizosphere soils exhibit distinctive microbial behaviors and physicochemical properties compared to bulk soils without roots (<xref ref-type="bibr" rid="ref-25-20913">Philippot et al., 2013</xref>). The discrepancy is attributed to the substantial influx of plant photosynthetic products into the rhizosphere, which subsequently alters soil properties and enhances nutrient assimilation (<xref ref-type="bibr" rid="ref-23-20913">Nakayama &amp; Tateno, 2022</xref>). Concurrently, labile organic compounds, known as root exudates, are continuously released into the rhizosphere by the fine roots of plants (<xref ref-type="bibr" rid="ref-16-20913">Kuzyakov&amp; Razavi, 2019</xref>). These root exudates serve as a primary reservoir of energy and substrate for microbial activities, thereby establishing the rhizosphere as a hotspot for heightened microbial abundance and activity (<xref ref-type="bibr" rid="ref-34-20913">Wang et al., 2019a</xref>). Consequently, microbial and enzyme activities are significantly augmented within the rhizosphere soils (<xref ref-type="bibr" rid="ref-16-20913">Kuzyakov &amp; Razavi, 2019</xref>).</p>
			<p>Forest gaps play significant roles in forest succession and nutrient cycling (<xref ref-type="bibr" rid="ref-13-20913">Gray et al., 2012</xref>; <xref ref-type="bibr" rid="ref-38-20913">Yang et al., 2017</xref>). Upon the formation of forest gaps, environmental conditions such as light, temperature, and humidity undergo changes due to the sudden reduction of the forest canopy and a decrease in the degree of shading (<xref ref-type="bibr" rid="ref-6-20913">d&#x2019;Oliveira &amp; Ribas, 2011</xref>). This alteration triggers the resurgence of understory vegetation, modifies soil nutrient cycling, and subsequently induces shifts in microbial and enzyme activities (<xref ref-type="bibr" rid="ref-13-20913">Gray et al., 2012</xref>; <xref ref-type="bibr" rid="ref-7-20913">Fei et al., 2023</xref>). In recent decades, there has been a growing emphasis on establishing silvicultural forest gaps that mimic natural disturbances, as they are more conducive to ecologically restoring forests (<xref ref-type="bibr" rid="ref-22-20913">Muscolo et al., 2014</xref>). Creating the silvicultural forest gaps aims to conserve biodiversity and ecosystem functioning while optimising harvesting practices (<xref ref-type="bibr" rid="ref-32-20913">Vajari et al., 2012</xref>). The size of the forest gap predicts the degree of the environmental heterogeneity it introduces (<xref ref-type="bibr" rid="ref-22-20913">Muscolo et al., 2014</xref>). Forest gaps of different sizes also vary in the intensity of their impacts on environmental conditions and soils within and surrounding them (<xref ref-type="bibr" rid="ref-23-20913">Nakayama &amp; Tateno, 2022</xref>).</p>
			<p>Therefore, different sizes of forest gaps introduce varying degrees of environmental heterogeneity to forest ecosystems, resulting in alterations in understory environmental conditions. These environmental changes affect rhizosphere and bulk soils, prompting inquiries into how microbial communities, enzyme activities, and nutrient cycling respond to such shifts. This aspect necessitates further exploration and a deeper understanding and clarification. To investigate the effects of forest gap sizes on the RE of four soil enzymes in <italic>Platycladus orientalis</italic> (L.) Franco, 1950 plantation, three different sizes forest gaps were established in Xuzhou, China. A range of soil biological and physicochemical variables characterizing the soil, including microbial biomass nitrogen and carbon (MBN and MBC, respectively), total nitrogen and carbon (TN and TC, respectively), soil soluble organic carbon (SOC), NO<sub>3</sub>
				<sup>&#x2212;</sup>-N, and NH<sub>4</sub>
				<sup>+</sup>-N, were determined.</p>
			<p>We hypothesized that (1) The heterogeneity of the understory environment increases with the expansion of forest gap size, leading to a corresponding rise in the RE of soil enzyme activities, and (2) the larger the forest gap size, the broader its effect on the RE of activity extends. This study provides valuable insights into the intricate interactions between forest gap size and the RE of soil enzyme activity. Consequently, this study may contribute to enhancing forest management strategies and practices.</p>
		</sec>
		<sec id="sec-2-20913" sec-type="materials|methods">
			<title>Material and methods</title>
			<sec id="sec-2.1-20913">
				<title>Study region</title>
				<p>The study region was selected in Xuzhou City (34&#xb0;12&#x2019; N, 117&#xb0;30&#x2019; E), Jiangsu Province, China (<xref ref-type="fig" rid="fig-1-20913">Figure 1a</xref>), with altitudes ranging from 50 to 60 m above sea level. Geographically situated within a warm-temperate zone, this region experiences a climatic pattern characterized by monsoons. Over the period from 2002 to 2013, the mean annual air temperature was 14.0 &#x2da;C, the average monthly lowest and highest air temperature was -3.3 &#x2da;C and 23.5 &#x2da;C respectively, and the annual precipitation amounted to 872 mm. The dominant parent materials in the study region are primarily clay and loess. The main soil groups in Xuzhou are classified as silty clay loam and cinnamon soils. The fundamental characteristics of study region are summarized in <xref ref-type="table" rid="taw-1-20913">Table 1</xref>.</p>
				<fig id="fig-1-20913">
					<label>Figure 1</label>
					<caption>
						<title>(a) The experimental sample plots were located in Xuzhou City, in the northwestern part of Jiangsu Province, China. (b) In this study, three circular forest gaps of different sizes were established, with radius of R<sub>L</sub>, R<sub>M</sub>, and R<sub>S</sub>, respectively (R<sub>L</sub>=12m, R<sub>M</sub>=8m, and R<sub>S</sub>=4m). From the center of each forest gap, proceeding due north, three 2 m&#xd7;2 m sampling points were established at radius intervals. The sampling points were designated as L1, L2, L3, M1, M2, M3, S1, S2, and S3.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf1.png" id="gra-1-20913"/>
				</fig>
				<table-wrap id="taw-1-20913">
					<label>Table 1</label>
					<caption>
						<title>Stand survey of <italic>Platycladus orientalis</italic> plantation in Zhaotuan Forest Farm of Xuzhou City, China.</title>
					</caption>
					<table>
						<colgroup>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center">Slope Aspect</th>
								<th align="center">Slope Gradient/(&#x25e6; )</th>
								<th align="center">Stand Age/(a)</th>
								<th align="center">Stand Density/ (Tree&#xb7;hm<sup>-2</sup>)</th>
