國立臺灣大學生命科學院生態學與演化生物學研究所 博士論文
Institute of Ecology and Evolutionary Biology College of Life Science
National Taiwan University Doctoral Dissertation
臺灣山地雲霧森林種子雨與樹苗之時空動態
Spatiotemporal Dynamics of Seed Rain and Seedling in a Montane Cloud Forest, Taiwan
翁其羽 Chi-Yu Weng
指導教授：謝志豪 博士 Advisor: Chih-hao Hsieh, Ph.D.
中華民國 106 年 7 月 July 2017
致謝
相隔在靜宜方濟樓寫生態碩論致謝之時，轉瞬又已八年。還記得當時光栩才 三歲，埋怨我不就是前面寫回來，後面寫回去即好！眼前他已將小學畢業，懂事 許多，即便我再忙再累，沒法陪他，也不怨言。他對我說，爸比你常說不能放棄，
怎麼你自己放棄不唸完？那時我正當休學工作期滿，幼兒宇澍還是天真爛漫年紀。
家庭、工作、志趣、前景，無頭緒的焦慮沉沉壓著不惑之年的我。我豈能無惑？
沉默的當下，體認該釋然的不是一笑放手，而是讓這刻骨銘心的人生後黃金十年，
如夢楠溪，真真切切勾勒一個句點。
博士之路當真孤獨，它輾磨你無謂的浪漫，冷視反覆的心志，迫近以巨觀的 龐然，消頹以枝微的無知。揚棄那些溢美的牽扯吧！數據、歧異、bug、因果、
假說，文獻海；格局、棲位、變異、相依，程式迷宮，顯著的聖杯。在發散或收 斂的輪迴，幾乎是把過去形相中的自己打掉重練，低頭去撿那實際支撐的骨肉。
在這過程中，衷心感激自己有妻子孩子、父母、親人們的陪伴，把血溫了，無數 孤醒的夜才足夠溫度。我也十分有幸，能作為業師謝志豪教授的博班學生，接受 他的指導。他對待學生視之為能獨立學習、解決問題的研究者，給予全然自由組 織、架構的空間，卻也積極地提供資源協助，務真務好地鞭策進程。面對我龐雜 的文稿，不厭其煩地往來評論，詰以邏輯辯證，揭示這生態學中純粹砥礪的科學 理性。生態科學路上，多遇貴人；謝長富、楊國禎、蘇夢淮三位老師從我生態碩 士到博士這麼多年以來，不論在求學或在計畫、野調中，都給予他們實驗室中最 大的支持，點滴皆在心頭。博士資格與審查能得到林宜靜、宋國彰、孫義方三位 老師們悉心指教，諮詢過程中對森林生態啟發受益良多。在野調、分析、投稿上，
請益於林笈克、林宗以、張楊家豪等多位學長，時有交流。而楠溪野調五年多，
特別感謝欣一、柏愷在植物、動物領域上的專業協助；受到太多朋友熱情參與幫 忙，無法一一致謝。我們同在楠溪啟蒙，這正是最大的福緣。
種子等待時機萌發小苗；小兒呱呱墮地，今自在歡躍。我們永遠不會知道句 點。五官打開，理性磨焠，天命如啟。若有笑淚，你仍有惑，仍在這因緣的雲霧 林中。山林空響，蹊徑皆謎，”或許是誤認一隻長鬃山羊，將從崖壁飛躍而下”。
摘要
在樹木更新之長期過程中，樹木種子傳播和樹苗動態影響了森林群落構建，因此 對於維持森林物種共存至為重要。但由於被掠食與傳播限制，種子轉換至樹苗存 在落差，擴大了樹種補充更新在時空上的變異。其中，種子不一定能到達適合樹 苗生存的生育地，造就樹苗分布與環境因子的微弱相關。再者，樹苗和成樹組成 間也欠缺一致性，這些落差模糊了樹苗階段的更新棲位在森林群落構建中所扮演 的功能。
為解決此問題，首須了解樹苗群聚如何對應微環境中的局部異質性。本研究提
出地被植物因同時與樹苗和微環境互動，可做為樹苗棲地的空間指標。以2009 年
在楠溪森林動態樣區（23°27'40.7" N, 120°54'22.2" E，地處台灣山地雲霧森林）所 調查的樹苗與地被植物進行分析，結果顯示地被植物對於解釋樹苗群聚的空間變 異貢獻度較大，且能用於區分空間上的更新區塊。不同的更新區塊間，較高草本 地被植物對樹苗豐度、歧異度以及和成樹組成關聯帶來不同程度的鄰伴效應。
接續三年（2010-2013）的樹苗動態監測也證實了高草鄰伴可帶來對樹苗的庇護 效應，並和樹苗物種特性（如耐蔭性、萌發高度）共同作用，抵抗有蹄類動物植 食。這促使了在樹苗建立初期，因有蹄類動物植食之死亡率有高度種間異質性。
誠然有蹄類動物植食造成樹苗高死亡率，但並未破壞樹苗物種共存。植食致死率 之種間異質性，以及在突增的補充更新之後而來的密度依變致死，都抑制了優勢 樹苗豐度。藉由動態模型，也發現更新補充擾動（亦即加入了時間異質性），可 使樹苗稀少種避開優勢種樹苗引發的外顯競爭。
以小苗樣點附近的種子網進行五年的種子雨收集（2008–2009, 2010–2012），研 究更新補充動態中的時間異質性與其對種子轉換至樹苗間的影響。多數優勢樹種 每年週期性地繁殖，也相關於氣候上的季節性，但產出的種子和樹苗補充不論在 時空或豐度上都產生不一致。分析各物種在各樣點年間種子－樹苗轉換和各生物 與非生物因子的關聯，其具影響力之因子（如傳播前種子損失率、傳播限制、土 壤種子庫補充等）在不同種子傳播方式的物種間影響各異。這些研究顯示了在與 動物族群與鄰伴植物間的生物交互作用下，如何在樹種種子與樹苗所存在的動態 本質間扮演連結的角色，其重要性可提升在森林群落構建中的物種共存。
關鍵詞：傳播模式、山地雲霧森林、更新補充動態、樹苗建立、有蹄類動物植食
ABSTRACT
Seed dispersal and seedling dynamics are crucial for tree species in their long-term regeneration processes by affecting community assembly of a forest. However, the discordance in seed-to-seedling transition, which is due to loss by predation and dispersal limitation, constitutes a great spatiotemporal variation in recruitment of tree species.
Seeds are not guaranteed to be able to disperse to the microhabitats favorable to seedling establishment or survival; this results in weak association between seedling distributions and environmental factors. Moreover, the uncoupling between seedling and adult tree assemblages further obscures our understanding of the role of ecological niches specific to the seedling stage (regeneration niches) in forest community assembly.
To address the discrepancies at the seedling stage, detecting local heterogeneity in microenvironments, to which seedling assemblages respond, is a prerequisite. I propose that understory plants, which may interact with microenvironments and seedlings at local scale, can be a better spatial descriptor for regeneration habitats. To test this, a demographic survey for tree seedlings and understory plant assemblages was conducted in 2009 in the Nanhsi forest dynamics plot (23° 27'40.7" N, 120° 54'22.2" E), a montane cloud forest of Taiwan. I find that the understory plant spatial structure contributes most in explaining spatial variations of the seedling assemblages and facilitates the identification of patches of different regeneration habitats. Among these regeneration patches, the neighboring effects from tall herbs play an important role in affecting seedling density, diversity, and its coupling with conspecific adult trees.
