Testing the hypothesis of hierarchical predictability in ecological restoration and succession

Abstract

To advance predictive ecology, the hypothesis of hierarchical predictability proposes that community measures for which species are interchangeable (e.g., structure and species richness) are more predictable than measures for which species identity matters (e.g., community composition). Predictability is hypothesized to decrease for response measures in order of the following categories: structure, species richness, function, and species composition. We tested this hypothesis using a 14-year, oak savanna-prairie restoration experiment that removed non-native pine plantations at 24 sites in northwestern Ohio, USA. Based on 24 response measures, the data showed minimal support for the hypothesis, because response measures varied in predictability within categories. Half of response measures had over half their variability modeled using fixed (restoration treatment and year) and random plot effects, and these "predictable" measures occurred in all four categories. Pine basal area, environment (e.g., soil texture), and antecedent vegetation accounted for over half the variation in change within the first three post-restoration years for 77% of response measures. Change between the 3rd and 14th years was less predictable, but most restoration measures increased favorably via sites achieving them in unique ways. We propose that variation will not conform with the hypothesis of hierarchical predictability in ecosystems with vegetation dynamics driven by stochastic processes such as seed dispersal, or where vegetation structure and species richness are influenced by species composition. The ability to predict a community measure may be more driven by the number of combinations of casual factors affecting a measure than by the number of values it can have.

Assessing restoration success by predicting time to recovery—But by which metric?

“…Although species composition may provide indications of a much wider range of ecosystem properties than aggregated, more generic properties of vegetation such as total cover and species richness, the latter are much more often used to assess restoration success (Ruiz‐Jaén & Aide, ; Waldén & Lindborg, ). Which metrics to use is still vigorously debated (Abella, Schetter, & Walters, ; Brudvig et al, ; Durigan & Suganuma, ; Reid, ). In general, we expect species composition to recover more slowly than total cover and species richness.…”

Predicting landscape‐scale biodiversity recovery by natural tropical forest regrowth

“…In tropical trees, plot‐scale variation in species richness is tightly linked to variation in tree abundance (Condit et al., 1996); therefore, it is unsurprising that recovery of plant richness and total abundance present the same amount of variation. It is important to note that other researchers comparing the predictability of distinct ecological metrics in the context of ecosystem restoration focused exclusively on plants (Abella et al., 2018; Laughlin et al., 2017; Rosenfield & Müller, 2017), highlighting the need to expand the taxonomic scope of predictive restoration ecology.…”

Assessing recovery of alpine spoil heaps by vascular plant, bryophyte, and lichen functional traits

“…Time‐to‐recovery predictions by ORBA for our study sites showed similar degrees of uncertainty, expressed as 95% CI of model estimates, for functional trait composition (this study) and species composition (Rydgren et al 2020). Laughlin et al (2017) found functional trait‐based metrics to be less variable and more predictable than species composition‐based metrics for a variety of restoration treatments, while Abella et al (2018) questioned the existence of a general hierarchy of predictability. Thus, the two types of metrics should be considered as complementary for predictions of time to recovery as well as for recovery assessments.…”