								<th align="center">Canopy Density/(%)</th>
								<th align="center">Height/(m) </th>
								<th align="center">Diameter at Breast Height/(cm)</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Southeast</td>
								<td align="center">5</td>
								<td align="center">46</td>
								<td align="center">4728</td>
								<td align="center">85</td>
								<td align="center">8.7</td>
								<td align="center">9.5</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="twf-1-20913">
							<p>The values are mean &#xb1; standard deviations. WC: water content; TC: total carbon; TN: total nitrogen; SOC: soluble organic carbon. (n=120)</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</sec>
			<sec id="sec-2.2-20913">
				<title>Experimental design</title>
				<p>In May 2016, various sizes forest gaps were established in a <italic>P, orientalis</italic> plantations at Zhaotuan Forest Farm in Xuzhou city in a randomized manner. The forest gaps were categorized based on their radius as 4 m (R<sub>S</sub>), 8 m (R<sub>M</sub>), and 12 m (R<sub>L</sub>). Each gap size was represented by three replicates, and three control plots (CK) were also established within a contiguous closed-canopy forest. The distance between the CK plots and the forest gaps was ensured to be no less than 100 m, with all adjacent forest gaps and CK plots also being at least 100 m apart. From the center of each forest gap, proceeding due north, three 2 m&#xd7;2 m sampling points were established at radius intervals (<xref ref-type="fig" rid="fig-1-20913">Figure 1b</xref>). Locations 1, 2, and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively. Depending on the distance of the sampling point from the centre of the forest gap, the sampling points were labeled as L1, L2, and L3 in the L forest gap, M1, M2, and M3 in the M forest gap, and S1, S2, and S3 in the S forest gap (<xref ref-type="fig" rid="fig-1-20913">Figure 1b</xref>). Following the falling of trees to create the forest gap, all plant debris in the area surrounding the forest gap was meticulously removed.</p>
				<p>Samples were collected from three control plots and nine forest gaps. Within each sampling point, five soil samples were randomly selected, mixed, and labeled. This process entailed removing an undecomposed layer of litter, followed by the extraction of soil samples from a depth of 0 to 20 cm. Fine roots (&lt;2 mm) were delicately removed, and the soil within 2 mm of the fine roots was separated and labeled as rhizosphere soil. The remaining soil was classified as bulk soil. Subsequently, these soil samples underwent sieving using a 2 mm mesh, were appropriately labeled, and promptly transported for thorough analysis. Soil sampling was conducted at four time points in 2017 (April, June, August and October). The initial fundamental physical and chemical attributes of the sample plots are outlined in <xref ref-type="table" rid="taw-2-20913">Table 2</xref>.</p>
				<table-wrap id="taw-2-20913">
					<label>Table 2</label>
					<caption>
						<title>Initial fundamental soil physical and chemical properties of the sample plots in the <italic>Platycladus orientalis</italic> plantation in Zhaotuan Forest Farm of Xuzhou City, China.</title>
					</caption>
					<table>
						<colgroup>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center"> </th>
								<th align="center">WC (%)</th>
								<th align="center">TC (g&#x22c5;kg<sup>&#x2212; 1</sup>)</th>
								<th align="center">TN (g&#x22c5;kg<sup>&#x2212; 1</sup>)</th>
								<th align="center">NH<sub>4</sub>
									<sup>+</sup>-N (mg&#x22c5;kg<sup>&#x2212;1</sup>)</th>
								<th align="center">NO<sub>3</sub>
									<sup>&#x2212;</sup>-N (mg&#x22c5;kg<sup>&#x2212;1</sup>)</th>
								<th align="center">SOC (mg&#x22c5;kg<sup>&#x2212;1</sup>)</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Bulk soil</td>
								<td align="center">21.58&#xb1;0.75%</td>
								<td align="center">30.79&#xb1;1.35</td>
								<td align="center">2.27&#xb1;0.09</td>
								<td align="center">1.16&#xb1;0.06</td>
								<td align="center">1.68&#xb1;0.08</td>
								<td align="center">116.77&#xb1;4.96</td>
							</tr>
							<tr>
								<td align="center">Rhizosphere soil</td>
								<td align="center">24.06&#xb1;0.94%</td>
								<td align="center">33.56&#xb1;1.59</td>
								<td align="center">2.61&#xb1;0.11</td>
								<td align="center">1.35&#xb1;0.09</td>
								<td align="center">1.87&#xb1;0.10</td>
								<td align="center">129.40&#xb1;6.11</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="twf-2-20913">
							<p>The values are mean &#xb1; standard deviations. WC: water content; TC: total carbon; TN: total nitrogen; SOC: soluble organic carbon. (n=120)</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</sec>
			<sec id="sec-2.3-20913">
				<title>Laboratory analysis</title>
				<p>We measured the photosynthetically active radiation (PAR) at every sampling point and every distance point using the SKP 215 photosynthetically active radiation sensor (SKYE, UK), which was observed at 6: 00-18:00 every 2 h on 6 days per month. Soil temperature (ST) was ascertained in situ employing DS1921-F5# temperature sensor (Maxim, USA), configured to record data at 30-minute intervals. Soil water content (WC) was obtained by calculating the loosed weight of fresh soil with the weight of approximately 10 g after 105 &#x2da;C oven-drying for a duration of 24 h. Quantifications of TC and TN were executed utilizing the Vario EL III elemental analyzer (Elementar, Germany).</p>
				<p>The concentrations of NO<sub>3</sub>
					<sup>&#x2212;</sup>-N and NH<sub>4</sub>
					<sup>+</sup>-N were measured by extracted a soil sample in 2 mol/L KCl. Subsequently, the concentrations of NO<sub>3</sub>
					<sup>&#x2212;</sup>-N and NH<sub>4</sub>
					<sup>+</sup>-N were evaluated with colorimetric method, employing the indophenol blue and the Griess assay method (<xref ref-type="bibr" rid="ref-20-20913">Miranda et al., 2001</xref>) respectively. These solutions were conducted using Synergy HXT microplate reader (BioTek, USA) with the optical density readings taken at wavelengths of 636 nm and 540 nm, correspondingly.</p>
				<p>We use Shimadzu TOC-VCPN carbon analyzer (Japan) to determinate the concentration of SOC. The chloroform fumigation extraction method was using to ascertain the MBN and MBC in the soil were (<xref ref-type="bibr" rid="ref-1-20913">Brookes et al., 1985</xref>; <xref ref-type="bibr" rid="ref-33-20913">Vance et al., 1987</xref>). Concisely, underwent fumigate 20 g of fresh soil with anhydrous CHCl<sub>3</sub> within a desiccator at 25 &#x2da;C for 24 h. Post CHCl<sub>3</sub> removal, both unfumigated and fumigated soil samples were subjected to extraction with 80 ml of 0.5 mol/L K<sub>2</sub>SO<sub>4</sub> for 30 min. We analyzed the organic nitrogen and carbon present in the solution using Shimadzu TOC-VCPN carbon analyzer (Japan). Applying a conversion factor of <italic>k</italic>