A consecutive seedling dynamics survey for three years (2010–2013) provides further supporting evidences for the neighboring effects from tall herbs. The nurse-plant effect by tall herbs can co-operate with seedling species traits (shade-tolerance, and seedling
initial height) against ungulate herbivory, resulting in uneven species-specific mortality at an early stage of seedling establishment. Ungulate herbivory greatly caused seedling mortality here; however, it was not really detrimental to seedling species coexistence. The uneven species-specific mortality by herbivory and density-dependent mortality after bursts of recruitments suppress the abundant seedling species. Furthermore, by dynamic modelling, I also find that fluctuating recruitment, which increases temporal heterogeneity in recruitment dynamics, may prevent rare species from apparent competition from the abundant species.
The temporal heterogeneity in recruitment dynamics and the consequence of seed-to- seedling transition are studied using seed rains that had been collected in seed traps nearby the seedling sites for five years (2008–2009, 2010–2012). Most of the abundant tree species reproduced annually and periodically, correlating to the climatic seasonality. But the seed arrival uncoupled with recruitments in space, and in time, as well as in the level of species abundance. By analyzing the specific seed-to-seedling transition across years in each site associated with various biotic and abiotic factors, I find that the influential factors (such as the pre-dispersal loss of seeds, dispersal limitation, seed-bank buffering, etc.) had different impacts among tree species with different seed dispersers. My studies highlight the importance of bio-interactions with animals and neighboring plants that can interplay with the dynamic nature of seeds and seedlings from the dispersal, recruitment, to establishment stage, and thereby facilitate species coexistence in the forest community.
Keywords: Dispersal syndrome, montane cloud forest, recruitment dynamics, seedling establishment, ungulate herbivory
Index
摘要 ... iii ABSTRACT ...iv Index ...vi Chapter 1. Overviews of the spatiotemporal dynamics of seed rain and seedling in a
montane cloud forest ... 1 Chapter 2. Local neighborhood communities in the understory play a critical role by
affecting regeneration niches and subsequent community assembly in a
montane cloud forest ... 7 Chapter 3. Recruitment dynamics mediated by ungulate herbivory can affect species
coexistence for tree seedling assemblages ... 44 Chapter 4. Recruitments in the regeneration habitats depend on bio-interactions between animal dispersal and spatiotemporal dynamics of seed rain ... 81 Chapter 5. Conclusion ... 113 References ... 115
List of Tables
Table 2.1 Results of redundancy analysis (RDA) based on forward selection, showing significant factors explaining spatial variations in seedling assemblages. .... 32 Table 3.1. Changes in community patterns (seedling species diversity, density, and family-level composition) among years 2009, 2010, 2013, and among dynamic models with different percentage of death caused by herbivory. ... 69 Table S2.1 Results of redundancy analysis (RDA) based on forward selection, showing the understory tall herb species and adult tree species that were significant in explaining spatial variations of seedling assemblages. ... 37 Table S2.2 Biotic and abiotic properties for each spatial-structuring group (SSG) ... 38 Table S2.3 Indicator species for each spatial-structuring group (SSG) ... 39 Table S3.1 Species composition of tree seedlings, and the numbers of recruitments, survivals, deaths, deaths by herbivory, deaths by other causes, average of relative growth rate and frequency of occurrence in the Nanhsi forest dynamics plot. .. 74 Table S3.2 Results of mixed-effect models for seedling survival and growth. ... 76
Table S3.3 Results of mixed-effect models for leaf damages in the fence experiment. 76 Table S4.1 Seed rain and seedling recruitments in sampling sites surrounding the traps of
each taxon, along with its dispersal mode and seed-bank trait. ... 109
Table S4.2 Results of mixed-effect models for the three regeneration habitat types: the subsidized sites, established sites, and seed-arriving sites. ... 110
Table S4.3 Results of mixed-effect models for the persistence, colonization, and discontinuity of a regeneration habitat across year cohorts. ... 110
List of Figures
Fig. 1.1 Schematic diagram of the recruitment dynamics in a forest.. ... 5Fig. 1.2 The geographic map of Nanhsi forest dynamic plot.. ... 6
Fig. 1.3 Monthly average temperature and rainfall in Nanhsi plot... 6
Fig. 2.1 Field survey of this study in Nanhsi forest dynamics plot.. ... 33
Fig. 2.2 Understory plant spatial structure in relation to (a) tall herb cover, (b) understory species diversity, (c) seedling species diversity, and (d) seedling density. (e) Species distribution with respect to both the understory plant spatial structure and topographic factors. ... 34
Fig. 2.3 Interspecific codispersion map on the plane of x-y lags... 35
Fig. 2.4 Comparisons of (a) seedling density and Hill’s N1 diversity index of (b) tree seedlings and (c) understory plants among five spatial-structuring groups. ... 36
Fig. 3.1 Significant factors affecting on (a) leaf damages of seedlings and understory plants by herbivores in unfenced and fenced (control) groups; (b) 3-yr seedling survival (death event as response) and growth rate. ... 70
Fig. 3.2 Comparisons of seedling deaths caused by herbivory relative to other causes (a) Percentage of death causes for seedling species and (b) distribution of initial height and survival days of the dead seedlings. ... 71
Fig. 3.3 Observed seedling dynamics: (a) Overall recruit rate, death rate, and increase rate; (b) Death rate of recruits by herbivory and other death causes. ... 72
Fig. 3.4 A dynamic model simulating seedling assemblage dynamics of (a) abundant species populations and (b) changes of species abundances compared to the initial value (named species abundance ratio). ... 73
Fig. 4.1 Reproductive phenology (2008–2012) and recruitment dynamics (2010–2012) of tree species in Nanhsi plot. ... 105 Fig. 4.2 The relationships between seeds in traps and (a) diaspore fragments; (b) fruit appendages for each year cohort and dispersal mode. ... 106 Fig. 4.3 Characterizing regeneration habitat types with (a) the distribution of sites among species, and (b) the nearest possible dispersal distance to nearby adult trees. . 107 Fig. 4.4 Significant factors affecting (a) the seed-to-seedling transition in the species-
specific regeneration habitat types and (b) the consistency of the regeneration habitats across year cohorts. ... 108 Fig. S2.1 Flowchart of constructing (a) neighborhood factors, (b) understory plant spatial structure, and determining (c) candidate neighboring species. ... 41 Fig. S2.2 Principal coordinates of neighbor matrices (PCNM) variables in joining variation partitioning. (a) PCNM variables (as explanatory variables) whose associated variance (in y-axis) of each component (in x-axis) conditioning on topographic factors. Results of variation partitioning are expressed in adjusted-R2 (%), for (b) understory plants and (c) the seedling assemblage. ... 42 Fig. S2.3 Spatial correlograms with Moran’s I coefficients for (a) understory plant spatial structure projected on the first redundancy analysis (RDA) axis (uRDA1), and neighborhood factors (b) tall herb cover, (c) tree density, (d) shrub basal area (BA), (e) overstory BA and their distribution map across ten transects in Nanhsi plot. ... 43 Fig. S3.1 Field survey of this study in Nanhsi forest dynamics plot. ... 77 Fig. S3.2 Biplot of the principal component analysis for the environmental factors. .... 78 Fig. S3.3 Flowchart explaining the dynamic model simulating seeding assemblage dynamics by controlling the percentage of death by herbivory. ... 79 Fig. S3.4 Species dynamics simulated by the dynamic model for examining coexistence of tree seedling species under different percentages of death by herbivory. ... 80 Fig. S4.1 Field survey of this study in the Nanhsi forest dynamics plot. ... 111 Fig. S4.2 Community reproductive phenology with monthly species abundance for each phenological pattern, including (a) flowering and fruiting, as well as (b) fruit appendage and diaspore fragment. (c) The abundance of flowering and fruiting species are related to the local climatic factors. ... 112
Chapter 1.
