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Plant Science > Kew Internship > Flashcards

Kew Internship Flashcards

1
Q

RBG Kew Study Limitations

A

Our study does have certain limitations that need to be acknowledged. Firstly, the utilisation of herbarium specimens, which consist of dried material, poses some challenges. It is important to note that these specimens vary in quality, and that some are damaged or degraded, potentially impacting the accuracy and reliability of our observations.

Another limitation is related to georeferencing, which relies on the accuracy of the data recording. The precision of our georeferencing is contingent upon the quality and completeness of the available data. Therefore, any inaccuracies or limitations in the georeferencing data may influence the reliability of our findings.

Additionally, our study is constrained by a relatively small sample size. As a result, it becomes challenging to generate statistically significant p-values. However, it is crucial to emphasize that our approach is exploratory in nature, aiming to uncover initial trends and patterns that can guide further research in this area.

Furthermore, it is important to understand that the relationship between stomatal size and drought tolerance is not universally linear. Different plant species and genotypes may exhibit distinct responses to variations in stomatal size under drought stress. Moreover, a plant’s overall drought tolerance is influenced by various traits, including stomatal density, leaf anatomy, and root characteristics. Therefore, it is essential to consider these additional factors alongside the four chosen leaf traits when evaluating a plant’s response to drought conditions.

Additionally, there is an ongoing debate in the field of functional ecology regarding the predictability of traits from environmental factors and the possibility of isolating single climatic variables and traits amid the complexity of drought conditions. This discussion highlights the challenges and complexities associated with understanding and predicting plant responses to drought, underscoring the need for comprehensive research and analysis in this field.

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2
Q

Fourth Corner Analysis

A

Fourth corner analysis allows the researcher to explore the relationship between species traits, environmental variables, and community composition. It can help identify the environmental variables that are most strongly associated with the distribution of species traits.

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3
Q

Phylogenetic Principal Component Analysis (PCA)

A

PPCA is an extension of the traditional PCA method that incorporates information about the phylogenetic relationships among species.
- It considers the evolutionary relatedness of species when analysing and interpreting patterns of variation in multivariate data.

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4
Q

NMDS

A

Non-metric Multi-Dimensional Scaling is a technique used for visualizing and analysing dissimilarity or distance matrices.
- multiple variables and want to explore the similarity or dissimilarity patterns among your samples.

  • If samples with similar trait values tend to cluster together or show a gradient in the reduced-dimensional space, the pattern would suggest a potential association between the leaf traits and climatic variables.

NMDS is a nonlinear ordination method that can handle complex relationships between variables.

Unlike PCA, NMDS does not assume linear relationships or normality. This flexibility makes NMDS suitable for capturing non-linear patterns and exploring complex ecological data.

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5
Q

What is an ANOVA?

A

ANOVA tells you if there are any statistical differences between the means of three or more independent groups.

If there is a lot of variance (spread of data away from the mean) within the data groups, then there is more chance that the mean of a sample selected from the data will be different due to chance

All these elements are combined into a F value, which can then be analysed to give a probability (p-value) of whether or not differences between your groups are statistically significant.

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6
Q

What is an eigenvalue?

A

PCA
- variation in distance along each principal component

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7
Q

What is continuous variable?

A

A continuous variable is a variable that can take on any value within a range (infinite values)

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8
Q

What is a discrete variable?

A

A discrete variable is a variable that takes on distinct, countable values

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9
Q

Summarise your palm findings

A

Data is still being analysed

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10
Q

Why did you pick yams?

A
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11
Q

What is a correlation?

A

any statistical relationship, whether causal or not, between two random variables or bivariate data

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12
Q

Why does this study matter?

A
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13
Q

What are the implications for yam conservation?

A
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14
Q

What’s next for the research?

A
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15
Q

If you had £1m what would you do to continue the research?

A

repeat in the field
consider tuber yield
growth habit
transplant experiment

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16
Q

Why did you sample like that?

A
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17
Q

How many known species?

A
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18
Q

Explain yam diversification onto Madagascar

A
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19
Q

Define climatic niche?

A
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20
Q

What factors did you not include in your niche?