					<sub>
						<italic>EN</italic>
					</sub> = 0.68: MBN = EN/ <italic>k</italic>
					<sub>
						<italic>EN</italic>
					</sub> and <italic>k</italic>
					<sub>
						<italic>EC</italic>
					</sub> = 0.45: MBC = EC/<italic>k</italic>
					<sub>
						<italic>EC</italic>
					</sub> the differential between the unfumigated and fumigated samples facilitated the computation of MBN and MBC was computed (<xref ref-type="bibr" rid="ref-1-20913">Brookes et al., 1985</xref>; <xref ref-type="bibr" rid="ref-14-20913">Joergensen &amp; Mueller, 1996</xref>).</p>
				<p>Soil enzyme activity was determined by adding substrates specific to different enzymes to air-dried soil and measuring the concentration of their products after a unit of time which was assessed using the colorimetric technique with spectrophotometric quantification. The activity of PER was ascertained and denoted as &#x3bc;g purple gallic acid g<sup>-1</sup> soil h<sup>-1</sup> (<xref ref-type="bibr" rid="ref-19-20913">Markkola et al. 1990</xref>). The activity of DEH was estimated and denoted as &#x3bc;g TPF g<sup>&#x2212;1</sup> soil h<sup>&#x2212;1</sup> (<xref ref-type="bibr" rid="ref-2-20913">Casida et al. 1964</xref>). Urease activity was determined and denoted as mg NH<sub>4</sub>-N g<sup>-1</sup> soil d<sup>-1</sup> (<xref ref-type="bibr" rid="ref-15-20913">Kandeler&amp; Gerber, 1988</xref>). Invertase activity was obtained and denoted as mg glucose g<sup>-1</sup> soil d<sup>-1</sup> (<xref ref-type="bibr" rid="ref-12-20913">Frankeberger &amp; Johanson, 1983</xref>).</p>
				<p>The RE was obtained as the differential between bulk soil and rhizosphere soil:</p>
				<disp-formula id="dif-1-20913">
					<mml:math id="mml-1-20913">
						<mml:mi>R</mml:mi>
						<mml:mi>E</mml:mi>
						<mml:mo>=</mml:mo>
						<mml:mfrac>
							<mml:mrow>
								<mml:msub>
									<mml:mrow>
										<mml:msub>
											<mml:mrow>
												<mml:mi>C</mml:mi>
											</mml:mrow>
											<mml:mrow>
												<mml:mi>r</mml:mi>
												<mml:mi>h</mml:mi>
												<mml:mi>i</mml:mi>
												<mml:mi>z</mml:mi>
												<mml:mi>o</mml:mi>
												<mml:mi>s</mml:mi>
												<mml:mi>p</mml:mi>
												<mml:mi>h</mml:mi>
												<mml:mi>e</mml:mi>
												<mml:mi>r</mml:mi>
												<mml:mi>e</mml:mi>
												<mml:mo>_</mml:mo>
												<mml:mi>s</mml:mi>
												<mml:mi>o</mml:mi>
												<mml:mi>i</mml:mi>
												<mml:mi>l</mml:mi>
											</mml:mrow>
										</mml:msub>
										<mml:mo>-</mml:mo>
										<mml:mi>C</mml:mi>
									</mml:mrow>
									<mml:mrow>
										<mml:mi>b</mml:mi>
										<mml:mi>u</mml:mi>
										<mml:mi>l</mml:mi>
										<mml:mi>k</mml:mi>
										<mml:mo>_</mml:mo>
										<mml:mi>s</mml:mi>
										<mml:mi>o</mml:mi>
										<mml:mi>i</mml:mi>
										<mml:mi>l</mml:mi>
									</mml:mrow>
								</mml:msub>
							</mml:mrow>
							<mml:mrow>
								<mml:msub>
									<mml:mrow>
										<mml:mi>C</mml:mi>
									</mml:mrow>
									<mml:mrow>
										<mml:mi>b</mml:mi>
										<mml:mi>u</mml:mi>
										<mml:mi>l</mml:mi>
										<mml:mi>k</mml:mi>
										<mml:mo>_</mml:mo>
										<mml:mi>s</mml:mi>
										<mml:mi>o</mml:mi>
										<mml:mi>i</mml:mi>
										<mml:mi>l</mml:mi>
									</mml:mrow>
								</mml:msub>
							</mml:mrow>
						</mml:mfrac>
					</mml:math>
				</disp-formula>
				<p>C<sub>rhizosphere_soil</sub> denoted microbial biomass, soil properties, and enzyme activities in the rhizosphere soils, while C<sub>bulk_soil</sub> denoted attributes in the bulk soils.</p>
			</sec>
			<sec id="sec-2.4-20913">
				<title>Statistical analysis</title>
				<p>Repeated-measures analysis of variance (RMANOVA) of the effect of gap size, location, season and their interaction and graphical representations were conducted in the R Studio software program (version 4.2.0). One-way analysis of variance (ANOVA) in SPSS 26.0 was conducted to scrutinize the variances among disparate gap sizes, with subsequent utilizing the method of Least Significant Difference (LSD) (<italic>p</italic>&#x2264;0.05) to multiple comparisons. The correlations among the pertinent explanatory factors and soil enzyme activities were discerned by deployed Pearson correlation analysis in SPSS.</p>
			</sec>
		</sec>
		<sec id="sec-3-20913" sec-type="results">
			<title>Results</title>
			<sec id="sec-3.1-20913">
				<title>Environmental factors in forest gaps</title>
				<p>Forest gaps exerted significant effects on PAR in all seasons and across all sizes of forest gaps (<xref ref-type="fig" rid="fig-2-20913">Figure 2a</xref>). As depicted in <xref ref-type="fig" rid="fig-2-20913">Figure 2a</xref>, PAR was maximal at location 1 in every season and all forest gap sizes, decreasing proportionally with the distance from the center of the forest gap. The PAR at location 3 was the lowest and did not significantly different with CK (<xref ref-type="fig" rid="fig-2-20913">Figure 2a</xref>).</p>
				<fig id="fig-2-20913">
					<label>Figure 2</label>
					<caption>
						<title>(a) Photosynthetically active radiation in different locations and gap sizes across seasons (b) Soil temperature in different locations and gap sizes across seasons. PAR: photosynthetically active radiation. ST: soil temperature. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf2.png" id="gra-2-20913"/>
				</fig>
				<p>The size of the forest gaps had a more pronounced impact on ST in August (<xref ref-type="fig" rid="fig-2-20913">Figure 2b</xref>). As depicted in <xref ref-type="fig" rid="fig-2-20913">Figure 2b</xref>, ST at location 1 was the highest in August (&gt;35 &#x2da;C) across all sizes of forest gaps. In comparison to CK, ST showed no significant difference in June and October (<xref ref-type="fig" rid="fig-2-20913">Figure 2b</xref>). However, a decrease in ST was observed under all sizes of the forest gaps in October (22 &#x2da;C) (<xref ref-type="fig" rid="fig-2-20913">Figure 2b</xref>).</p>
			</sec>
			<sec id="sec-3.2-20913">
				<title>Soil physical and chemical properties and microbial biomass</title>