Overviews of the spatiotemporal dynamics of seed rain and seedling in a montane cloud forest
Introduction
The importance of recruitment dynamics in tree regeneration through affecting forest community assembly had been highlighted (Clark et al. 1999; Connell & Green 2000;
Norden et al. 2007; Norden et al. 2009b). In recruitment dynamics, plant individuals in each intermediate stage, from seed production, dispersal, germination, to seedling establishment (Fig. 1.1), interact with surrounding abiotic and biotic factors, which results in great spatiotemporal variation of plant community (De Steven 1994; Nathan & Muller- Landau 2000; Paine and Harms 2009). Spatiotemporal variations in recruitment dynamics cause uncoupling in the seed-to-seedling transition (García et al. 2005), uncoupling between seedling distribution and microenvironments (Pinto & Macdougall 2010), as well as uncoupling between seedling and adult assemblages (Maltez-Mouro et al. 2007;
Pérez-Ramos & Marañón 2012). These discrepancies obscure our understanding of underlying mechanisms in recruitment dynamics that may contribute to species coexistence in forest community assembly (Clark et al. 1999; Comita et al. 2007;
Usinowicz et al. 2012; Joseph Wright et al. 2016).
In my dissertation research, three studies were proposed to explore the causes and consequences of these discrepancies that occurred at each stage in recruitment dynamics.
(1) A demographic survey for assemblages of understory plants and tree seedlings was conducted in a montane cloud forest, Taiwan, in 2009. I proposed that the discrepancy which is due to the weak association between seedling distribution and environmental factors can be to some extent explained by the spatial structure of understory plants.
Understory plants are the nearest neighbors surrounding tree seedlings, and they can form close spatial relationships with seedling assemblages. Such spatial relationships help us identify patches of regeneration habitats where seedlings established. In these regeneration patches, the uncoupling (or coupling) between seedling and adult assemblages revealed how regeneration niches (Grubb 1977) are shaped by neighboring understory plants and can potentially contribute to forest community assembly.
(2) A 3-year seedling dynamics survey (2010–2013) was conducted to explore how recruitment dynamics can affect species coexistence in a forest under intense ungulate herbivory. A best-known mechanism that herbivory affects species coexistence of tree seedlings is negative density-dependence driven by specialist natural enemies (Comita et al., 2014; Norghauer and Newbery, 2014). I argued that the rare species of seedlings may not be favored if the herbivory is caused by non-specialist herbivores, such as ungulates.
I proposed that seedling species may interact with neighboring understory plants (nurse- plant effects), causing an uneven species-specific mortality against ungulate herbivory. I also proposed a dynamic model to test if the uneven species-specific mortality can facilitate species coexistence under different herbivory pressure.
(3) A 5-year seed rain survey (2008–2009, 2010–2012), by associating the dispersion of seed rain in seed traps (Fig. 1.1) with nearby seedling quadrats in these two year cohorts, was used to investigate the seed-to-seedling transition in the recruitment dynamics. The uncoupling in seed-to-seedling transition may be related to both the seed availability and animal dispersal, such that the evaluation for pre-dispersal seed predation (Fig. 1.1) could help to address the discrepancy from seed to seedling. Furthermore, I examined whether the seed-to-seedling transition in a regeneration habitat is consistent across different year cohorts. By testing this, how the seed-to-seedling transition for tree species may respond to a variable microenvironment can be explored.
These proposed studies were all conducted in in the Nanhsi forest dynamics plot, a montane cloud forest at a bio-reserve of Yushan National Park in the middle of Central Range of Taiwan. Almost all native herbivorous or frugivorous mammals had been found in this plot (Hwang and Chian, 2007), such as Formosan Macaque, Formosan Muntjacs, Formosan Sambars, Formosan Serows, etc. These mammals could play key roles in seed dispersal, seed predation or seedling herbivory. Such plant-animal interactions which are responsible for the discrepancies between fundamental and realized ecological niches (Pulliam 2000) may help bridge the uncoupling relationships from seed to seedling stages in recruitment dynamics. Thereby, my dissertation research focused on how recruitment dynamics in this plot was affected by the plant-animal interactions and the consequences on species coexistence in the regeneration processes of trees.
Moreover, I stressed special importance on the comparisons of seedling and seed rain dynamics before and after typhoon Morakot in 2009. Typhoon Morakot brought extreme rainfall, caused landslides and gaps, and changed the flora in this plot. The changes in the recruitment dynamics before and after typhoon Morakot may have some implications for the tree regeneration in response to a variable environment. However, such regeneration patterns in a montane cloud forest are still poor known (Ramírez-Bamonde et al. 2005;
Gutiérrez et al. 2008) although several montane cloud forests have become endangered ecosystems nowadays (Ponce-Reyes et al. 2012).
Study area
The studies in my dissertation research were conducted in the Nanhsi forest dynamics plot, with an area of 8.37 ha and elevations ranging from 1,960 to 2,060 m (Fig. 1.2). The plot is located in a bio-reserve of the Yushan National Park, mid-southern Taiwan
(23°27'40.7" N, 120°54'22.2" E). The climate is characterized by daily regular cloud immersion and seasonal precipitation. Typhoons and southwest monsoon can cause extremely heavy rainfall (annual average 3,011 mm in 2008–2012, Fig. 1.3). Typhoon Morakot (August, 2009) caused landslide here, and created gaps and a decline of canopy cover. During our survey years, the mean annual rainfall was 5,554 mm (2008–2009), and 4,159 mm (2010–2012). The monthly and yearly variations in rainfall were great.
The annual mean temperature was 11.4 °C, with a January mean of 6.3 °C and July mean of 15 °C (2008–2012).
Vegetation
The forest vegetation in this plot is dominated by evergreen broadleaved tree species, which is equivalent to the Quercus montane evergreen broadleaved cloud forest defined in Li et al. (2013). According to a tree demographic census in 2006 (Yang et al. 2008), there were 18,766 trees (with a diameter at breast height (DBH) ≥ 1 cm) belonging to 27 families, 49 genera, and 65 species. The dominant families were Lauraceae, Fagaceae, and Theaceae (whose members of Eurya spp. were moved into the Pentaphylacaceae in the Angiosperm Phylogeny Group system). The top five dominant species were Castanopsis cuspidata (Castanopsis carlesii as denoted by Yang et al., 2008), Machilus japonica, Litsea acuminata, Quercus stenophylloides, and Lithocarpus kawakamii. Forest vegetation types identified in this plot included two Lauraceae-dominated forest types (the M. japonica type and the M. japonica-C. cuspidata type), one Theaceae–Fagaceae forest type (the Schima superba-C. cuspidata type), and one deciduous forest type (the Alnus formosana type).
Figures
Fig. 1.1 Schematic diagram of the recruitment dynamics in a forest. Every intermediate stage (from pre-dispersal to seedling establishment), including deaths at each stage (in solid wide arrow), constitutes the recruitment dynamics in tree regeneration. The seedling recruitment includes three inputs of sources before germination: dispersion from seed rain, post-deposition during post-dispersal, and soil seed bank. After germination, the seedling establishment still exhibits a spatiotemporal dynamics of survival and growth, in relation to the bio-interactions (e.g., herbivory) and microenvironments. The life-history stages in tree regeneration processes, from seedling to adult, are linked using dashed arrows.
