Drivers of plant functional group richness and beta diversity in fire‐dependent pine savannas

Abstract

Aim To assess the drivers of plant functional group richness and beta diversity in fire‐maintained North American Coastal Plain (NACP) savannas. Location The southern portion of the NACP, a global biodiversity hotspot. This region is characterized by fire‐dependent pine savanna fragments that are isolated within a matrix of agriculture, urban development, non‐pyrogenic plant communities and plantation forestry. Methods We used nested quadrats to sample plant species on 30 fire‐maintained savanna preserves in Florida and Georgia, USA. We analysed between‐site Sørenson dissimilarity, a measure of beta diversity, using NMDS and PerMANOVA. We measured nestedness using NODF, and we used Betapart to partition Sørenson dissimilarity into nestedness and turnover components. We used linear and generalized linear mixed models to explore drivers of functional group richness and composition, including fire regime (return interval, number of fires, time since fire and seasonality), vegetation structure (herbaceous cover, woody cover and tree density) and spatial factors (surrounding landscape and geographic distance). Results We found turnover‐dominated beta‐diversity patterns in all functional groups. Turnover was explained partly by spatial and environmental gradients, but roughly half of the turnover between sites was unexplained. Species richness was higher on sites where fire and fire surrogates had been used longer and more consistently, and these effects were partly independent of current vegetation structure. Fire regimes containing more growing season fire and more diversity of burn seasons promoted higher species richness. Relationships between small‐scale and large‐scale species richness varied by soil type and functional group. Main conclusions Fire‐maintained savannas in the southeastern NACP vary greatly in their plant functional group richness, but high beta diversity resulting primarily from species turnover suggests that even species‐poor sites can harbour less‐common members of the regional plant metacommunity. Prescribed fire regimes that include growing season fire as well as a diversity of burn seasons may best promote species and functional group richness.

Fire as a fundamental ecological process: Research advances and frontiers

“…When fire‐intolerant species are selectively filtered, fire homogenizes species composition among sites (Pausas & Verdu, 2008) and increases phenotypic (and often phylogenetic) clustering in communities (Forrestel, Donoghue, & Smith, 2014). However, even where fire regimes have long remained similar across sites, other contextual factors such as soil type and landscape patterns can exert considerable influence on beta diversity, nestedness and community assemblages (Freeman, Kobziar, Leone, & Williges, 2019). More locally, biotic interactions within and among plant species can further influence changes in deterministic assembly with time since fire and produce changes in fire regimes (Landesmann, Gowda, & Kitzberger, 2016; Tepley, Thompson, Epstein, & Anderson‐Teixeira, 2017).…”

Habitat-Suitability Model for the Yellow Rail (Coturnicops noveboracensis) in the Northern Gulf Coast of Alabama and Mississippi, USA

“…Dominant woody vegetation consists of gallberry (Ilex glabra), wax myrtle (Morella cerifera), sweetbay magnolia (Magnolia virginiana), St. John's-wort (Hypericum brachyphyllum), yaupon (Ilex vomitoria) and various greenbriers (Smilax spp.). Species composition across pine savanna habitats is primarily influenced by differing soil moisture gradients [14,35,36].…”

Prescribed fire science: the case for a refined research agenda

“…Wildfire science has also tended to focus on single-fire events, while prescribed fire science must consider the cumulative and long-term effects of managed fire regimes and fire-vegetation feedbacks that occur at smaller spatial and temporal scales (Freeman et al 2019). In this light, prescribed fire science is a bridge to fire ecology and ecosystem resilience.…”

“…In addition, for many ecosystems, the frequent and iterative nature of many prescribed burns generates effects that are inherently compounded or influenced by fire-vegetation feedbacks-dynamics not easily captured in existing decision-support tools. For example, three prescribed burns following extended fire exclusion in a single stand have very different consequences than 50 years of burning at a three-year interval, and it is the overall fire regime's effects that require assessment (Freeman et al 2019). Examples of potential developments include repeated-fires decision-support tools that target how prescribed fire can be used to consume fuels (Gallagher 2017), kill encroaching trees or non-native plants (Engber and Varner 2012), modify wildlife habitat characteristics for species of special concern (Hiers et al 2016), modify forest structure ), release soil nutrients and affect carbon sequestration (Godwin et al 2017), predict fire emissions (Robertson et al 2014), and perpetuate particular populations and community assemblages Dell et al 2017;Freeman et al 2019).…”

“…For example, three prescribed burns following extended fire exclusion in a single stand have very different consequences than 50 years of burning at a three-year interval, and it is the overall fire regime's effects that require assessment (Freeman et al 2019). Examples of potential developments include repeated-fires decision-support tools that target how prescribed fire can be used to consume fuels (Gallagher 2017), kill encroaching trees or non-native plants (Engber and Varner 2012), modify wildlife habitat characteristics for species of special concern (Hiers et al 2016), modify forest structure ), release soil nutrients and affect carbon sequestration (Godwin et al 2017), predict fire emissions (Robertson et al 2014), and perpetuate particular populations and community assemblages Dell et al 2017;Freeman et al 2019). Although some global vegetation models have advanced capacity to integrate firevegetation-climate dynamics (e.g., Bachelet et al 2005), they lack flexibility for producing predictions at spatial and temporal scales relevant to prescribed fire decision-making.…”