				<p>In <xref ref-type="fig" rid="fig-3-20913">Figure 3a</xref>, it is evident that the WC was lower at location 1 than in the CK (<italic>p</italic>&lt;0.05) in June, specifically in the bulk soil. Similarly, in August, the WC was significantly lower than CK in all locations of the M and S gaps, and in October, it was notably lower in all locations of the M gaps (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-3-20913">Figure 3a</xref>). Conversely, in October, the soil water content in all locations of the L gaps was significantly higher than CK (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-3-20913">Figure 3a</xref>). From <xref ref-type="fig" rid="fig-3-20913">Figure 3b</xref>, it is apparent that in the rhizosphere soil, the WC was lower than CK at all locations except location L3 in August. However, in October, it was higher than CK at location L1 and L2, and lower than CK at all locations in M gaps and location S1 and S2 (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-3-20913">Figure 3b</xref>).</p>
				<fig id="fig-3-20913">
					<label>Figure 3</label>
					<caption>
						<title>(a) Water content in different locations and gap sizes across seasons for: (a) bulk soil and (b) rhizosphere soil. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf3.png" id="gra-3-20913"/>
				</fig>
				<p>As depicted in <xref ref-type="fig" rid="fig-4-20913">Figure 4a</xref>, in the bulk soils, TC was lower at M3 and S2 than CK in April (<italic>p</italic>&lt;0.05). In June and October, TC was significantly lower at all locations in the L gaps than CK (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-4-20913">Figure 4a</xref>). Conversely, in June, TC was noticeably higher at all locations in the M gaps, and the opposite trend was observed in October (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-4-20913">Figure 4a</xref>). Additionally, in October, TC was significantly higher than CK at all locations in the S gaps (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-4-20913">Figure 4a</xref>). From <xref ref-type="fig" rid="fig-4-20913">Figure 4b</xref>, it is apparent that in the rhizosphere soils, TC was lower than CK in all seasons in the L gaps, while it was higher than CK in most locations and seasons in the M and S gaps (<italic>p</italic>&lt;0.05). Furthermore, as depicted in <xref ref-type="fig" rid="fig-4-20913">Figure 4c</xref>, in the bulk soils, TN was lower than CK in all locations in the L gaps in June, and higher than CK in April and June in the M gaps and location S2 (<italic>p</italic>&lt;0.05). Lastly, from <xref ref-type="fig" rid="fig-4-20913">Figure 4d</xref>, it can be observed that in rhizosphere soils, TN was lower in April, June, and October for all locations in the L gaps compared to CK, and lower in October for the M gaps and the locations S1 and S3 compared to CK (<italic>p</italic>&lt;0.05).</p>
				<fig id="fig-4-20913">
					<label>Figure 4</label>
					<caption>
						<title>Total carbon content in different locations and gap sizes across seasons for: (a) bulk soil and (b) rhizosphere soil. Total nitrogen content in different locations and gap sizes across seasons for: (c) bulk soil and (d) rhizosphere soil. TC: total carbon; TN: total nitrogen. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf4.png" id="gra-4-20913"/>
				</fig>
				<p>In <xref ref-type="fig" rid="fig-5-20913">Figure 5a</xref>, it is observed that in bulk soils, SOC levels at locations L1, L2 and S1 were lower than CK in August, while all locations in the M gaps exhibited significantly higher SOC levels than CK (<italic>p</italic>&lt;0.05). <xref ref-type="fig" rid="fig-5-20913">Figure 5b</xref> illustrates that in the rhizosphere soils, SOC levels at all locations except L3 were higher than CK in April, whereas in October, SOC levels at locations L2, M1, M2, S1, and S3 were lower than CK (<italic>p</italic>&lt;0.05).</p>
				<fig id="fig-5-20913">
					<label>Figure 5</label>
					<caption>
						<title>Soluble organic carbon content in different locations and gap sizes across seasons for: (a) bulk soil and (b) rhizosphere soil. SOC: soluble organic carbon. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf5.png" id="gra-5-20913"/>
				</fig>
				<p>As depicted in <xref ref-type="fig" rid="fig-6-20913">Figure 6a</xref>, in bulk soils, NH<sub>4</sub>
					<sup>+</sup>-N levels were higher than CK at locations M2 and S2 in August, while they were lower than CK (<italic>p</italic>&lt;0.05) at all locations in the L gaps in October. In the rhizosphere soil, as shown in <xref ref-type="fig" rid="fig-6-20913">Figure 6b</xref>, NH<sub>4</sub>
					<sup>+</sup>-N levels were higher than CK at all locations in the M gaps in April and at the locations M2 and S2 in August, while they were lower than CK at all forest gap sizes in October (<italic>p</italic>&lt;0.05). From <xref ref-type="fig" rid="fig-6-20913">Figure 6c</xref>, it is evident that in bulk soils, NO<sub>3</sub>
					<sup>&#x2212;</sup>-N levels at locations L1 and L2 were lower than CK in August, and higher than CK in August and October in the M and S gaps (<italic>p</italic>&lt;0.05). Furthermore, as depicted in <xref ref-type="fig" rid="fig-6-20913">Figure 6d</xref>, in the rhizosphere soils, all locations in the L gaps had lower NO<sub>3</sub>
					<sup>&#x2212;</sup>-N levels than CK in October, while NO<sub>3</sub>
					<sup>&#x2212;</sup>-N levels in the M gaps and locations S2 and S3 were notably higher than CK in October (<italic>p</italic>&lt;0.05).</p>
				<fig id="fig-6-20913">
					<label>Figure 6</label>
					<caption>
						<title>NH<sub>4</sub>
							<sup>+</sup>-N content in different locations and gap sizes across seasons for: (a) bulk soil and (b) rhizosphere soil. NO<sub>3</sub>
							<sup>&#x2014;</sup>N content in different locations and gap sizes across seasons for: (c) bulk soil and (d) rhizosphere soil. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf6.png" id="gra-6-20913"/>
				</fig>
				<p>Illustrated in <xref ref-type="fig" rid="fig-7-20913">Figure 7a</xref>, in bulk soils, MBC levels were lower than CK at all forest gap sizes in August (<italic>p</italic>&lt;0.05). From <xref ref-type="fig" rid="fig-7-20913">Figure 7b</xref>, it is observed that in the rhizosphere soils, MBC levels were lower than CK at locations L1, M1, M2, and all locations of S forest gaps in October (<italic>p</italic>&lt;0.05). Furthermore, as shown in <xref ref-type="fig" rid="fig-7-20913">Figure 7c</xref>, in bulk soils, MBN levels were lower in August at locations L2, L3, M1, and S3 compared to CK (<italic>p</italic>&lt;0.05). Finally, from <xref ref-type="fig" rid="fig-7-20913">Figure 7d</xref>, it can be seen that in the rhizosphere soil, all locations except M3 had lower MBN levels than CK in October (<italic>p</italic>&lt;0.05).</p>
