bioscience – aarhus university measuring competitive interactions in natural plant populations...

Bioscience – Aarhus University

Measuring competitive interactions in natural plant populations

Christian DamgaardBioscience

Aarhus University


Evidence for competitive interactions in plant communities

How often has the competitive interactions in a plant community been measured in a natural plant community compared to the times competition has been postulated to be an important ecological mechanisms?

1/1000 is certainly too high 1/1000,000?

Measuring interspecific interactions in natural ecosystems is a non-trivial task and the results are still too sparse to allow generalization across different plant communities or even among years (Turnbull et al. 2004)


Measuring competitive interactions in natural plant communities

Competition is a process occurring in time Just as you need more than one measurement in time to

measure a velocity or a rate, you need more than one measurement in time to measure the effect of the neighboring plants on growth (= change in biomass) of the target plant species

Longitudinal (=time series) plant abundance data Non-destructive sampling


Measuring ecological success

𝑑𝑁𝑑𝑡 = 𝑓 (𝑁 ) 𝑁 ?


Measuring ecological success Natural and semi-natural plant

communities are dominated by spatially structured perennial species with variable life histories

Often difficult to count individual plants

Large size variation among individuals of the same species.

The ecological success may be assessed using the pin-point method

cover (= relative area that the species cover) vertical density (= 3D space occupancy ~

biomass, plant volume, and LAI)

𝑁 ?


Plant cover and vertical density – assumptions

i) Plant cover and vertical density measure ecological success

ii) Due to the growth form of most plant species, the vertical density will increase relatively faster than plant cover during the growing season

iii) Species with a high cover in spring will have relatively high vertical density at the end of the growing season, however, the vertical density is reduced by the cover of other species due to competition

year t year t 1

iv) A plant species that grows to a relatively high vertical density has a relatively high cover the following year, i.e., plants allocate resources into occupying resource space the following year


Plant cover and vertical density – model

X: coverY: vertical density

P1: competitive growthP2: survival, establisment etc.



Competitive growth (P1):

Yi = increasing function of Xi * decreasing function of X j,k,…

Survival and establishment (P2):

Xi,y+1 = increasing function of Yi,y * decreasing function of Y j,k,…,y



Competitive growth (P1):

Survival and establishment (P2):

ryiPd

rytkrkd

rytjrjb

rytirirytikji XzcXzcXzaY ,,,1,,1,,,1,,,1,,,2, ))(exp())(exp()(

ryiPd

rytkrkd

rytjrjb

rytirirytikji YzcYzcYzaX ,,,2,,2,,,2,,,2,,1,1, )))(exp())(exp()((logit)(logit

constant slope( )z z

Structural variation: for predictions, , ,P i y r

Effect of environment:

M1: ), (Binomial~ ,,,,,, rytiryti Xnu 1

M2: ),(g~ ,2,,,,,, iMrytiryti YPv 1


The effect of glyphosate on the competitive interactions between Festuca ovina and Agrostis capillaris

Christian DamgaardBeate Strandberg

Solvejg K. Mathiassen Per Kudsk

Aarhus University


Festuca ovina and Agrostis capillaris

Permanent plots with mainly F. ovina and A. capillaris in different proportions

Four glyphosate levels: 0, 1, 5 and 25% of label rate (1440 g glyphosate/ha)

Cover and vertical density was measured twice a year for three years : two weeks after herbicide application

in the spring

: at the end of the growing season

Festuca ovina (solid)Agrostis capillaris (stripped)


Estimating the competitive interactions

The joint Bayesian posterior distribution of the parameters and latent variables for cover and vertical density were calculated using a Markov chain Monte Carlo method (Metropolis-Hastings)

The prior distributions of all parameters and latent variables were assumed to be uniformly distributed in their specified domains. Relative informative priors of b, d, and were in initial explorations of the model found to be necessary in order to ensure a successful fitting procedure.

Plots of the deviance and the sampling chains of all parameters and latent variables were inspected in order to check the fitting and mixing properties of the used sampling procedure.


Marginal distribution of competition parameters

Festuca ovina Agrostis capillaris Other species

Percent. 2.5% 50% 97.5% 2.5% 50% 97.5% 2.5% 50% 97.5%

Process equation 1 – during the growing season:

cconstant 0.612 0.835 0.965 0.161 1.940 2.641 -0.688 0.128 0.638

cslope 0.114 0.139 0.159 -0.509 -0.342 -0.082 -0.081 0.132 0.542

Process equation 2 – among growing seasons:

cconstant 0.413 2.279 4.335 -0.017 0.015 0.057 -0.034 0.013 0.095

cslope -0.122 0.001 0.202 -0.002 0.002 0.007 -0.013 -0.003 0.009

Significant competitive effect Competitive effect depends on glyphosate


Importance of competitive interactions relative to the effect of glyphosate

Importance of competition is highest in the growing season

Importance of competition on A. capillaris increases with glyphosate

( , )

( , ) ( , )j

j

X i

X i z i

Y X z

Y X z Y X z


Competitive interactions between Festuca ovina Agrostis capillaris - summary

F. ovina and A. capillaris compete for resources!

The competitive effect of F. ovina increases with glyphosate

The competitive effect of A. capillaris decreases with glyphosate

Importance of competition is highest in the growing season

Importance of competition of F. ovina on A. capillaris increases with glyphosate

Structural equation models allow the separation of structural and measuring variance

Quantitative ecological predictions with a known uncertainty


Competitive interactions were measured using the pin-point methodI. Effect of climate change (temperature, draught, C02), on the

competitive interactions between Calluna vulgaris and Deschampsia flexuosa in dry heathlands Draught increased the probability that Deschampsia would

outcompete Calluna (Damgaard et al. 2009, Ransijn et al., 2015, in prep.)

