Ecological restoration is carried out with a multitude of specific goals [1
] but in general should always contribute positively to biodiversity, human health and wellbeing and the delivery of ecosystem services [2
]. As global interest in restoring ecosystems is growing (e.g., the United Nations’ Decade on Ecosystem Restoration; https://www.decadeonrestoration.org/
), it is imperative that targets and actions rely on the best available evidence. The science in support of restoration activities is advancing rapidly [3
] but to have useful impact, we need to understand what information is generalizable and broadly applicable, and when data is needed. For instance, increasing evidence advocates that promoting genetic diversity will improve the long-term sustainability of restored populations [5
]. A range of carefully crafted germplasm-sourcing strategies has been proposed to facilitate such practices, but the majority of these (26 at last count [3
]) are hampered by the paucity of information needed to identify the genetic and climatic boundaries on which they are based [7
]. Therefore, in practice, restoration actions often rely on generalizations about evolutionary boundaries and/or climatic suitability [8
]. Yet the evidence and methodological approaches necessary for appraising the suitability of taxonomic, distributional or other types of generalization are scarce at best [9
]. We avail of novel standardized datasets and methodological approaches to test if generalized provenancing strategies are reliable.
Evolutionary resilience is critical to restoration success [10
], and full recovery is only achieved when all key ecosystem attributes closely resemble those of a reference ecosystem, including the capacity of species and communities to adapt and evolve [2
]. The restoration of evolutionarily sustainable ecosystems and biodiversity is therefore reliant on the establishment of a strong link between contemporary fitness and longer-term evolutionary potential [11
]. As the necessary evolutionary, ecological and environmental information is rarely available, surrogate approaches are often relied upon to provide arbitrary guidance to seed sourcing strategies [13
Assumptions on the distribution of genetic diversity can potentially be misleading, and while “local is best” [14
] is increasingly viewed as a testable experimental hypothesis rather than a universal rule [3
], prioritizing areas of high diversity for one species is unlikely to capture the same degree of diversity for another. Provenancing strategies invoking the inclusion of broad genetic representativeness [5
], climate-adjusted strategies [16
] or the use of climatological data [17
] generally assume that a replicated approach is applicable across multiple species or, at a minimum, across related and/or co-distributed ones. Generalized expectations on the distribution of genetic diversity can originate from multi-species metanalyses (e.g., [18
] using a wide range of sampling and analytical strategies; [19
] relying on a more constrained but replicated approach) but it remains challenging to define replicable provenancing strategies from such studies alone.
Here we present a multispecies study aimed at testing the validity of provenancing generalizations based on taxonomic, distributional and functional similarity. We selected five Acacia
species with overlapping distributions across the Sydney Basin Bioregion and along the east coast of Australia (Figure 1
). The genus Acacia
(Mimosaceae) is used extensively for restoration practices in Australia and globally (e.g., http://acaciatreeproject.com.au/acacia-tree-project/
). In Australia acacias also have an important role in ecosystem function and dynamics, as they are prolific post-disturbance colonizers recruiting from soil seed banks, contributing to nitrogen fixation and providing food and shelter for a vast array of insects and vertebrates [20
Comparative investigations involving species representing functional or taxonomic groups commonly used in restoration practices can provide a better understanding of the relative strength of simplified provenancing strategies. This is particularly relevant when extensive overlaps in distribution, ecology and phylogeny are likely to impart localised practitioner communities with an expectation for evolutionary similarities. In order to test the validity of provenancing generalizations, we apply novel standardized methodologies and datasets across five co-distributed acacias frequently used in restoration practices and ask if overlap in distributional patterns mirror: (1) habitat preferences; (2) landscape-genomic patterns; and (3) empirically or arbitrarily defined genetic provenancing boundaries.
Provenance boundaries empirically estimated from patterns of genetic differentiation were noticeably different among five species of Acacia that are closely tied to an analogous vegetation type and often found in sympatry. We show that broad similarities in habitat requirements among congeneric taxa do not correspond to similar landscape-level dynamics nor to similar distribution of genetic variation. Our data confirms that matching provenancing strategies based on a perception of taxonomic, distributional, environmental or ecological similarities could lead to suboptimal choices. Based on the results presented here, we recommend the implementation of evolutionary-based methodologies to optimize restoration efforts.