				<fig id="fig-7-20913">
					<label>Figure 7</label>
					<caption>
						<title>Microbial biomass carbon content in different locations and gap sizes across seasons for: (a) bulk soil and (b) rhizosphere soil. Microbial biomass nitrogen different locations and gap sizes across seasons for: (c) bulk soil and (d) rhizosphere soil. MBC: microbial biomass carbon; MBN: microbial biomass nitrogen. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf7.png" id="gra-7-20913"/>
				</fig>
			</sec>
			<sec id="sec-3.3-20913">
				<title>Rhizosphere effects of four soil enzyme activities and repeated-measures analysis of variance</title>
				<p>Compared to CK, the RE on PER were elevated at locations M2 and S2 in June (<italic>p</italic>&lt;0.05) and decreased at L3 and S2 locations in October (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-8-20913">Figure 8</xref>). The RE on DEH were lower than CK at locations M1, M2, and S3 in June (<italic>p</italic>&lt;0.05) and lower than CK at all the locations except L2 in August (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-9-20913">Figure 9</xref>). In comparison to CK, the RE on URE were increased at locations L3, M1, and S1 in April (<italic>p</italic>&lt;0.05) and increased in L and S gaps but decreased in M gaps in June (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-10-20913">Figure 10</xref>). In August, RE on URE were lower (<italic>p</italic>&lt;0.05) than CK at locations L1, L3, M2, M3, S1, and S2 but higher than CK at M1 and S3 (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-10-20913">Figure 10</xref>). The RE on URE were lower than CK at L1, L2, L3, and S1 in October (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-10-20913">Figure 10</xref>). The RE on INV were higher than CK at all the locations except locations L1 and M3 in April (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-11-20913">Figure 11</xref>). Compared to CK, RE on INV were higher at locations L2 and S3 but lower at L3, M1, M2, M3, S1, and S2 locations in August (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-11-20913">Figure 11</xref>). The RE on INV were lower than CK at all locations in October (<italic>p</italic>&lt;0.05) (<xref ref-type="fig" rid="fig-11-20913">Figure 11</xref>).</p>
				<fig id="fig-8-20913">
					<label>Figure 8</label>
					<caption>
						<title>Rhizosphere effects on soil peroxidase activity content in different locations and gap sizes across seasons. RE: rhizosphere effect; PER: peroxidase. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf8.png" id="gra-8-20913"/>
				</fig>
				<fig id="fig-9-20913">
					<label>Figure 9</label>
					<caption>
						<title>Rhizosphere effects on soil dehydrogenase activity content in different locations and gap sizes across seasons. RE: rhizosphere effect; DEH: dehydrogenase. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf9.png" id="gra-9-20913"/>
				</fig>
				<fig id="fig-10-20913">
					<label>Figure 10</label>
					<caption>
						<title>Rhizosphere effects on soil urease content in different locations and gap sizes across seasons. RE: rhizosphere effect; URE: urease. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf10.png" id="gra-10-20913"/>
				</fig>
				<fig id="fig-11-20913">
					<label>Figure 11</label>
					<caption>
						<title>Rhizosphere effects on soil invertase activity content in different locations and gap sizes across seasons. RE: rhizosphere effect; INV: invertase. Locations 1, 2 and 3 represent 1 time the forest gap radius, 2 times the forest gap radius, and 3 times the forest gap radius, respectively.</title>
					</caption>
					<graphic xlink:href="FS-34-01-20913-gf11.png" id="gra-11-20913"/>
				</fig>
				<p>From the RMANOVA (<xref ref-type="table" rid="taw-3-20913">Table 3</xref>), gap size only significantly affected the RE on URE. Season significantly affected the RE on DEH (<xref ref-type="table" rid="taw-3-20913">Table 3</xref>). The interaction of gap size and season strongly affected the RE on URE (<xref ref-type="table" rid="taw-3-20913">Table 3</xref>). The interaction of gap size, location and season significantly affected the REs on DEH, URE and INV (<xref ref-type="table" rid="taw-3-20913">Table 3</xref>).</p>
				<table-wrap id="taw-3-20913">
					<label>Table 3</label>
					<caption>
						<title>
							<italic>F</italic> values in repeated-measures analysis of variance of the effect of gap size, location, season and their interaction on RE on the enzyme activities in the <italic>Platycladus orientalis</italic> plantation in Zhaotuan Forest Farm of Xuzhou City, China.</title>
					</caption>
					<table>
						<colgroup>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center">Effect</th>
								<th align="center">df</th>
								<th align="center">RE on PER</th>
								<th align="center">RE on DEH</th>
								<th align="center">RE on URE</th>
								<th align="center">RE on INV</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Gap size</td>
								<td align="center">2</td>
								<td align="center">0.15</td>
								<td align="center">0.385</td>
								<td align="center">9.472**</td>
								<td align="center">1.37</td>
							</tr>
							<tr>
								<td align="center">Location</td>
								<td align="center">2</td>
								<td align="center">0.38</td>
								<td align="center">&lt;0.001</td>
								<td align="center">0.802</td>
								<td align="center">0.032</td>
							</tr>
							<tr>
								<td align="center">Season</td>
								<td align="center">3</td>
								<td align="center">2.413</td>
								<td align="center">9.173**</td>
								<td align="center">0.01</td>
								<td align="center">2.376</td>
							</tr>
							<tr>
								<td align="center">Gap size &#xd7; Location</td>
								<td align="center">4</td>
								<td align="center">0.193</td>
								<td align="center">0.723</td>
								<td align="center">0.69</td>
								<td align="center">2.925</td>
							</tr>
							<tr>
								<td align="center">Gap size &#xd7; Season</td>
								<td align="center">6</td>
								<td align="center">0.190</td>
								<td align="center">3.728</td>
								<td align="center">12.184***</td>
								<td align="center">3.272</td>
							</tr>
							<tr>
								<td align="center">Location &#xd7; Season</td>
								<td align="center">6</td>
								<td align="center">0.398</td>
								<td align="center">0.512</td>
								<td align="center">0.589</td>