Fig. 1.2 The geographic map of Nanhsi forest dynamic plot. This plot is near the 9.5 K to 10.0 K of Nanzihsianhsi forest road, at 23°27'40.7" N, 120°54'22.2" E, in a bio-reserve of the Yushan National Park, mid-southern Taiwan. This plot is about 5 km southwest away from Yushan Main Peak, and is in the upstream basin of Nanzihsian River.
Fig. 1.3 Monthly average temperature and rainfall in Nanhsi plot. The mean annual temperature is 11.4 °C, and the mean annual rainfall is 3011 mm (1998–2012).
Chapter 2. Local neighborhood communities in the understory play a critical role by affecting regeneration niches and subsequent
community assembly in a montane cloud forest
Abstract
Regeneration niches for tree seedlings are defined as possessing the biotic and abiotic requirements to successfully replace adults. However, two critical discrepancies obscure our understanding of the role of regeneration niches in forest community assembly: a weak association between seedling assemblages and environmental factors, and an uncoupling between seedling and adult-tree assemblages. However, understory plants, which may interact with microenvironments and seedlings, may be a better spatial descriptor of regeneration habitats. To test this, the spatial variations of seedling assemblages in a montane cloud forest of Taiwan were analyzed in terms of their association with neighborhood assemblages of understory plants, the shrub layer, and overstory trees, as well as environmental variables. We found that the understory plant spatial structure contributed most in explaining spatial variations of the seedling assemblages (especially for widespread Lauraceae and patchy Fagaceae) and facilitated the identification of patches of different regeneration habitats for specific seedling assemblages. Moreover, among these regeneration patches, tall herbs affected seedling density and diversity differently. We found segregation between tall herbs and Lauraceae seedlings, indicating that tall herbs shape seedling assemblages and uncouple the association between seedling and adult stages. However, positive covariations between seedlings/tall herbs and between seedlings/adults were found for Fagaceae and Pentaphylacaceae in different regeneration patches, suggesting that positive, neighboring
effects and dispersal limitation may cause the patchy distribution of seedling assemblages and affect their coupling with adults. Thus, the understory plant spatial structure shapes seedling assemblages and provides a better link to spatial associations with regeneration habitats.
Keywords
Montane cloud forest; regeneration niche; seedling establishment; species codispersion; understory community
Introduction
The role of ecological niches specific to the tree regeneration stage (i.e., regeneration niches) in forest community assembly has been debated (Brokaw and Busing 2000; Metz 2012). A regeneration niche at the seedling stage is defined as possessing the biotic and abiotic requirements that provide tree seedlings with a greater chance of replacing adult trees (sensu Grubb 1977). Tree seedlings (especially shade-tolerant tree species) can wait for chances to replace dead trees when the canopy opens. Therefore, seedling assemblages, known as seedling banks, which live in the forest understory, have the potential to contribute to canopy composition (Antos et al. 2005). However, the dynamic nature of tree seedlings (such as their high mortality during development), which arises from niche and stochastic processes (De Steven 1994; Brokaw and Busing 2000; Paine and Harms 2009; Pinto & Macdougall 2010), can cause an uncoupling or weak association in the community composition between the seedling and adult stages (Maltez-Mouro et al. 2007;
Pérez-Ramos & Marañón 2012). Moreover, regeneration niches are related directly to the biotic and abiotic factors in microhabitats (hereafter, “regeneration habitats”) where seedlings are established. However, regeneration niches are usually assessed only by analyzing the species association with abiotic factors (Collins and Good 1987; Baraloto and Goldberg 2004; Poorter 2007). In fact, previous studies found difficulties linking seedling distribution with environmental factors (Baraloto and Goldberg 2004; Comita et al. 2007; Kanagaraj et al. 2011; Bai et al. 2012; Masaki et al. 2015). These two discrepancies, i.e., the weak association of tree seedlings with adult assemblages and abiotic environments, highlight the difficulties in assessing regeneration niches.
A possible way address these issues is to detect local heterogeneity in microenvironments, to which seedling assemblages respond (Gómez-aparicio et al. 2005),
such as by identifying different patches of regeneration habitats (hereafter, “regeneration patches”) (Emborg 1998; Caquet et al. 2010; Karami et al. 2012). Identifying regeneration patches that are associated with specific biotic and abiotic conditions in patchy microhabitats can help clarify how regeneration niches affect species assembly therein.
Past studies usually characterized regeneration patches using forest gaps (Grubb 1977;
Emborg 1998; Hubbell et al. 1999; Brokaw and Busing 2000), some critical neighborhood interactions (e.g., nurse-plant effects from the neighborhood) (Smit and Vandenberghe 2007; Caccia et al. 2009), soil properties (Chávez and Macdonald 2010), or other topographic structuring (Albrecht and McCarthy 2009). Besides environmental factors, the most emphasized process is forest gap dynamics, because forest gaps, which create space or light availability in the understory, can form regeneration patches. However, the spatial heterogeneity introduced by gaps cannot be used to predict regeneration niches in tropical forests because the shade-tolerant seedling bank, which has survived and grown into its next life-history stage, is established prior to gap formation (Hubbell et al. 1999;
Wright 2002). In addition, gaps are often occupied by chance colonizers, rather than by the best adapted species (Brokaw and Busing 2000). Dispersal limitation can further restrict tree species from reaching such optimal habitats (Pinto and Macdougall 2010). In contrast, in a non-preferred habitat, sub-populations of tree seedling species still can be maintained and disperse from neighborhoods (Zuidema et al. 2010).
Apart from these environmental factors, we believe one important factor affecting regeneration niches is the neighborhood communities that inhabit the forest understory and surround tree seedlings. Neighborhood communities, including understory plants, and trees in the upper forest strata (i.e., the shrub-layer or overstory trees occupying space on the forest ground), may have positive or negative effects on seedling assemblages, because neighborhood communities interact with microenvironments and residents (or
occupy space) in the forest understory. Thus, the potential that neighborhood communities can be better surrogates as spatial descriptors of regeneration habitats is worth exploring. For example, neighboring trees or herbs in the understory can produce dense shading, compete for nutrients, or cause facilitative interactions, such as nurse-plant effects (George and Bazzaz 1999; Beckage et al. 2000; Smit and Vandenberghe 2007;
Brooker et al. 2008; Royo and Carson 2008). Considering the relationship between adult tress and seedlings, while dispersal limitation can cause clumped seedlings and, therefore, spatial patterns of positive covariations between seedlings and adults, negative density dependency may compensate this effect (Wright 2002; Queenborough et al. 2007).
Moreover, neighborhood communities can promote spatial heterogeneity by changing microenvironments and forming patches in the understory, e.g., by changing canopy openness, stemflow, and the physical or chemical properties of substrates such as litter cover and mineral deposition (Beatty 1984; Crozier & Boerner 1984; Chávez &
Macdonald 2010; Gazol & Ibáñez 2010; Mejía-Domínguez et al. 2011). Therefore, patches in the understory may be affected by the properties of neighborhood communities, including canopy cover, stem densities, basal areas, or bio-interactions from clumped herbs, and species composition.
In this study, we aimed to address these problems using data in a montane cloud forest of Taiwan. We expected that both neighborhood communities and environmental factors would jointly explain the spatial variations of seedling assemblages, thereby helping to explain the weak association between seedling assemblages and environmental factors.
We hypothesized that among neighborhood communities, the spatial structure of the understory assemblage is a better surrogate as a spatial descriptor of regeneration habitats.