II. Effect of glyphosate and nitrogen on the competitive interactions of Festuca ovina and Agrostis capillaris Glyphosate increased the competitive effect of Festuca ovina

(Damgaard et al. 2012, 2014)

III. Effect of water level on the competitive interactions among grassland species The importance of competition varied with water level (Merlin et al,

2015)

IV. Measure the competitive interactions of different plant traits (Damgaard, submitted)


Evidence for competitive interactions in plant communities II

Competition is measured by the effect of neighboring plants on plant growth longitudinal plant abundance data

Macro-ecological assumption I: the biomass of neighboring plant is used as a proxy for interspecific interactions Variation in total biomass due to plot heterogeneity

Macro-ecological assumption II: plant communities are assumed to in equilibrium Generally known not to be true Problematic to assume that plant abundance is in equilibrium in a test

of the very nature of the ecological mechanism that control plant abundances


“Demography” of space occupancy

Christian DamgaardDepartment of Bioscience

Aarhus University


“Demography” of space occupancy Often difficult to distinguish

individuals in natural and semi-natural plant communities

Instead of measuring individual plants, record if a species is present at a fixed pin-position in at least two years

If a new species is recorded at a position, then a colonisation event may have occurred

If a species disappear at a position, then a mortality event may have occurred


Events and transition probabilities

Event Description Transition probability

X1:At, At+1 A plant species A was present in year t and was also present in year t+1 p( s(z) + 1 ( 1 – s(z) ) c(z) )

X2:nAt ,nAt+1 A plant species A was not present in year t and was also not present in year t+1 ( 1 – p ) (1 – c(z) )

X3:At ,nAt+1

A plant species A was present in year t but was not present in year t+1

(indicates a possible mortality event)p( 1 – p ) (1 – c(z) )

X4:nAt , At+1

A plant species A was not present in year t but was present in year t+1

(indicates a possible colonization event)( 1 – p ) c(z)


Change in cover and elasticity

𝜋=𝑝 ′

𝑝 =𝑝𝑋 1

+𝑝𝑋 2

𝑝𝑋 1+𝑝𝑋 3

=𝑐(𝑧 )𝑝 +𝑠 (𝑧 ) −𝑐 (𝑧 )𝑠 (𝑧 )

𝑐( 𝑧)𝜋

𝜕𝜋𝜕 𝑐(𝑧)=

𝑐 (𝑧 )− (𝑝𝑐 (𝑧 ) 𝑠 (𝑧 ) )𝑐 (𝑧 )+(𝑝 𝑠 (𝑧 ) )− (𝑝𝑐 (𝑧 )𝑠 (𝑧 ) )

𝑠 (𝑧)𝜋

𝜕𝜋𝜕𝑠 (𝑧 )

=1 − 𝑐 (𝑧 )𝑐 (𝑧 )+(𝑝 𝑠 (𝑧 ) ) (1 −𝑐 (𝑧 ) )


Demography along a flooding gradientCollaboration with Anne Bonis, Rennes

Marais du Poitevin, France

,


Survival and colonisation probabilities as a function of the flooding gradient (z)

Species

2.5% 50% 97.5% 2.5% 50% 97.5% 2.5% 50% 97.5% 2.5% 50% 97.5%

Agrostis stolonifera 0.333 0.473 0.604 -0.002 0.022 0.039 0.394 0.455 0.510 0.021 0.033 0.040

Cynosurus cristatus 0.042 0.117 0.192 -0.011 -0.001 0.020 0.072 0.112 0.161 -0.010 -0.006 0.001

Lolium perenne 0.048 0.238 0.457 -0.029 -0.009 0.019 0.226 0.278 0.336 -0.022 -0.018 -0.012

Hordeum secalinum 0.004 0.114 0.455 -0.017 0.007 0.052 0.167 0.247 0.346 -0.022 -0.014 -0.002

Carex divisa 0.004 0.086 0.284 -0.008 0.024 0.054 0.063 0.108 0.168 -0.006 0.002 0.012

Poa trivialis 0.003 0.066 0.365 -0.019 0.012 0.056 0.112 0.194 0.308 -0.019 -0.008 0.010


Elasticity along a water gradient

Abundance Elasticity

p :0.90.50.10.01


Elasticity along a water gradient

Abundance Elasticity


Demography and the niche

Results support the hypothesis that colonization has a predominant role for determining the ecological success along a hydrological gradient compared to survivalConnection between the Hutchinson realized niche and demography Maguire (1973)

Results suggests that it may be possible to predict the realized niche of different species from demographic studies

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