								<td align="center">0.072</td>
							</tr>
							<tr>
								<td align="center">Gap size &#xd7; Location &#xd7; Season</td>
								<td align="center">12</td>
								<td align="center">3.435</td>
								<td align="center">15.601***</td>
								<td align="center">10.217**</td>
								<td align="center">5.446*</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="twf-3-20913">
							<p>Note: <italic>F</italic> values are reported with <italic>p</italic> values indicated as follows: * <italic>p</italic>&lt;0.05; ** <italic>p</italic>&lt;0.01; *** <italic>p</italic>&lt;0.001. RE: rhizosphere effect; PER: peroxidase; URE: urease; DEH: dehydrogenase; INV: invertase.</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</sec>
			<sec id="sec-3.4-20913">
				<title>Correlation analysis</title>
				<p>We can observe the correlations between RE of soil enzyme activities and soil environmental factors in bulk and rhizosphere soil, respectively (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). The correlation between RE on PER and ST was extremely significantly positive (<italic>p</italic>&lt;0.01), and the correlation between RE on DEH and PAR and ST was significantly positive (<italic>p</italic>&lt;0.05) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). In bulk soil, RE on PER was significantly positively correlated with NH<sub>4</sub>
					<sup>+</sup>-N, MBC, and MBN (<italic>p</italic>&lt;0.05) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). Conversely, RE of DEH, URE, and INV were extremely significantly negatively correlated with TC and TN (<italic>p</italic>&lt;0.01), while they were significantly negatively correlated with MBN (<italic>p</italic>&lt;0.05) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). Rhizosphere effects on URE and INV were highly significantly negatively correlated with SOC (<italic>p</italic>&lt;0.01) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). Additionally, significantly negative correlations were found among various soil properties and RE of enzyme activities, such as between NO<sub>3</sub>
					<sup>&#x2212;</sup>-N and RE on INV and NH<sub>4</sub>
					<sup>+</sup>-N and RE on DEH and URE (<italic>p</italic>&lt;0.01) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). In rhizosphere soil, RE on PER revealed extremely significantly positive correlations with NH<sub>4</sub>
					<sup>+</sup>-N and MBC (<italic>p</italic>&lt;0.01) and was significantly positively correlated with MBN (<italic>p</italic>&lt;0.05) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). Furthermore, RE on URE demonstrated extremely significantly positively correlations with MBN (<italic>p</italic>&lt;0.01) and was significantly positively correlated with NH<sub>4</sub>
					<sup>+</sup>-N and MBC (<italic>p</italic>&lt;0.05) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). Rhizosphere effect on INV was significantly positively correlated with WC (<italic>p</italic>&lt;0.05) (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). However, correlation between RE on DEH and any factor in the rhizosphere soil is minor (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>).</p>
				<table-wrap id="taw-4-20913">
					<label>Table 4</label>
					<caption>
						<title>Pearson correlations between rhizosphere effect of enzymatic activity and environmental factors and soil physicochemical properties in rhizosphere and bulk soil in the <italic>Platycladus orientalis</italic> plantation in Zhaotuan Forest Farm of Xuzhou City, China.</title>
					</caption>
					<table>
						<colgroup>
							<col span="2"/>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center" colspan="2">Rhizosphere effect </th>
								<th align="center">PER</th>
								<th align="center">DEH</th>
								<th align="center">URE</th>
								<th align="center">INV</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center" rowspan="2">Environmental factors</td>
								<td align="center">PAR</td>
								<td align="center">ns</td>
								<td align="center">0.18*</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">ST</td>
								<td align="center">0.25**</td>
								<td align="center">0.19*</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center" rowspan="8">Bulk Soil</td>
								<td align="center">WC</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">-0.22*</td>
							</tr>
							<tr>
								<td align="center">TC</td>
								<td align="center">ns</td>
								<td align="center">-0.3**</td>
								<td align="center">-0.38**</td>
								<td align="center">-0.36**</td>
							</tr>
							<tr>
								<td align="center">TN</td>
								<td align="center">ns</td>
								<td align="center">-0.3**</td>
								<td align="center">-0.38**</td>
								<td align="center">-0.37**</td>
							</tr>
							<tr>
								<td align="center">SOC</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">-0.36**</td>
								<td align="center">-0.32**</td>
							</tr>
							<tr>
								<td align="center">MBC</td>
								<td align="center">0.3*</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">MBN</td>
								<td align="center">0.23*</td>
								<td align="center">-0.23*</td>
								<td align="center">-0.21*</td>
								<td align="center">-0.21*</td>
							</tr>
							<tr>
								<td align="center">NO<sub>3</sub>
									<sup>-</sup>-N</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">-0.32**</td>
							</tr>
							<tr>
								<td align="center">NH<sub>4</sub>
									<sup>+</sup>-N</td>
								<td align="center">0.23*</td>
								<td align="center">-0.19*</td>
								<td align="center">-0.27**</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center" rowspan="8">Rhizosphere Soil</td>
								<td align="center">WC</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">0.22*</td>
							</tr>
							<tr>
								<td align="center">TC</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">TN</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">SOC</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">MBC</td>
								<td align="center">0.32**</td>
								<td align="center">ns</td>
								<td align="center">0.23*</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">MBN</td>
								<td align="center">0.19*</td>
								<td align="center">ns</td>
								<td align="center">0.25**</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">NO<sub>3</sub>
									<sup>-</sup>-N</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
								<td align="center">ns</td>
							</tr>
							<tr>
								<td align="center">NH<sub>4</sub>