This hypothesis is based on two perspectives. First, among all the neighborhood communities, understory plants are the nearest neighbors surrounding tree seedlings, and
they can form close spatial relationships with seedling assemblages. Second, understory plants and tree seedlings can covary with microenvironments at the same fine scales, whereas other neighborhood communities (trees in the upper forest strata) respond to environmental variability at much coarser scales (Metz 2012). Moreover, if our hypothesis is supported, the understory plant spatial structure can be used to classify emerging ecological groups of seedling assemblages with their specific regeneration habitats to identify different regeneration patches. Then, among the different regeneration patches, changes in species composition and diversity across life-history stages can further help us clarify how regeneration niches affect subsequent community assembly.
Such analyses help compensate for the uncoupling between seedling and adult-tree assemblages. To test these ideas, we (1) analyzed how seedling assemblages were structured spatially in regeneration habitats by neighborhood communities and topography, (2) examined spatial covariations between seedling species versus their adults and other understory species, (3) classified emerging ecological groups of seedling assemblages with their specific regeneration habitats and investigated differences in community patterns (the abundances and diversities of seedlings and understory plants) therein, and (4) explored the association between seedling and adult assemblages in different regeneration patches.
Material and methods
Study area
The study was conducted in the Nanhsi forest dynamics plot, with an area of 8.37 ha (Fig.
2.1). The geographic location and local climate of this plot are stated in Chapter 1 Study area. Note that the mean annual rainfall was 3,044 mm during 1998–2009, and 77% of
the precipitation occurred from May to September. The mean annual temperature was 11.5 °C (1998–2009), with a January mean of 6.7 °C and July mean of 14.9 °C.
The forest vegetation and tree data according to Yang et al. (2008) are stated in Chapter 1 Vegetation. Recall that the forest vegetation types identified in this plot included two Lauraceae-dominated forest types (the M. japonica type and the M. japonica-C. cuspidata type), one Theaceae–Fagaceae forest type (the Schima superba-C. cuspidata type), and one deciduous forest type (the Alnus formosana type).
Field survey of understory plants and tree seedlings
Analyzing assemblages of understory plants is a prerequisite for exploring the effects of neighboring communities on tree seedlings in regeneration habitats. Understory plants and tree seedling species were surveyed in 10 parallel transects with a regular spacing of 30 m in the north–south direction. Each transect was 200 m in length and 2 m in width, and it was divided into 100 quadrats of 2 m × 2 m sampling units. A total of 994 quadrats were surveyed (excluding six quadrats that were located either in water or on the bare rock of slopes) from May to July 2009 (Fig. 2.1). Understory plants (excluding tree seedlings) contained 60 families, 118 genera, and 190 species; most of them were herbs, including ferns. Tree seedlings (individuals of tree species with DBH < 1 cm) contained 19 families, 29 genera, and 36 tree species with 3,687 individuals, and the most commonly occurring families were the Lauraceae (2,605), Pentaphylacaceae (303), Adoxaceae (233), and Fagaceae (225). Both the understory plants and tree seedlings in each quadrat were measured to obtain their heights and coverage percentages.
Neighborhood communities, neighborhood factors, and environmental factors
Neighborhood communities include understory plants and trees in the upper forest strata that surround seedling assemblages in space. To explore the effects of neighboring trees, we used data from a tree census in 2006 (Yang et al. 2008), which were subdivided into trees in the shrub layer (for trees ≤ 5 m in height) and the overstory (> 5 m). We computed the basal areas (BAs) of the shrub layer (shrub BA) and overstory (overstory BA) of trees within a circle (radius = 4 m) surrounding the center of each quadrat (also see Fig. S2.1a), and we calculated tree density as the number of stems (both in the shrub layer and overstory) divided by the circle area.
Among the understory plants, tall herbs often throw dense shade on seedlings and compete with seedlings for limited resources; therefore, tall-herb (with heights exceeding 30 cm) cover and mean height in each quadrat were also recognized as neighborhood factors that might affect seedling assemblages. In summation, the neighborhood factors include overstory BA, shrub BA, tree density, and herb cover and mean height in each quadrat (Fig. S2.1a). These factors were treated by a principal component analysis (PCA), and the PCs were abbreviated as strata PCs (Fig. S2.1a). Strata PC1–4 explained 29.0%, 20.0%, 16.3%, and 13.6% of the variations, respectively. The top two factors for PC1–4 were cover and the mean height of tall herbs for PC1, shrub BA and tree density for PC2, cover and the mean height of other herbs for PC3, and overstory BA and shrub BA for PC4. Note, the neighborhood factors do not consider species identity.
Topographic factors can directly or indirectly affect species assembly (Brown et al.
2013). Topographic factors include altitude, slope, and convexity. Altitude was measured by a theodolite (Yang et al. 2008), and it was used to calculate the slope and aspect. The circular aspect angle was mapped into two orthogonal components on sine and cosine axes. The convexity of each subplot was calculated as the subplot’s altitude minus the mean altitude of eight neighboring subplots. Altitude, slope, and convexity, together with
their third degree orthogonal polynomials, were used to model the relationships between topography and community variations (Legendre et al. 2009).
Data analysis
Examination of the spatial structure of seedling assemblages in regeneration habitats Here, among the effects of neighborhood communities, we singled out the understory assemblages for the aforementioned ecological reasons. To test our hypothesis that understory plants, as the nearest neighbors of tree seedlings, are a better spatial descriptor of regeneration habitats, we needed to extract the understory plant spatial structure using the spatial patterns of understory assemblages. We purposely distinguished this factor from neighborhood factors (strata PCs) that are related to the spatial patterns of coverage (e.g., the BA if trees, or herb cover). Flowcharts for the analytical procedures are shown in Fig. S2.1.
We first derived the understory plant spatial structure from principal coordinates of neighbor matrices (PCNM) (Borcard & Legendre 1994; Borcard & Legendre 2002; Dray et al. 2006). Briefly, PCNM transforms pair-wise distances among quadrats into Moran’s eigenvector maps (MEMs) that represent spatial variables at different scales (hereinafter, PCNM variables). PCNM variables that significantly explained the spatial variation of understory assemblages were considered potential critical variables to represent the understory plant spatial structure. The significance of PCNM variables as explanatory variables (X) in redundancy analysis (RDA) for spatial variation of understory communities (Y, after being subjected to a Hellinger transformation) were determined by forward selection. We identified a minimum subset of 44 PCNM variables based on a permutation test (Jombart et al. 2009, Wagner 2013) (Fig. S2.1b, also see Fig.
S2.2a), which explained 20.1% of the variation in the understory assemblage Fig. S2.2b).
Then, a partial RDA (Fig. S2.1b) was used to partial out the covarying effects of topography (which explained 7.1% of the variation, Fig. S2.2b), and extract the pure understory plant spatial structure. Hereafter, we used uRDA1 and uRDA2 to refer to the understory plant spatial structure, and they comprise the site scores of the 44 PCNM variables ordinated on the first two RDA axes; that is, the two major axes in relation to the PCNM variables in scales, which represent the variability in understory assemblages across quadrats.
Then, we tested our hypothesis by examining whether (i) the understory plant spatial structure affected the spatial distribution of seedling assemblages, and (ii) seedling species were distributed differentially over its major gradient (using uRDA1 as a proxy).
