Relationships between macro-fungal dark diversity and habitat parameters using LiDAR
Introduction
Understanding the underlying drivers shaping biodiversity patterns is a central goal in ecology and conservation biology. This is also true for fungi which play a vital role in ecosystem functioning as decomposers, mutualists, and pathogens. However, fungi and the underlying environmental factors influencing fungal diversity are less studied than animals and plants, and quantifying fungal diversity is far from trivial. The most commonly used biodiversity metric is observed species richness (Mueller 2011). However, this measure is not always suitable for comparisons across habitats and conveys no information on the part of the diversity that is potentially missing in a given site (Pärtel et al. 2011). In addition, monitoring fungal diversity is often severely hampered by detectability issues and the life history of the involved species (Yahr et al. 2016; Blackwell and Vega 2018). Several alternative approaches have been developed to more effectively monitor and compare biodiversity across landscapes (Solow and Polasky 1994; Sarkar and Margules 2002; Ricotta 2005, 2007bib_Ricotta_2005bib_Ricotta_2007). Although these methods can provide valuable insights, they do not consider the dark diversity, the absent part of the species pool which can potentially inhabit an environmentally suitable site (Pärtel et al. 2011). This often-ignored aspect of diversity provides an ecologically meaningful metric for estimating how much of the potential species diversity – the site-specific species pool – is lacking (Pärtel et al. 2011). This information is important for understanding the underlying mechanisms and dynamics of community assembly (e.g., community saturation) (Mateo et al. 2017). Dark diversity may also become an important conservation tool to measure biodiversity potential, such as aiding managers or policy-makers to prioritize certain habitats, estimate restoration potential of degraded habitats, or forecast potential impacts of invasions (Lewis et al. 2017). Here, we use fungal data from 130 thoroughly inventoried sites covering all terrestrial habitats, from open to forest, and wet to arid, to investigate important drivers of fungal dark diversity.
Dark diversity aims to reconcile the role of simultaneous, and potentially confounding, regional and local processes underlying biodiversity patterns and biological communities (Pärtel et al. 2011; Pärtel 2014). In any given landscape, the biodiversity potential is ultimately determined by large-scale biogeographic and evolutionary processes (i.e., species diversification and historic migration patterns) determining the set of species which can theoretically inhabit a site, defined as the regional pool (Pärtel et al. 1996; Cornell and Harrison 2014; Zobel 2016). This regional pool is further filtered by local processes such as environmental gradients, species interactions, population dynamics, dispersal, disturbance, and stochastic events, and referred to as the site-specific species pool, i.e., the species that could possibly live in a given site (Pärtel et al. 2013; Cornell and Harrison 2014; Ronk et al. 2015; Zobel 2016).
While many studies have investigated the drivers of fungal diversity, only a few studies have focused on the determinants of fungal dark diversity. These studies demonstrate that higher temperatures increase arbuscular mycorrhizal dark diversity (Pärtel et al. 2017a) and annual precipitation decreases the dark diversity of ectomycorrhizal fungi at the global scale (Pärtel et al. 2017b). These results concur with previous research suggesting that large-scale climatic factors are strong drivers of fungal richness and community composition, attributed to the direct and indirect effects which alter soil and floristic conditions (Staddon et al. 2003; Kivlin et al. 2011; Tedersoo et al. 2014). Local edaphic conditions such as soil moisture, pH, and calcium concentration are also known to influence fungal diversity (Geml et al. 2014; Tedersoo et al. 2014; Tonn and Ibáñez 2017; Frøslev et al. 2019), but it is not known how these environmental factors affect fungal dark diversity. In fact, the general mechanisms determining dark diversity in fungal communities remain largely unknown. While species can be absent from an area just by chance (Hubbell 2011), species can also be missing from a site due to environmental stochasticity and human disturbances, which can alter a species dispersal, establishment, or persistence. In principle, these factors can be both biological and chemical and act at various spatial scales. While habitats often have a constant level of relatively low or typical disturbances that the habitat's species are adapted to (e.g., grazing), these do not count towards disturbances that can cause dark diversity. Note that the dark diversity is conceptually different to the hidden diversity; i.e., species that are actually present in a given site but just not recorded (Milberg et al. 2008; Abrego et al. 2016).
Besides the influence of environmental gradients, other factors particularly important for fungi are vegetation and habitat structure, such as vegetation height, shrub layer, vegetation cover, dead wood, and other woody features (Humphrey et al. 2000; Nordén and Paltto 2001; Nordén et al. 2004; Gómez-Hernández and Williams-Linera 2011; Zuo et al. 2016). As the dominant primary producers in terrestrial ecosystems, plants also form the living and dead organic carbon pools and biotic surfaces that are the niche space for not only fungi but other taxonomic groups as well (DeAngelis 2012; Brunbjerg et al. 2017). These structural elements are important for biodiversity, and can influence not only fungal diversity, but the diversity of plants, animals, and bacteria as well (Penone et al. 2019). However, despite the obvious contribution of these variables, such factors are rarely covered extensively since they are difficult to measure and require large amounts of resources to obtain sufficient and high quality data. However, emerging technologies such as LiDAR (light detection and ranging) could potentially remedy this situation.
