Plant Functional Traits - Science topic

According to the Human Genome Report, there are 30,000 genes in humans. The report provides the functionality of every gene, and accordingly, the disease can be found. CRISPR CAS 9 also works for the modifications in the genes and the fast genome sequencing analysis like in the Corona period could have helped even find the traits very easily by AI.

Reproductive fitness is the ability of an organism to produce offspring that survive and reproduce. It is a measure of how well an organism is adapted to its environment.

In the context of urban plants, reproductive fitness is important because it determines the ability of a plant to persist in a polluted environment. Plants that are able to reproduce successfully in polluted environments are more likely to survive and pass on their genes to the next generation.

There are a number of reasons why reproductive fitness is important for urban plants. First, pollution can damage plant tissues, making it difficult for them to reproduce. Second, pollution can reduce the availability of pollinators, which are essential for plant reproduction. Third, pollution can increase the risk of disease, which can also reduce plant reproduction.

Plants that are able to reproduce successfully in polluted environments have a number of adaptations that help them to cope with the challenges of the urban environment. These adaptations include:

By selecting pollution-tolerant plant species with high reproductive fitness, urban planners can help to create more sustainable and resilient urban environments.

Here are some examples of pollution-tolerant plant species with high reproductive fitness:

By planting these species in urban areas, planners can help to create more livable and sustainable cities.

This is a great point, however one of the advantages of using plant functional traits is that they are relatively easier to measure/obtain via online databases or through field measurements versus molecular techniques. For example, TRY is a global database that has pretty great coverage for commonly measured traits such as SLA, leaf nutrient concentrations, and plant height. Additionally functional trait diversity and composition correspond to fast versus conservative adaptive strategies (Diaz et al. 2016 and Reich 2014) and are related to ecosystem resilience (Laughlin et al. 2014), ecosystem functions (Diaz et al. 2007 and Zhang et al. 2015), and can help infer the community assembly processes that are structuring plant communities at different spatial scales (Lammana et al. 2014, Mouchet et al. 2010, and Thonicke et al. 2022). Finally, for applied ecologists (such as restoration ecologists) functional traits are useful monitoring indicator as there is evidence that functional diversity and composition responds predictably to restoration actions and that they capture greater complexity than species diversity or composition (Laughlin et al. 2017 and Morenos-Mateo. 2020).

Because of this, a functional trait framework remains relevant within the fields of community and restoration ecology.

Hi Dr Muthulingam Uday,

Please take a look at this link

Quantification of carbon based on traits is important to know the contribution of each component.

Hi Agnish Kumar Das , I'm not aware of a GEM equivalent (noting their site says they hope to expand to sites in India soon...) but I wonder if you can make a start using the Global Biodiversity Information Facility (https://www.gbif.org/ ). GBIF entries do have some trait info. Although a perfect, pre-defined functional trait search does not yet seem to be available, you can make do: for example, searching 'shade toleran*' in occurrences and then selecting a geographic area (available by latitude or by drawing a shape on the map under the 'location' menu dropdown) will return a list of species for you.

Here's a 2018 paper that might be of interest: Towards an Ecological Trait-data Standard, https://www.biorxiv.org/content/10.1101/328302v1.full.pdf

Dear Sir,

See next link:

Good luck,

That is very good approach. I think the database be useful too. But the first thing I had to do - the collection management system and the ontology linked to data entities. After that it the laboratory data management became a real.

Have you seen very close projects like Invenio (on base of Flask framework) or DKAN (on drupal framework)? It is opensource projects as I know. Also there is a drupal-based laboratory management system.

They include: whole plant traits, such as plant height or life form; leaf traits, such as specific leaf area (SLA), leaf size or leaf phenology; stem and belowground traits, such as specific root length or bark thickness; and finally regenerative traits, such as dispersal mode, seed mass or resprouting capacity

Dear Nicolás, I can't recall specific studies on that to be honest... what I have seen are studies relating the plant-soil interaction with a focus on water budget, but I can't recall that being used for planning purposes.

I found one article that could be mostly related to what you are looking for, hope it helps.

