Many scientific papers deal with aspects of plant interactions including competition and facilitation. It is very likely that plant interactions play an important role in structuring our ecosystems and this is indeed a prolific research field that has kept many researchers busy for the last hundred years. In the absence of a deeper understanding of the physiological mechanisms involved in interactions, plant performance (in terms of growth and yield) was often used as an indicator of plant interaction patterns. Given the same environmental conditions, a lack of performance may be an indicator of competition and surplus performance may indicate dominance or facilitation, but this does not always need to be the case.
Stem growth of trees can, for example, be considered a performance variable. A common assumption based on ecological theories is that trees with few competitors, i.e. growing in quite open conditions, respond more in terms of growth than those that grow in tight neighbourhoods. Research in tree mechanics, however, suggests that what is interpreted as competition effects, has more to do with a tree’s need to adjust to environmental challenges, particularly to withstand the forces of wind. A tree protected by neighbouring trees in a tight neighbourhood does not need to invest much in stem growth because it is sheltered from the wind, whilst another tree growing in more open conditions is strongly affected by wind. Considering this the growth response merits a very different interpretation.
Promising research is currently underway attempting to uncover the physiological receptors and processes related to interaction. This will undoubtedly put us into a better situation to review and select appropriate criteria for identifying the mode and intensity of interactions.
In this context, individual-based models assume an important role in exploring plant interactions. This model type is in fact specialised on handling interactions between individuals or agents. The behaviour of a system, for example a forest ecosystem, is the result of individuals acting and interacting in a given environment. The same applies to the projection of growth or mortality, which no longer is the result of statistical estimations but a consequence of individual “decisions” and interactions. Thus following this modelling philosophy interaction is handled in a very mechanistic way and can also be linked with physiological receptors and processes. Individual-based modelling is also very intuitive and creative. Many software packages such as NetLogo and the Java Agent-Based Modelling Toolkit have been developed to support this modelling approach, however, R and Python are also good choices to begin with. Individual-based modelling is not limited to any particular field of science and shares interesting links with physics and point process statistics.
An excellent opportunity for exploring the world of A/IBMs is a workshop that my group is offering at Umeå (Sweden) on 21/22 August 2018, see our workshop website. In this workshop we will have a few keynote talks followed by talks of the workshop participants. Every participant can highlight anything they consider important, e.g. why they are interested in IB modelling, what they perceive as challenge or programming problems they ran into and want to share. The workshop is not limited to any particular ecosystem or agent and I believe the exchange across (eco)systems and specific applications can be very inspiring and eye-opening. If you haven’t signed up yet please consider the opportunity and get in touch!
Thank you for sharing and making this knowledge available for any student to see. I really appreciate the ecological background that supports your discussions. I am currently studying tree interactions in southern Brazil, and sometimes we end up seeing only through statistical lenses, and do not focus or do not take into account some ecological theories that should underpin our research.
Lastly, How do you think one should approach interactions among trees when there are many species? I mean, a given tree A has its own response or reaction to a certain competition load, but if we replace that tree A for a tree of another species, the response would be different. Also, these interactions can be so complex that not just the response itself could be different, but also the competition load can change (like if the neighbour tree J has more or less competitive power over the target tree based on the target tree species). But how can we handle all this information when there are 100 species in the forest?
Thank you for your words of appreciation. Yes, it is correct, individuals of different species exert different competition signals but also handle the same signals but from different species in different ways. There are a number of approaches to this and would take too long to go into detail here. In ecosystems with many species it may be useful to go by trait groups.