In (i), we evaluated the relative contributions from the understory plant spatial structure, topography, and neighborhood factors (strata PCs) to explain the spatial variation of the seedling assemblages, as well as the dominant taxon groups of tree seedling species (Lauraceae, Pentaphylacaceae, Adoxaceae, and Fagaceae). The relative contributions toward explaining the variations (adjusted R2 statistic, R2a) were tested using F-statistics by forward selection in an RDA (Legendre et al. 2005; Blanchet et al. 2008; Legendre and Legendre 2012). Contributions from the neighborhood factors imply that there are neighboring effects on seedling assemblages across forest strata; notably, those spatial scales, especially those from the upper forest strata, are usually quite coarse (Metz 2012), as can be seen from the spatial autocorrelation scales of neighboring effects assessed through a spatial correlogram of Moran’s I (Borcard et al. 2011).
In (ii), we examined how seedling species were distributed over the gradient of the understory plant spatial structure and the topographic factors. Specifically, we used the first PC of the topographic factors (topographic PC1, which explained 30% of the variation in the PCA and mostly correlated to altitude, slope, and convexity) to represent
the topographic gradient. Note, as explained above, the topographic effects on the understory plant spatial structure were partialed out; that is, the abiotic and biotic gradients were orthogonal. The biplot of uRDA1 and topographic PC1 indicated the niche differentiation between understory and seedling assemblages. The confidence interval (within 0.025–0.975 quantiles) of the understory and seedling species distributed over the two axes were evaluated by bootstrapping 1,000 times.
Examining the spatial covariations between seedling species versus adults and other understory species
The neighborhood effects introduced by bio-interactions between seedlings and neighboring species (Fig. S2.1c, also see Table S2.1) were further examined by a codispersion analysis. Bio-interactions, such as dispersal limitation, positive facilitation, or competitive exclusion, can be evaluated based on positive or negative codispersion patterns in space. Codispersions were conducted for the most abundant species to assess the link between the seedling species and the top two influential tall herb species (Table S2.1), and between the seedling species and their conspecific adults. The codispersion analysis is a nonparametric technique used to assess how the presence of a pair of species covaried across a range of spatial lags (distances between points) based on cross- variograms of the pair of spatial series (Rukhin and Vallejos 2008). Briefly, the codispersion analysis we applied included three steps. First, the codispersion coefficient of two spatial series between points at a given lag was evaluated. The codispersion coefficient is defined as the cross-variograms normalized by the square root of the product of each semi-variogram. Formulas for the codispersion coefficient are given in Vallejos et al. (2015), with more statistical details in Cressie (1993) and Rukhin and Vallejos (2008). The codispersion coefficient ranges from −1 (strongest negative covariations) to
+1 (strongest positive covariations). Second, codispersion coefficients for lags and for azimuths (0–π) were mapped in a radial way onto the x-y plane (a so-called codispersion map, Vallejos et al. 2015), which facilitates detecting the directionality (anisotropy) of covariations between two spatial series (Buckley et al. 2016). Third, the significance of the codispersion coefficients was tested by comparing observed values against a spatial permutation. Specifically, we toroidal-shifted individuals’ distributions (torus permutation), which maintains the autocorrelation structure after the shifts. This torus permutation accounts for the autocorrelation in space. Significance was determined following 200 permutations using the two-tailed test, with P < 0.025.
Classifying emerging ecological groups of seedling assemblages with their specific regeneration habitats, and examining the differences of community patterns therein To assess how regeneration niches for seedling assemblages were shaped by the effects of the understory plant spatial structure, we identified different regeneration patches over the uRDA1 gradient. In practice, uRDA1 was used to cluster quadrats into spatial- structuring groups (SSGs) by the constrained clustering method (Borcard et al. 2011;
Dray et al. 2012). Five SSGs (hereafter SSG1–5) were identified along uRDA1 (plotted in Fig. 2.1; the physical and biotic attributes are listed in Table S2.2). We further examined the associations between species (both understory plants and tree seedlings) and site-groups (i.e., SSGs) by an indicator species analysis (De Cáceres et al. 2009). A generalized form of an association index, the phi-coefficient (rpb), was adopted to indicate a specific preference among species for SSGs.
To verify whether regeneration niches really differentiated among SSGs, abundance (density) and a diversity index were compared among the SSGs for different seedling stages and understory plants. Seedling stages included new recruits of seedlings (height
< 30 cm) and older seedlings (height ≥ 30 cm). For the diversity index, we used Hill’s N1 diversity index, which expresses α diversity as the effective number of species (Hill 1973;
Jost 2007). The significance of differences in abundance and the diversity index were tested by a random permutation test. Here, we did not use the torus permutation, because the SSGs were defined by uRDA1, which already accounted for the spatial autocorrelation mostly related to topography. Through this analysis, we assessed how seedling assemblages responded to the biotic effects of neighborhood understory assemblages for those regeneration patches identified along the uRDA1 gradient.
Investigating the association between seedling and adult assemblages
For each SSG, a similarity index (Jost 2007) of species composition for seedlings versus juvenile trees (those that only reached shrub layer and whose DBH was less than 5 cm) and adults (trees other than juvenile trees) in each forest type was determined. Here, juvenile trees were also incorporated into the analysis to clarify the transition in association across life-history stages to better understand the coupling (or uncoupling) between seedling/adult stages. This analysis clarified how regeneration niches affect subsequent community assembly.
Computation
All the data analyses were performed in R (R Development Core Team 2016), with our data and codes available at https://github.com/cywhale/spatially_structured_regen. Here, PCNM, forward selection, and RDA were performed using the R packages “PCNM”,
“packfor”, and “vegan” (Legendre et al. 2012; Dray 2016; Oksanen et al. 2016), respectively. The spatial correlogram of Moran’s I was evaluated using the “spdep”
package (Bivand et al. 2013; Bivand and Piras 2015). Codispersion patterns were tested by the torus permutation with the “geoR” package (Ribeiro Jr and Diggle 2016). An
indicator species analysis was performed using the “indicspecies” package (De Cáceres et al. 2009). A similarity index was computed using the “vegetarian” package (Charney and Record 2012).
Results
Understory plant spatial structure as the spatial descriptor of spatially structured regeneration habitats
The understory plant spatial structure that ordinated on the first RDA axis (uRDA1) was the most influential factor in terms of explaining the variation in seedling assemblages, more so than the topographic factors (Table 2.1). We noted that uRDA1 negatively correlated with tall herb cover (Fig. 2.2a) and positively correlated with understory and seedling species diversity, and seedling density (Fig. 2.2b–d). Moreover, tall herbs and uRDA1 had a similar fine-scale autocorrelation structure (finer than 25 m; Fig. S2.3a, b), suggesting that tall herb species played important roles in characterizing the understory plant spatial structure.
In addition to the understory plant spatial structure and topographic factors, neighborhood factors also contributed significantly to the seedling assemblages (strata PCs in Table 2.1). Nevertheless, we found that the dominant influential factors varied among taxonomic assemblages. While the Lauraceae and Fagaceae were mostly affected by uRDA1, the Pentaphylacaceae and Adoxaceae were mainly affected by the topographic slope and altitude, respectively (Table 2.1). More specifically, the Adoxaceae occupied two extremes: Viburnum luzonicum at higher elevations (ridges) and Viburnum taitoense at lower elevations (also see Fig. 2.2e). Eurya species (Pentaphylacaceae) generally inhabited steeper slopes. The high-order polynomials of topographic factors
hardly contributed, suggesting that the variation in seedling assemblages was only related linearly to the topographic factors.