Hi Guillaume

I agree with Lucie Mahaut. Remember also that CWMs reflect the 'biomass-ratio hypothesis' (Grime, 1998; Garnier et al. 2004). This hypothesis states that ecosystem properties are driven by the characteristics of dominant species in the community. Biomass is a good way to assess species dominance because it is more closely related to species fitness. In traditional approaches, density or cover is usually used as a proxy of biomass when biomass cannot be assessed properly (in particular when you need a non-destructive way of assessing CWM). The bias towards tall species seems in your case relevant, as tall plants may be important competitors for crops. If you need information about plant demography, you could maybe consider seed traits.

All the best,

Karim

A paper we published last year suggested a pretty close coupling between photosynthetic rates and root biomass (Robin et al. 2018, Agronomy 8(11) 236) although granted this was 1) whole season data, 2) in hydroponics, 3) based on phytomer level root mass.

Our model suggested that C is allocated approximately equally between phytomer positions. Where the C supply was greater than that required for maintenance respiration, we found root growth. Where photosynthetic C supply to a phytomer position was less than that required for maintenance respiration, you'd get death. Of course, you'd expect fine roots would die before axial roots.

A paper we published this year (Irving et al. 2019 Journal of Plant Physiology 223 54-59) showed that irrespective of N supply C allocation to the roots was broadly the same. Admittedly, the N levels used were unnaturally high (to compensate for a short labeling period). We hypothesized that light intensity / photosynthetic rate was the rate limiting factor for shoot to root C supply. We're currently testing this.

So, I think light limitation would probably limit the growth of new, fine roots, especially if the C balance of shoot photosynthesis and root growth / respiration is as tightly coupled as our model suggests.

When you talk about NSC, if I assume you're thinking about starch, then I think leaf starch levels may be largely depleted overnight. If there was a strong demand for C by the roots, you might see competition. I don't know if that's been measured.

You have a great question, but like Dr Hogan, I'm not sure how much work has been done. I second his comments about drought, which I think has been shown in many previous studies.

great thank you! Ari Jokimäki

How about assign each pixel with the Land Cover with maximum value ?

In Mexico there is interest in the process of obtaining oil or amaranth honey

There are some jobs but they are in Spanish, I hope you can use the following links in English

https://www.youtube.com/watch?v=y4JP1FtKkiw

file:///C:/Users/UsuarioHP/Downloads/2189207.pdf

In-vitro screening and greenhouse screening of different germplasms against abiotic and biotic stresses , accomplished for different plant functional traits , when brought to main filed , do you feel , they will express differently, i dont think so , dear colleagues..

Yes, functional traits are expected to change at individual level, between ontogenetic stages and also in response to environmental variation, although inter specific variation in traits in general is larger. You should see this recent paper in Ecography about trait variation across scales, including variation within individual, between individual, between species, and among communties along environmental gradients:

Messier et al 2017. Trait variation and integration across scales: is the leaf economic spectrum present at local scales?

Thank you so much for your response!

I will follow your advices carrefully.

Dear Colleagues,

Tahnk you very much for your suggestions. That will be very helpfull for my studies.

Cordially

This paper may help you. Wright, I. J., et al. 2004. The worldwide leaf economics spectrum. Nature 428:821-827.

Dear Jorge,

If your main interest is in the trait differences between habitats, then you have two additional options:

(1) Treat all traits as a matrix of response variables (multivariate normally distributed) and enter them in a multivariate linear model e.g. using lm() followed by Anova() in John Fox´s "car" library:

model1=cbind(trait1,trait2,trait3...)~habitat

Anova(model1)

You can set up contrast matrices for the response matrix to test individual differences and hypotheses on sets of traits. You may also introduce random effects if desired.

See an example provided in the link below.

(2) Alternatively, you could just look at individual traits or groups of traits in structural equation models, e.g. using the lavaan or piecewiseSEM packages.

I hope these suggestions are helpful in some way for you - don´t know if they work but maybe worth a try.

