Kin recognition in plants: a mysterious behaviour unsolved
Bibliographic record
Abstract
Chemical communication plays an integral part in the rhizosphere and in larger ecological processes. The idea of kin recognition and perception of kin-specific chemical signals have long been recognized in microbial and animal models. By contrast, general chemical communication between plants has been well established, especially with regard to the negative communication between plants of two different species. Recent studies have shown that plants, too, have the ability to recognize other plants in their surroundings based on relatedness and identity. To date, the fields of plant kin recognition are met with several inconsistencies of data and conflicting results. Little molecular evidence has been provided for root secretions and, to date, no molecular evidence for understanding kin recognition has been presented, leaving many questions in the field unanswered. This review attempts to provide a background to this intriguing topic and to encourage further studies to decipher kin interactions in plants. Biologists have long accepted that diverse animal species have evolved means to recognize and interact with other members of their species, often specifically kin members to enhance their survival. More recently, studies have shown that various microbes, some of the simplest life forms, also have the ability to recognize their kin. So why is it surprising that studies have shown that plants, too, can recognize and interact with their kin? Although they are not mobile, plants are not passive organisms. It is for this immobility that they have evolved many biochemical mechanisms to sense and respond to abiotic factors in their environment such as changes in light, temperature, humidity, and soil pH. Plants are also known to sense and respond to biotic stresses such as herbivory and pathogen attack. In addition to these plant–insect and plant–microbe interactions, plant– plant interactions have been well documented ranging from negative interactions such as allelopathy to positive interactions such as the release of volatile compounds as a warning to other plants (Bais et al., 2006; Dicke and Baldwin, 2010). Plant–plant interactions can be interspecies or intraspecies communications; a perfect example of interspecies communication comes from plant species which use chemicals to inhibit the growth of other plants. The most elegant example is the recent work on the chemical effects of garlic mustard (Alliaria petiolata), an invasive understorey forb in North American forests (Stinson et al., 2006; Wolfe et al., 2008). Garlic mustard secretes benzyl isothiocyanate, which inhibits the growth of mycorrhizal fungi that support tree diversity. Recent studies also suggest that other invasive plants, such as Canada goldenrod (Solidago canadensis), Centaurea stoebe, and narrow-leaf cattail (Typha angustifolia), may produce allelochemicals that inhibit native plants directly (Bais et al., 2003; Perry et al., 2005; Abhilashsa et al., 2008; He et al., 2009; Jarchow and Cook, 2009; Thorpe et al., 2009; Tharayil and Trubessiweir, 2010). Perhaps we can examine interactions to reveal a more specific recognition and examine behaviour between plants that are closely related versus plants that are unrelated within the same species? Recently, Dudley and File (2007) reported that an annual plant, Cakile edentula, exhibited kin recognition, as the plants produced more roots when grown in pots with strangers (plants of the same species, but grown from seeds collected from different mother plants) versus being grown with kin plants (plants grown from seeds collected from the same mother plant) (Dudley and File, 2007). It has not been established, however, if the difference in root growth between plants grown with kin or grown with strangers is due to altruism between kin or increased competition between strangers (Dudley and File, 2007; Callaway and Mahall, 2007). Since this was the first study to highlight such a specific interaction in plants, reactions to this pioneering work were divided (Klemens, 2008). Despite the differing opinions of scientists in the field, the work of Dudley and File (2007) has inspired interest in plant kin interactions, resulting in a handful of recent studies (Dudley and File, 2007). The following aims to present a brief background in the general field of kin recognition studies in order specifically to draw attention to advances in kin recognition in plants and to stimulate increased research in this budding area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".