The distribution of seedling species over the gradient of uRDA1 and topographic PC1 (Fig. 2.2e) revealed that their association with regeneration habitats differed because of the heterogeneity of the understory assemblage or topography. Species clusters could be distinguished along the uRDA1 axis, accompanied by a co-occurrence of seedling species and tall herb species. For example, Fagaceae seedlings and Dryopteris formosana were at the positive end of uRDA1, while Lauraceae seedlings and Alpinia shimadae were near the zero value of uRDA1, and Eurya loquaiana (Pentaphylacaceae) was at the negative end of uRDA1, with the tall herb Miscanthus sinensis at the distal end. A spatial interspecific correspondence was also revealed by showing how these tall herb species contributed to the variation in seedling assemblages (Table S2.1). Adult trees also exhibited a similar correspondence, but they were not as influential as tall herb species, except for the Adoxaceae (Table S2.1).
In summation, our hypothesis that the understory plant spatial structure is a better surrogate as a spatial descriptor of regeneration habitats than topographic structuring is supported by these results, because (i) uRDA1 explained most of the seedling distribution and (ii) seedling species were distributed differentially over uRDA1. Therefore, we can use uRDA1 to classify emerging ecological groups of seedling assemblages with their specific regeneration habitats.
Codispersion analyses showing how seedlings covaried with adults and understory neighbors
Codispersion patterns between seedling and adults varied among taxonomic groups. We found that the codispersion coefficients of C. cuspidata (Fagaceae) and Eurya leptophylla
(Pentaphylacaceae) were significant relative to the torus permutation in all directions (Fig.
2.3c, d). The patchy-distributed seedlings of Fagaceae and Pentaphylacaceae suggest a dispersal limitation from adult trees. In contrast, Litsea acuminata (Lauraceae) had widespread seedlings, but it seldom exhibited significant codispersion coefficients relative to the torus permutation (Fig. 2.3a). The codispersion coefficients of Machilus spp. (Lauraceae) were rarely significant relative to the torus permutation in most y- direction lags (Fig. 2.3b), whereas those of V. taitoense (Adoxaceae) were mostly significant, except for some clumped lags (within 30 m) (Fig. 2.3e).
Codispersion analyses between seedlings and understory tall herb species indicated that they could be co-occurring or segregating. The segregation patterns, as shown in the two most widespread Lauraceae seedlings versus M. sinensis (Fig. 2.3f, g), were more significant in the −x (left) through +y (upper) direction, which was inhabited by M.
sinensis. The codispersion coefficient of V. taitoense was not significant relative to the torus permutation when it was evaluated with two covarying tall herbs (Fig. 2.3j, o). This is consistent with the result showing that tall herbs were much less influential factors than topography (altitude) for Adoxaceae seedlings (Table 2.1). Both E. leptophylla and C.
cuspidata co-occurred with tall herb species. Eurya leptophylla significantly co-occurred with Arachniodes rhomboidea in most +x (right) directions (Fig. 2.3i), and with D.
formosana, especially in some +x (right) through +y (upper) directions (Fig. 2.3n).
Castanopsis cuspidata co-occurred with both D. formosana and Carex spp. (Fig. 2.3h, m) and the codispersions were significant in almost all directions. Seedling species were not only distributed differentially over uRDA1 (as mentioned above), but they also formed close spatial relationships with understory species (i.e., co-occurrence or segregation according to the codispersion test).
Differences in species diversity and seedling density reveal that seedling assemblages responded differently among the SSGs
Recall that the five SSGs were identified using constrained clustering along uRDA1.
Different regeneration patches can be further recognized by (i) differences in community patterns (diversity and abundance of seedlings and understory plants) among the SSGs, and (ii) specialization in species–SSG relationships (as revealed by the indicator species analysis, Table S2.3).
In (i), for understory species, each SSG had significantly lower diversity than expected by random chance (Fig. 2.4c). The lower-than-expected understory species diversity in each SSG was due to the clumped tall herb species, which covered larger areas of neighboring quadrats than other understory species. Understory plants in SSG4 were overwhelmingly dominated by M. sinensis (with a very high phi-coefficient of rpb = 0.82 in the indicator species analysis, Table S2.3), resulting in much lower diversity.
For older seedlings (height > 30 cm), SSG1–4 had significantly lower diversity than expected by random chance (Fig. 2.4b). In contrast, the species diversity of new recruits (height < 30 cm) was usually not lower than that expected by random chance (Fig. 2.4b, in SSG1, 3–5). The difference between older seedlings and new recruits may be explained by the fact that older seedlings were continuously shaped by neighboring tall herbs, while new recruits were affected to a lesser degree.
SSG5 had the greatest seedling density among the SSGs and a significantly greater density of older seedlings than expected by random chance (Fig. 2.4a). The seedling density of SSG4 for both older seedlings and new recruits was the lowest among the SSGs and lower than that expected by random chance. SSG3, which is in the neighborhood of SSG4, had a higher density of older seedlings but a lower density of new recruits than expected by random chance. Seedling densities showed clearer differences among the
regeneration patches of SSG3–5. In contrast, SSG1 and SSG2 exhibited random patterns of moderate values and smaller variations of confidence interval.
In (ii), SSG1 and SSG2 were the widespread regeneration habitats in valleys and slopes that could accommodate different sets of seedling species (Table S2). However, no indicator species of tree seedlings was dedicated exclusively to SSG1 or SSG2 (Table S2.3, only V. taitoense was associated with the union of SSG1 and SSG2). Therefore, we do not regard SSG1 and SSG2 as patches; instead, they represent the baseline with which to compare the community patterns in the other regeneration patches (SSG3–5) below.
SSG4 had the lowest seedling density and species diversity, with one indicator species of seedlings, Eurya chinensis (rpb = 0.16, Table S2.3). However, different indicator species of the seedlings Acer kawakamii (rpb = 0.32, Table S2.3) and E. leptophylla (rpb
= 0.26) were found in SSG3. SSG5 maintained a very specific community pattern with the most diverse and abundant older seedlings. Many indicator species of seedlings were specific to this patch, including C. cuspidata, S. superba, and others (detailed in Table S2.3).
Association between seedling and adult assemblages varies among different SSGs In SSG1 and SSG2, we found a high similarity index between seedling assemblages versus juvenile tree and adult assemblages (approximately 0.72–0.9, Table S2.2); i.e., the community assemblies across life-history stages were coupled. In SSG4, uncoupling was substantial, as can be seen in its low similarity (0.39–0.4) of species composition between seedlings versus adults in Lauraceae-dominated or deciduous forest types (Table S2.2).
In SSG3, a discrepancy between seedling and adults was also found (a relatively low similarity index of 0.62, Table S2.2), but its similarity index was still higher than that of SSG4. SSG5, which is located in the Theaceae (S. superba)–Fagaceae (C. cuspidata)
forest type, seedling assemblages exhibited high similarity with juvenile and adult trees (Table S2.2), suggesting that the older seedlings have a higher chance of surviving into the next life-history stage. Together with the results of the most diverse and abundant older seedlings found here, SSG5 was an optimal habitat for species assemblages to persist across life-history stages.
Discussion
Local heterogeneity of regeneration habitats
Our hypothesis that the understory plant spatial structure is a better surrogate for a spatial descriptor of regeneration habitats is supported by two lines of evidence. First, the understory plant spatial structure was more influential than topographic factors in affecting the spatial distribution of seedling assemblages (Table 2.1). These results are consistent with our expectation that seedling assemblages were structured spatially in regeneration habitats by understory assemblages. This result may explain why weak associations between seedling assemblages and environmental factors were often found.
The critical issue is that the scale of structuring by abiotic factors may be too coarse compared with the fine-scale heterogeneity of neighborhood bio-interactions, to which seedling assemblages respond (Baraloto and Goldberg 2004; Gómez-aparicio et al. 2005).
