Ant Quality Index: an ant-based approach for assessing restoration success of grasslands from the ground up
The goals of restoration ecology are to repair damage and return functionality of ecosystems. A challenge of restoration ecology is developing assessment criteria for gauging restoration success. The Floristic Quality Assessment Index (FQAI) is a plant based method to determine restoration success that uses Coefficients of Conservatism (CC) values from expert knowledge of plant habitat fidelity. Plant related variables (i.e. richness, FQAI) are often good predictors for plant dependent arthropods (pollinators and herbivores). However, it is not established whether FQAI can reliably gauge restoration of other arthropod functional groups.
We argue that while FQAI may be adequate for plant dependent arthropods, the functionality of a given system is incomplete without considering higher trophic levels and below-ground communities. We suggest that ant diversity could be an easily measured index that spans higher trophic interactions and soil communities. Ants are frequently used as bioindicators for restoration in Australia, because they are diverse, easily collected and identified, and interact with the ecosystem at multiple levels. More importantly, ants are responsive to changing environments and demonstrate habitat fidelity.
For this project we developed an Ant Quality Index (AntQI) to measure habitat quality of constructed grasslands in Ohio. We modeled our ant index after the well-established FQAI system and the Auchenorrhyncha Quality Index. Ant species in the grasslands were given CC values, 0-17, based on life history and county records. We calculated the AntQI for each site as meanCC*sqrt(ant spp). AntQI and FQAI scores were compared for effectiveness in predicting bees, butterflies, predatory beetles and ant-associated mites.
AntQI was not significantly correlated with FQAI, but had a significant negative relationship with plant richness (p=0.0088,dev.exp.3.8%). AntQI predicted ant-associated mite richness (p<0.0001,dev.exp.36.8%), beetle richness (p=0.014,LRT=6.08), butterfly abundance (p=0.007,LRT=7.26), and was positively correlated with site age (p<0.0001,dev.exp.28.1%). FQAI was a better predictor of butterfly richness (p=0.0003,LRT=13.23) and abundance (p=0.002,LRT=9.50), bee (p=0.006,LRT=7.58), and beetle abundance (p=0.016,LRT=5.91), and was negatively related to age (p=0.041,dev.exp.4.5%). While FQAI may be useful as a predictive tool for insect pollinators and herbivores, our study suggests it is less useful for predicting the development of soil arthropods that interact in both above- and below-ground food webs. Consistent with other studies that promote ants as bioindicators in restoration following soil disturbance, the AntQI was associated with site age and mite communities, both of which likely reflect the development of soil organic matter and detritivore food webs during the restoration of ecosystem functions.