Species dominance and biodiversity in plant communities have received considerable attention and characterisation. However, species codominance, while often alleged, is seldom defined or quantified. Codominance is a common phenomenon and is likely to be an important driver of community structure, ecosystem function and the stability of both. Here we review the use of the term ‘codominance’ and find inconsistencies in its use, suggesting that the scientific community currently lacks a universal understanding of codominance. We address this issue by: (1) qualitatively defining codominance as mostly shared abundance that is distinctively isolated within a subset of a community, and (2) presenting a novel metric for quantifying the degree to which relative abundances are shared among a codominant subset of plant species, while also accounting for the remaining species within a plant community. Using both simulated and real‐world data, we then demonstrate the process of applying the codominance metric to compare communities and to generate a quantitatively defensible subset of species to consider codominant within a community. We show that our metric effectively distinguishes the degree of codominance between four types of grassland ecosystems as well as simulated ecosystems with varying degrees of abundance sharing among community members. Overall, we make the case that increased research focusses on the conditions under which codominance occurs and the consequences for species coexistence, community structure and ecosystem function that would considerably advance the fields of community and ecosystem ecology.
- Award ID(s):
- 1920908
- PAR ID:
- 10211603
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 12
- Issue:
- 11
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 1739
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Summary -
more » « less
Abstract The illegal timber trade has significant impact on the survival of endangered tropical hardwood species like
Dalbergia spp. (rosewood), a world‐wide protected genus from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Due to increased threat toDalbergia spp., and lack of action to reduce threats, port of entry analysis methods are required to identifyDalbergia spp. Handheld laser‐induced breakdown spectroscopy (LIBS) has been shown to be capable of identifying species and establishing provenance ofDalbergia spp. and other tropical hardwoods, but analysis methods for this work have yet to be investigated in detail. The present work investigates five well‐known algorithms—partial least squares discriminant analysis (PLS‐DA), classification and regression trees (CART),k ‐nearest neighbor (k ‐NN), random forest (RF), and support vector machine (SVM)—two training/test set sampling regimes, and data collection at two signal‐to‐noise (S/N) ratios to assess the potential for handheld LIBS analyses. Additionally, imbalanced classes are addressed. For this application, SVM and RF yield near identical results (though RF takes nearly 100 longer to compute), while the S/N ratio has a significant effect on model success assuming all else is equal. It was found that forming a training set with replicate low S/N analyses can perform as well as higher precision training sets for true prediction, even if the predicted samples have low signal to noise! This work confirms handheld LIBS analyzers can provide a viable method for classification of hardwood species, even within the same genus. -
Understanding how environmental adaptations mediate plant and ecosystem responses becomes increasingly important under accelerating global environmental change. Multi-stemmed trees, for example, differ in form and function from single-stemmed trees and may possess physiological advantages that allow for persistence during stressful climatic events such as extended drought. Following the worst drought in Hawaii in a century, we examined patterns of stem abundance and turnover in a Hawaiian lowland dry forest (LDF) and a montane wet forest (MWF) to investigate how multi-stemmed trees might influence site persistence, and how stem abundance and turnover relate to key functional traits. We found stem abundance and multi-stemmed trees to be an important component for climate resilience within the LDF. The LDF had higher relative abundance of multi-stemmed trees, stem abundance, and mean stem abundance compared to a reference MWF. Within the LDF, multi-stemmed trees had higher relative stem abundance (i.e., percent composition of stems to the total number of stems in the LDF) and higher estimated aboveground carbon than single-stemmed trees. Stem abundance varied among species and tree size classes. Stem turnover (i.e., change in stem abundance between five-year censuses) varied among species and tree size classes and species mean stem turnover was correlated with mean species stem abundance per tree. At the plot level, stem abundance per tree is also a predictor of survival, though mortality did not differ between multiple- and single-stemmed trees. Lastly, species with higher mean stem abundance per tree tended to have traits associated with a higher light-saturated photosynthetic rate, suggesting greater productivity in periods with higher water supply. Identifying the traits that allow species and forest communities to persist in dry environments or respond to disturbance is useful for forecasting ecological climate resilience or potential for restoration in tropical dry forests.more » « less
-
more » « less
Abstract Hummingbirds are important pollinators of many native Neotropical plants but their abundance and diversity in landscapes dominated by intensive human uses such as agriculture have rarely been examined, despite such land‐uses prevailing in the tropics. We examined how tropical deforestation affects hummingbird community structure in premontane forest patches embedded in a tropical countryside of Coto Brus Canton, Costa Rica. We captured hummingbirds in fourteen landscapes representing a gradient in patch size and forest amount, and tested for the effects of these variables on (1) hummingbird captures at flowers (pollinator availability); (2) species richness; and (3) filtering of functional traits. After accounting for sampling effects, both hummingbird availability and species richness declined by 40% and 50%, respectively, across the gradient in deforestation that we observed (9–66% forest within 1000 m). Focal patch size was the strongest predictor, even after statistically accounting for the amount of forest and matrix composition of landscapes. These reductions in availability and richness were well predicted by functional traits; morphologically specialized species with the capacity to transport long‐distance outcrossed pollen and low functional redundancy within the pollinator network showed the greatest sensitivity to landscape change. We hypothesize that declines in hummingbird availability, diversity, and functional traits are important mechanisms driving the observed pollen limitation of ornithophilous flowers in fragmented tropical landscapes. Efforts to conserve large forest patches and enhance matrix permeability are critical for maintaining forest hummingbird communities and pollination services under current and predicted deforestation regimes.
-
more » « less
Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM’s performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs12111739