Second, we found close spatial relationships between seedling assemblages and tall herb species in the understory at both the community and species levels. At the community level, the gradient of the understory plant spatial structure (uRDA1) correlated with the seedling density and species diversity in the regeneration habitats (Fig. 2.2c, d). Tall herb cover and uRDA1 were closely related (Fig. 2.2a), and they had a corresponding fine- scale spatial autocorrelation structure (Fig. S2.3a, b). Over the uRDA1 gradient, we
defined spatial structuring groups. We found clear differences in seedling diversity and density, especially for older seedlings (Fig. 2.4), among spatial structuring groups, which were greatly affected by tall herb cover. At the species level, seedling species showed significant covariations with tall herb species along uRDA1 (Fig. 2.2e), which were further verified in the codispersion analyses (Fig. 2.3). In summation, the spatial distribution of tall herb species exhibited a fine-scale structure, which promoted local heterogeneity in microenvironments. Such additional heterogeneity critically affects the spatial variations of seedling assemblages. In fact, past studies often overlooked the fine- scale heterogeneity in microenvironments provided by bio-communities (Gonza et al.
2011). In addition, Gómez-aparicio et al. (2005) argue that the heterogeneity provided by neighboring understory plants is at the within-microhabitat scale, which makes it difficult to assess the regeneration niche of tree species only using abiotic factors. Our findings support these arguments by showing a link between the understory plant spatial structure and spatially structured regeneration habitats. These effects could jointly affect the niche processes that shape seedling assemblages, i.e., regeneration niches.
Bio-interactions with neighboring plants and dispersal limitation on seedling distribution
The results of the codispersion analyses indicate that tall herbs could be the most important neighbors influencing seedling species in the understory (Fig. 2.3). It is worth noting that while tall herbs are usually reported to have negative effects on seedlings via shading or competition (Royo and Carson 2006; Gilliam 2007), our analyses suggest that positive neighboring effects may exist in some cases. For example, the shade-tolerant seedling species C. cuspidata and E. leptophylla co-occurred with tall herb species (particularly D. formosana; Fig. 2.3h, n). In fact, in our field observations, tall herbs had
nurse-plant effects on seedlings, especially for new recruits, by providing shade, thus reducing grazing pressure from ungulates (Weng et al. 2017a). The results of the codispersion analyses also suggest that dispersal limitation operated in seedlings/adults of C. cuspidata and E. leptophylla (Fig. 2.3c, d). In addition, C. cuspidata and E.
leptophylla are the indicator species for SSG5 and SSG3, respectively (Table S2.3).
Therefore, dispersal limitation from adults and positive bio-interactions with tall herbs synergized to shape the patchy seedling distribution.
In contrast, L. acuminata seedlings did not exhibit positive codispersion with their adults, suggesting that the adult trees were not limiting factors to their seedlings (Fig.
2.3a). Lauraceae seedlings also did not exhibit positive codispersion with tall herb species;
instead, Lauraceae seedlings (which were widely distributed in SSG1 and SSG2) were segregated from M. sinensis (Fig. 2.3f, g). That is, even though the niche of Lauraceae seedlings is broad, they could not extend to the habitats occupied by M. sinensis. In SSG1 and SSG2, we found much more abundant new recruits, but fewer older seedlings, with much higher species diversity in older seedlings (Fig. 2.4). These abundant Lauraceae seedlings in SSG1 and SSG2 suffered negative density-dependent processes; as such, these seedlings cannot reach older stages. The fact that Lauraceae seedlings did not co- occur with tall herbs suggests that tall herbs did not provide positive neighboring effects in SSG1 and SSG2. Similarly, V. taitoense, which was specific to the union of SSG1 and SSG2 (Table S2.3), also did not co-occur with herbs. Rather, V. taitoense significantly co-occurred with adults, except in clumped lags (within 30 m, Fig. 2.3e). This result suggests that the seedling distribution of V. taitoense might be subject to dispersal limitation and a negative density dependency at distances of approximately 30 m from adult trees.
Our analyses of the codispersion between seedlings and understory tall herb species help confirm that patchy seedlings can be shaped by their encompassing neighbors. Our results are consistent with previous studies demonstrating that neighboring understory plants can selectively filter seedlings in the context of a seedling’s attributes (e.g., age and shade tolerance) (George and Bazzaz 1999), or the biotic factors encompassing the seedlings (such as herbivore pressure) (Smit and Vandenberghe 2007; Bachelot et al.
2016). The selective filtering by neighboring herbs facilitates the determination of the spatial structure of seedlings (George and Bazzaz 1999). That is, as aforementioned, herbs contribute to spatial heterogeneity in regeneration habitats.
Neighborhood effects on regeneration patches
The presence or absence of M. sinensis played an important role in determining the association between seedlings and adults in SSG4 and SSG3. SSG4 was dominated by M.
sinensis, whereas M. sinensis was absent in SSG3. Miscanthus sinensis is a highly light- demanding perennial tall herb (approximately 2 m high). Critically, M. sinensis greatly reduces the amount of light reaching the understory; it covers most of the forest ground, which hinders seedling recruitment and establishment. These negative impacts resulted in SSG4 having the lowest seedling density and species diversity among all the SSGs (Fig.
2.4). SSG4 and SSG3 are under similar forest types: half under the deciduous forest type and half under the Lauraceae-dominated forest type (Table S2.2). Nevertheless, in SSG4, there was a substantial uncoupling between seedlings and adults in both forest types (Table S2.2). In SSG3, in contrast, we found a higher seedling density (Fig. 2.4) and a higher similarity between seedlings and adults in the Lauraceae-dominated forest type.
These results indicate that the regeneration patterns and availability of the regeneration niches for seedling species varied greatly in SSG4 and SSG3, depending on the presence
or absence of M. sinensis. Our results suggest that the dense herbaceous layer acts as a strong filter of floristic diversity in the understory, suppressing tree regeneration and potentially changing future succession; our results are consistent with previous studies (George and Bazzaz 1999; Abe et al. 2002; Griscom and Ashton 2003; Royo and Carson 2006).
SSG5 might be an optimal habitat for regeneration and species coexistence, as SSG5 had the highest older seedling and juvenile tree densities, as well as the highest diversity.
Tree density and tall herb cover were negatively correlated (R = −0.31, P < 0.001) in our study. Some studies showed that tree density could have negative effects on understory plants (Beckage et al. 2000; Caccia et al. 2009). Although in SSG5, herb cover was suppressed by a dense shrub layer, this does not mean that understory plants did not influence seedlings. Instead, uRDA1 was still the most influential factor for Fagaceae seedlings (Table 2.1). These results highlight the importance of the existence of positive neighboring effects on the co-occurrence of remaining tall herbs (D. formosana) and seedlings (C. cuspidata, Fagaceae). In addition, Pinto and Macdougall (2010) argued that dispersal limitation hinders seedling species from occupying their optimal habitats under the absence of a fine-scale autocorrelated environment, thereby preventing their clumping in suitable habitats at nearby sites. However, in our study, we showed that fine-scale microenvironments were structured spatially by the neighborhood understory assemblage.
Furthermore, a previous study found a high diversity of adult trees in the Theaceae–
Fagaceae forest type (Yang et al. 2008). These results suggest that diverse means of propagule dispersal might help promote diversity in seedling assemblages in SSG5.
Dispersal limitation was not the restriction here. Instead, dispersal limitation, together with neighboring understory species, helped spatially structure this optimal habitat at a fine scale.