Commonalities in the delineation of genetic provenances are unlikely to be the norm, as species respond differently to selective filtering processes and stochastic events through time. The purpose of this study was not to investigate and identify these biological and historical drivers. It is likely however that variations in breeding systems, dispersal mechanisms and response to disturbances gave rise to contrasting patterns of landscape-level connectivity and the genetic provenance boundaries derived from them. For instance, among the five Acacia
studied, self-compatibility was only reported for Acacia ulicifolia
] and A. suaveolens
, with current experimentation suggesting that selfing rates and related measures of fitness and viability can vary across a species’ distribution range (van der Merwe pers. comm.).
Differential responses to fire and recruitment from soil seed banks and/or resprouting capacity (Supplementary Table S1
) are also likely to impact on the distribution of diversity at population- and species-level. While most Acacia
respond to fire through recovery from the seedbank, previous studies suggest that A. ulicifolia
and A. terminalis
may have mixed responses to post-fire recovery. For example, our genomic data suggest that A. ulicifolia
is alone in displaying unexpectedly high levels of clonality (Supplementary Figure S1
). Clonality and the potential of resprouting after disturbance events that might have otherwise killed above-ground ramets, can decrease localised vulnerability [61
] but can also decrease within-population diversity and increase between-populations divergence (Figure 5
). For clonal species, a rapid shift in climate may inflict local extinction due to lack of evolvability and interestingly, A. ulicifolia
was the most difficult to collect in consistent numbers with historical occurrence records suggesting localised extinction patterns. Agamospermy could also explain the extensive geographic distribution of clones observed in this study, and while seed production through self-fertilization and agamospermy can both have short term evolutionary benefits (such as range expansion), the levels of genetic diversity captured through seed sourcing will greatly vary depending on the collecting strategy. Consequently, undetected clonality can significantly influence restoration outcomes.
The lack of similarity in provenancing boundaries and the difficulty in developing generalized guidelines [9
] is particularly important within highly localized contexts, where practitioners might rely on personal interpretations derived from locally replicated conditions and assemblages (Figure 1
and Figure 2
). It has been previously suggested that as anthropogenic influences continue to have an impact on natural systems, the simple protection of standing biodiversity is unlikely to suffice [2
]. Consequently, restoring vegetation needs to strike a balance between considering natural historical boundaries (as revealed by genetic structure), responding to contemporary conditions (resulting in loss of available habitat for example) and predicting climatic shifts (“future-proofing”). Consequently, the implementation of sourcing strategies that are conscious of both climatic requirements (current or future) and the distribution of genetic diversity, are critical to the success of ecological restoration.
Better access to relevant genomic and environmental data ushered a new era for evolutionarily informed restoration activities [63
] and enabled the implementation of novel methodologies that have become cost effective and easy to apply and interpret. Our replicable analytical and interpretational approach suggests that genetic provenancing areas, as defined by natural levels of gene flow, are often large and genetically diverse (Figure 4
). The species A. suaveolens
was a notable exception. This was possibly due to high levels of selfing or biparental inbreeding in this species (M. van Der Merwe in prep.), leading to high levels of drift and population genetic differentiation, including over relatively short distances. The relatively simple site-matching model developed here provides an additional mechanism to consider climate-related options within these comprehensive, genetically defined sourcing areas (Table 2
, Figure 4
). Simplifying the logistic requirements of germplasm-sourcing strategies while preventing localized over-harvesting, circumvents some of the limitations of current restoration practices [64
Finally, while we show that generalized provenancing guidelines and approaches need to be considered with caution, informative replicated patterns are still likely to emerge from large-scale, standardized, multispecies datasets. However, these will not necessarily be based on simple phylogenetic relationships or distributional similarities but will more likely denote shared functional and evolutionary histories [38