									<sup>+</sup>-N</td>
								<td align="center">0.34**</td>
								<td align="center">ns</td>
								<td align="center">0.22*</td>
								<td align="center">ns</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="twf-4-20913">
							<p>Note: * Significant correlations (<italic>p</italic>&lt;0.05). ** Extremely significant correlations (<italic>p</italic>&lt;0.01). ns: non-significant. PAR: photosynthetically active radiation; ST: soil temperature; WC: water content; TC: total carbon; TN: total nitrogen; SOC: soluble organic carbon; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; PER: peroxidase; URE: urease; DEH: dehydrogenase; INV: invertase.</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</sec>
		</sec>
		<sec id="sec-4-20913" sec-type="discussion">
			<title>Discussion</title>
			<p>The RE on PER demonstrated a consistent positive trend across most seasons and sampling locations (<xref ref-type="fig" rid="fig-8-20913">Figure 8</xref>), showing a highly significant positive correlation with ST as indicated in <xref ref-type="table" rid="taw-4-20913">Table 4</xref>. This finding suggests that the establishment of forest gaps had a marked stimulating effect on PER activity in the rhizosphere soil, with PER activity being greater sensitivity to fluctuations in soil temperature (<xref ref-type="bibr" rid="ref-7-20913">Fei et al., 2023</xref>), particularly in warmer months (August). Furthermore, MBC and MBN in both bulk and rhizosphere soils exhibited positive correlations with the RE on PER (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>), with notable increases at the M2 and S2 locations in August (<xref ref-type="fig" rid="fig-8-20913">Figure 8</xref>). This suggests that M and S gap sizes may create conditions that enhance microbial activity, and that this increase could be linked to the greater availability of organic substrates for microbial oxidation and decomposition. The strong positive correlation between RE on PER and NH<sub>4</sub>
				<sup>+</sup>-N (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>) suggests that PER may play a critical role in nitrogen cycling in the rhizosphere, potentially facilitating the mineralization of organic matter into available forms. Furthermore, the significant correlation with MBC and MBN (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>) suggests that RE on PER may be involved in the breakdown of certain organic compounds, indirectly supporting microbial nutrient cycling in both rhizosphere and bulk soils. However, contrary to the hypothesis of our study, larger forest gaps and increased environmental heterogeneity did not result in enhanced RE of enzyme activity, nor did they extend range of influence on RE of enzyme activity. This result challenges our original expectations but aligns with findings from previous studies which suggested that smaller forest gaps are more conducive to higher soil enzyme activities (<xref ref-type="bibr" rid="ref-21-20913">Muscolo et al., 2007</xref>; <xref ref-type="bibr" rid="ref-38-20913">Yang et al. 2017</xref>). Additionally, <xref ref-type="bibr" rid="ref-27-20913">Sinsabaugh (2010)</xref> emphasized a positive association between PER activity and litter quantity, proposing that smaller gaps tend to have higher litter accumulation, which can enhance enzymatic activity due to increased substrate availability.</p>
			<p>Our results indicate that the RE on DEH were consistently lower than those of CK at most locations, with the exception of L2 in August (<xref ref-type="fig" rid="fig-9-20913">Figure 9</xref>). These findings suggest that the establishment of forest gaps significantly impacted DEH activity, particularly at M and S gap sizes, where environmental changes such as PAR and ST likely influenced microbial processes. Interestingly, the RE on DEH was more sensitive to fluctuations in PAR and ST than to soil factors in the rhizosphere (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>), pointing to bulk soil conditions as the primary drivers affecting DEH activity. Seasonal effects also played a key role in the RE on DEH (<xref ref-type="table" rid="taw-3-20913">Table 3</xref>), with significant variations observed across different months. These seasonal variations could explain the observed increases in the RE on DEH during June and August (<xref ref-type="fig" rid="fig-9-20913">Figure 9</xref>), as higher temperatures and increased PAR likely stimulated microbial activity, leading to enhanced enzyme activity. This is in line with findings by <xref ref-type="bibr" rid="ref-28-20913">Steinweg (2012</xref>), who highlighted the importance of temperature and moisture in regulating soil enzyme activities. Additionally, the negative correlations between RE on DEH and TC, TN, and MBN suggest that the availability of carbon and nitrogen might limit DEH activity under certain conditions (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>), possibly due to microbial competition for resources or imbalances in substrate availability.</p>
			<p>Our results revealed a significant negative correlation between the RE on URE and NH<sub>4</sub>
				<sup>+</sup>-N in bulk soil, while a positive correlation was observed in rhizosphere soil (<xref ref-type="table" rid="taw-3-20913">Table 3</xref>). This differential response suggests that, under favorable hydrothermal conditions, substrate availability, particularly nitrogen, may limit URE activity in bulk soil following the forest gap establishment (<xref ref-type="bibr" rid="ref-40-20913">Zuccarini et al., 2020</xref>, <xref ref-type="bibr" rid="ref-39-20913">2023</xref>). In comparison to the CK, the RE on URE showed an increasing trend in both L and S forest gaps in April and June, but declined in August and October (<xref ref-type="fig" rid="fig-10-20913">Figure 10</xref>). This temporal pattern highlights the greater sensitivity of URE activity to environmental changes in rhizosphere soils compared to bulk soils across different phases of the growing season (<xref ref-type="bibr" rid="ref-37-20913">Xu et al., 2024</xref>). Notably, both in the rhizosphere and bulk soils, the RE of URE exhibited significant positive correlations with MBN, aligning with the findings of <xref ref-type="bibr" rid="ref-11-20913">Fisher et al. (2017)</xref> and <xref ref-type="bibr" rid="ref-4-20913">Corstanje et al. (2007)</xref>, who emphasized the influence of microbial community composition on URE activity. These results further corroborate the observations by <xref ref-type="bibr" rid="ref-35-20913">Wang et al. (2019b)</xref>, who demonstrated the association between URE activity and soil nitrogen transformations, reinforcing the importance of microbial processes in mediating soil nitrogen cycling.</p>