Airborne LiDAR records a three-dimensional set of points using laser ranging from an aircraft or a drone (Lefsky et al. 2002). It captures data suitable to represent many of the vegetation and landscape structural measures important to fungi (Vehmas et al. 2009; Lopatin et al. 2016; Peura et al. 2016; Thers et al. 2017; Mao et al. 2018). As a relatively new methodology, biodiversity studies that employ LiDAR have been limited in scope, typically addressing only one taxonomic group or habitat type at the local scale, and strongly biased towards forest ecosystems. However, studies using LiDAR-based indicators have already been shown to explain up to 66% and 82% of local plant and fungi richness, respectively (Lopatin et al. 2016; Peura et al. 2016; Thers et al. 2017). A recent study has demonstrated its potential to provide spatially accurate and comprehensive measures by predicting the local biodiversity of different taxonomic groups (plants, fungi, lichens, and bryophytes) across multiple habitat types and large geographic extent (Moeslund et al. 2019). LiDAR may also be a useful tool in studying dark diversity by incorporating potentially important spatiotemporal factors such as succession and disturbance (Mokany and Shine 2003; Scott et al. 2011; Pärtel et al. 2013). For example, recent studies have found that human impact increases dark diversity in arbuscular mycorrhizal fungi (Pärtel et al. 2017a), that ruderal plants are more likely to be in dark diversity (Moeslund et al. 2017), and that human density and agricultural land-use influence dark diversity of vascular plants (Riibak et al. 2017).
Alongside these structural and environmental factors, fungal diversity depends on biotic interactions, with a large proportion of fungi deriving their nutrients and carbon from host plants (Tedersoo et al. 2014; Nguyen et al. 2016). Recent evidence has hinted at the influence of these interactions on dark diversity, as plant species dependent on mycorrhiza have been found to have greater dark diversity than those without these mutualist relationships (Moeslund et al. 2017). Moreover, ectomycorrhizal fungal diversity seems to increase exponentially with an increasing proportion of their host plants, suggesting that competitive interactions among fungi might also drive their dark diversity (Pärtel et al. 2017b). Strong species interactions are indeed typically considered in dark diversity, as the estimation hereof is usually based on species co-occurrences (Beals 1984; McCune 1994; Münzbergová and Herben 2004; de Bello et al. 2012; Lewis et al. 2016). However, this is usually done only within the species group being studied. For example, in studies of plant dark diversity, only co-occurrences with other plants, and not fungi or other species groups, are typically considered. However, recognizing the close and interconnected relationship between plants and fungi allows stronger and more realistic estimations of the fungal dark diversity. Incorporating other taxonomic groups when determining species pools and estimating dark diversity is not a new insight, and the importance of biotic interactions across trophic groups has been discussed since the concept of dark diversity was first introduced (Pärtel et al. 2011). Yet, such cross-species group data has never been included in dark diversity estimates meaning that they may not sufficiently account for cross-species group interactions, and may be part of the explanation for why dark diversity is sometimes over-estimated (Boussarie et al. 2018).
In this study, we examined a number of environmental factors influencing the local dark diversity of fungi across habitat-types nationwide within Denmark. We used a comprehensive biodiversity dataset covering major environmental gradients (Brunbjerg et al. 2019) and combined it with LiDAR-based measurements. We also included fungi-plant-co-occurrence information to estimate local fungal dark diversity and thereby acknowledge the importance of their biotic interactions. More specifically, we addressed the following questions: (1) To what degree can we explain local fungal dark diversity by abiotic and biotic environmental factors? (2) Can vegetation and terrain structural factors that are important to local fungal dark diversity be derived from LiDAR and if so, (3) how important are they compared to field-measured factors?
Section snippets
Study area and site selection
The dataset was collected from a national biodiversity inventory in Denmark as part of the “Biowide” research project (Brunbjerg et al. 2019). A total of 130 study sites (40 × 40 m) were selected with a minimum distance of 500 m between each to reduce spatial covariance with 30 sites allocated to cultivated habitats and 100 sites to natural habitats (Fig. 1). The cultivated subset was stratified according to the type of land use and the natural subset was selected amongst uncultivated habitats
Data preparation
Prior to statistical analysis, we removed the six intensively managed fields from the study sites, as these were ploughed fields. We also removed two study sites because they were flooded during the LiDAR data recording period. Finally, we removed one site due to an extreme outlier in the LiDAR amplitude values (300 vs. a range of values between 10 and 130). Our final dataset therefore comprised a total of 121 study sites.
Our initial visual inspection of the data revealed that many of the LiDAR
Results
The two relative fungal dark diversity estimates (based on fungi-only and both fungi- and plant co-occurrences) were between 0.17 and 0.93 (open habitats, median 0.51) and 0.20–0.63 (woodland, median: 0.39); and 0.21–0.95 (open habitats, median: 0.56) and 0.24–0.7 (woodland, median: 0.44) respectively. In most cases, our models explained between 20 and 30% of the variation in fungal dark diversity and more than 40% for the woodlands models when including both LiDAR and measured variables (
Discussion
In this study, we demonstrate for the first time that LiDAR-derived variables, alone and in combination with field-measured variables, can explain a significant amount of the variation in local dark diversity of temperate macro-fungal communities. Our findings indicate that the dark diversity of fungi is influenced by the local vegetation structure, plant associations, and the abiotic environment. This is not surprising since local observed fungal diversity is also determined by these factors
Conclusion
This is the first study to investigate potential drivers of the local dark diversity of fungi using both LiDAR derived vegetation and terrain structure as well as field-measured variables. We showed that local fungal dark diversity is strongly dependent on the environment with vegetation structure, plant diversity, and abiotic factors playing important roles. Also, to our knowledge, this is the first study using cross-species group co-occurrence data to determine species pools. This may be a
Acknowledgements
We thank Thomas Læssøe for collecting and identifying macro-fungi. We sincerely thank Aage V. Jensen Nature Fund for financial support to CF, AKB, JM, LD, KC and JV through the project “Dark Diversity in Nature Management”. The Biowide project and REJ was supported by a grant from the Villum Foundation (VKR-023343). MP has been supported by the Estonian Ministry of Education and Research (IUT20–29), and the European Regional Development Fund (Centre of Excellence EcolChange). The authors
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