Best wishes,

Christoph

There is an updated version of the previously mentioned handbook, authored by Pérez et al. You can download it from Researchgate:  https://www.researchgate.net/publication/259741364

This new handbook will not give you the perfect and ultimate answer, since it depends on several factors, but I think you will find it useful. In Box 2  the authors discuss "Which traits to measure to answer which questions?" . 

Dear Wayne, many thanks. I will check out the IPCD framework. However, I have already a large set of traits. Maybe I can reduce them to match the IPCD.

A former postdoc of mine addressed the complex role of nutrient interactions between three subalpine species. I have attached one of his papers here which might provide a helpful lead to other more relevant material for you

Monika , very good question . Plant functional traits and sink capacity of soil for carbon storage are surely inter-linked. I feel , there are two over-riding parameters defining plant  functionality in terms of carbon sequestration are photosynthesis and respiration  as net photosynthesis. While soil parameters are :   texture , organic carbon , soil depth and soil microbial load . It is worth mentioning here to store carbon in passive pool and act upon active carbon pool since passive carbon pool has multifold functionality  with regard to carbon sequestration .   

Felix -

Interesting study - I have done similar work that is closely related, and may change how you think about the problem (see attached).  Make sure you think through sampling and replication issues thoroughly in addition to traits: you need to have more than N=1 replicate of each forest type.

On your question, there is perhaps no "correct" list of traits, but certain traits are more closely linked to conservative vs. acquisitive growth strategies than others.  Your list includes some but not all of the Leaf Economics Spectrum traits (i.e., you have leaf N, leaf P, SLA, and leaf lifespan, but are missing photosynthetic capacity and leaf dark respiration).  I would argue that some measure of whole-plant light compensation point is the best direct measure of shade tolerance (and thus of conservative vs. acquisitive growth strategies), but this is also difficult to measure.  Along these lines crown-level leaf area index is very difficult to measure, and there are in fact very few data published on this in the literature.  It is much more feasible to measure canopy gap fraction, but even that is laborious (see second paper attached).

Vielen glück!

I would measure the tissue flammability. See more at Cornelissen J, Lavorel, Garnier, Díaz, Buchmann, Gurvich, Reich, ter Steege, Morgan, van der Heijden M, et al. 2003. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian Journal of Botany 51: 335.

Muito obrigado. No momento estou no Pará coletando, mas assim que volta a Recife passo com você sim. abrç

Lawren Sack and Masher Waite published a set of papers on moss leaf economics and ecophysiology that you can use. See below:

Waite, M., & Sack, L. (2010). How does moss photosynthesis relate to leaf and canopy structure? Trait relationships for 10 Hawaiian species of contrasting light habitats. New Phytologist, 185(1), 156-172.

Waite, M., & Sack, L. (2011). Does global stoichiometric theory apply to bryophytes? Tests across an elevation× soil age ecosystem matrix on Mauna Loa, Hawaii. Journal of Ecology, 99(1), 122-134.

Waite, M., & Sack, L. (2011). Shifts in bryophyte carbon isotope ratio across an elevation× soil age matrix on Mauna Loa, Hawaii: do bryophytes behave like vascular plants?. Oecologia, 166(1), 11-22.

Hello. Try is a very good database but not so user friendly.

You can also try on CABI, EOL, and ISSG. There are some information on biological traits for the plants.

Nathalie,

I have already performed this kind of factorial analysis on an invertebrate dataset (biological traits). As suggested by Alain, the RLQ analysis is a first option to investigate the link between biological and environmental tables. But if you wish to assess only the relationship between a fuzzy-coded biological table (typically species traits) and an environmental table, you can also perform either a CCA or a CAIV.

Using the ade4 package in R as advised above, the two following sequences gave the same result in my case:

1) first perform a FCA on the fuzzy-coded array (needs the ‘prep.fuzzy.var’ and ‘dudi.fca’ functions in ade4) and then a CAIV between the FCA result and the environmental table (‘pcaiv’ function)

2) perform directly a CCA between the fuzzy-coded array and the environmental table (‘prep.fuzzy.var’ and ‘cca’ functions)

Hope that helps.

Mathieu

In attach a review about important root traits for ecological engineering