			<p>The changes in the RE on INV closely mirrored those of URE in our study, showing similar seasonal and spatial patterns (<xref ref-type="fig" rid="fig-10-20913">Figure 10</xref> and <xref ref-type="fig" rid="fig-1-20913">11</xref>). In April, the RE on INV increased across all forest gap sizes (<xref ref-type="fig" rid="fig-11-20913">Figure 11</xref>), indicating that forest gap creation positively influenced INV activity during the early growing season. However, by August and October, the RE on INV declined, following a distinct temporal pattern (<xref ref-type="fig" rid="fig-11-20913">Figure 11</xref>). This suggests that forest gaps initially enhanced INV activity by promoting favorable conditions for microbial activity, including increased substrate availability and rising temperatures in the rhizosphere soil (<xref ref-type="bibr" rid="ref-39-20913">Zuccarini et al., 2023</xref>). In the later stage of the growing season, the decomposition of litter likely contributed substantial nutrients inputs to the bulk soil, potentially overshadowing the effects of forest gaps on INV activity in the rhizosphere. This shift could explain why INV activity in bulk soils was markedly higher than in the rhizosphere during the later months, and why the RE on INV was lower than CK in these periods. This finding aligns with previous research, which observed that INV activity was more responsive to changes in soil conditions during the early growing season, with a subsequent decline as the soil nutrient dynamics shifted, as seen in studies on organic mulching (<xref ref-type="bibr" rid="ref-30-20913">Sun et al., 2021b</xref>) and straw returning practices (<xref ref-type="bibr" rid="ref-36-20913">Wu et al., 2020</xref>). Additionally, in bulk soils, the RE on INV was negatively correlated with WC, TC, TN, SOC, MBN, and NO<sub>3</sub>
				<sup>&#x2212;</sup>-N (<xref ref-type="table" rid="taw-4-20913">Table 4</xref>). These results are consistent with the findings of <xref ref-type="bibr" rid="ref-38-20913">Yang et al. (2017)</xref>, who noted a decrease in INV activity in response to NH<sub>4</sub>
				<sup>+</sup>-N additions during the early stages of plant growth, followed by an increase as the season progressed. Our study further emphasizes the complex interactions between soil nitrogen, carbon, and microbial activity, as these factors influence enzyme activity in response to varying forest gap sizes and the associated environmental changes.</p>
			<p>In addition to the environmental factors discussed above, plant fine roots and microorganisms play a crucial role in shaping both bulk and rhizosphere soils (<xref ref-type="bibr" rid="ref-30-20913">Sun et al., 2021b</xref>). Plants contribute organic substrates to the rhizosphere through litter deposition and root exudates, which regulate nutrient composition, enhance microbial habitat quality, and stimulate microbial abundance and metabolic activities (<xref ref-type="bibr" rid="ref-26-20913">Sasse et al., 2018</xref>). The establishment of forest gaps significantly impacts these dynamics by altering soil properties and microbial communities. Given the pivotal role of plants in shaping the rhizosphere environment, the observed changes in enzyme activity following forest gap establishment can be attributed, in part, to the direct and indirect effects of plant root activity and microbial interactions on the soil environment.</p>
		</sec>
		<sec id="sec-5-20913" sec-type="conclusions">
			<title>Conclusions</title>
			<p>During the growing season, forest gaps initially stimulated the RE of PER, URE, and INV, but these effects diminished as the season progressed, while the RE of DEH consistently decreased in both June and August. Additionally, forest gaps of L and S sizes influenced the RE of enzyme activity, with L gaps having the most pronounced impact. This study identified that the RE of soil enzyme activity did not increase with the enlargement of forest gap size. However, in L forest gaps, its impact range was more extensive, aligning with the assumptions made in Hypothesis 2 outlined earlier in this text.</p>
		</sec>
	</body>
	<back>
		<sec id="sec-6-20913" sec-type="data-availability">
			<title>Data availability</title>
			<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
		</sec>
		<ack>
			<title>Acknowledgements</title>
			<p>We would like to thank Liu Chang, Yan Xinli, and Zhao Jiahao for their valuable contributions to the experiments, including data collection, technical support, and fieldwork assistance. Although they are not included in the final authors&#x2019; list, their support was essential to this study.</p>
		</ack>
		<sec id="sec-7-20913" sec-type="transparency-statement">
			<title>Competing interests</title>
			<p>The authors have declared that no competing interests exist.</p>
		</sec>
		<sec id="sec-8-20913" sec-type="author-contributions">
			<title>Authors&#x2019; contributions</title>
			<p>
				<bold>Fei Fei:</bold> Data curation, Investigation, Writing &#x2013; original draft, preparation, Visualization. <bold>Qingwei Guan:</bold> Conceptualization, Methodology, Writing &#x2013; review &amp; editing, Supervision.</p>
			</sec>
		<sec id="sec-9-20913" sec-type="apoyo">
			<title>Funding</title>
			<table-wrap id="taw-5-20913">
				<table>
					<colgroup>
						<col/>
						<col/>
					</colgroup>
					<thead>
						<tr>
							<th align="left">Funding agencies/institutions</th>
							<th align="center">Project / Grant</th>
						</tr>
					</thead>
					<tbody>
						<tr>
							<td align="left">Priority Academic Program Development of Jiangsu Higher Education Institutions</td>
							<td align="center">PAPD</td>
						</tr>
						<tr>
							<td align="left">Jiangsu emission peak carbon neutrality technology innovation project</td>
							<td align="center">BE2022420</td>
						</tr>
					</tbody>
				</table>
			</table-wrap>
		</sec>
		<fn-group>
			<title>Notas</title>
			<fn fn-type="other" id="fn-1-20913">
				<p>The translation of the title, abstract, and keywords from the original version in English to Spanish has been generated using OpenAI, ChatGPT GPT-4o mini (2025).</p>
				<p xml:lang="es">La traducci&#xf3;n al espa&#xf1;ol del t&#xed;tulo, resumen y palabras clave de la versi&#xf3;n original en ingl&#xe9;s ha sido generada utilizando OpenAI., ChatGPT GPT-4o mini (2025).</p>
			</fn>
		</fn-group>
		<ref-list id="refl-1-20913">
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