A

Soil type
Humidity
Altitude - directly
Natural Disturbances: Consider other natural disturbances, such as floods or storms
Groundwater Level
pH

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21
Q

How does Stomatal size directly affects the rate of gas exchange in plants?

A

Larger stomata enhance the efficiency of gas diffusion, supporting processes like photosynthesis, but they also influence water loss through transpiration.

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22
Q

How does genome size determine stomatal size?

A

Sets the minimum nucleus - cell

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23
Q

Dioscorea is polyploid, how could that affect stomatal size?

A
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24
Q

What is a eudicot?

A

The eudicots, Eudicotidae, or eudicotyledons are a clade of flowering plants mainly characterized by having two seed leaves upon germination.

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25
Q

Explain: (Sandel et al., 2015) detected trait value biases in frequently measured against rarely measured species

A
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26
Q

Why is still some way to go before image processing technology can be used reliably, especially for less well studied crops (Li et al., 2023).

A
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27
Q

Heberling Protocols for SLA
“Specific Leaf Area data can also be gathered, provided that adjusted protocols are used (Heberling, 2022).”

A
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28
Q

What were your hypotheses?

A

“The first hypothesis questions whether a correlation exists between these traits and climatic conditions at the genus, bioregion and species level.

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29
Q

How did you supervisors make the SDMs?

The supervisors of the project provided Species Distribution Maps of EMYs and a dated Phylogenetic Tree of the Malagasy Clade

A
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30
Q

How did you supervisors make the dated Phylogenetic Tree of the Malagasy Clade

A

This tree was built using 260 targeted **low-copy nuclear genes **following Soto Gomez et al. (2019). HybPiper (Johnson et al. 2016)
was used to assemble and extract the targeted genes, alignments were built and edited with
MAFFT (Katoh & Standley 2013) and TRIMAL (Capella-Gutiérrez et al. 2009), respectively.
Phylogenetic trees were built using a supermatrix approach in RAxML-NG (Kozlov et al. 2019)
using the GTR+G molecular evolution model and 1000 bootstrap replicates. Divergence times
were estimated using treePL (Smith & O’Meara 2012) by calibrating the stem node of D.
sansibarensis at 22.5548 Ma (HPD: 15.5797-29.1444 Ma) following Viruel et al. (2016).
Overall, the phylogenetic relationships are very well supported by the bootstrap values. The earliest diverged species appears to be D. sansibarensis. Then the MEYs clade can be divided in four main subclades: the subclade I including 4 species, the subclade II including 9 species,
the subclade III including 16 species and the subclade IV including 4 species. D. arcuatinervis
occupies a very distinct position in the phylogeny and cannot be attributed to any of those
subclades. Finally, an accumulation curve of the number of lineages through time was produced

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31
Q

How did you clean? What did you remove?

The EMY-GBIF dataset was cleaned using CoordinateCleaner package (Zizka et al., 2019) to remove absent or inaccurate coordinates.

A
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32
Q

Why WorldClim, why 30 arc seconds?
Bioclimatic variables from the WorldClim 2.1 Dataset with a spatial resolution of 30 arc seconds.

A
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33
Q

What does a PCA do and why can we use it to identify climate niches?

A
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34
Q

What are the caveats of using a PCA to estimate species niche?

A
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35
Q

Why did you pick 0.8 to 0.8 collinearity?

A
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36
Q

Explain how you selected herbarium material?

A

K-means clustering algorithm was used in conjunction with a dendrogram

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37
Q

Leaf samples were mounted on Scanning Electron Microscope (SEM) stubs and sputter-coated with platinum (21.45g/m3) for 120s at 30mA using the Quorum Q150T ES.

Why Platinum? Why that time and speed?

A

A suitable platinum coating needs to be only half the thickness required for gold, resulting in less specimen distortion and giving superior peak-to-background ratio in X-ray analysis.

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38
Q

Why do you test for normal distribution using the Shapiro-Wilk test (shapiro.test() function? How does it work?

A

The Shapiro–Wilk test is essentially a goodness-of-fit test. That is, it examines how close the sample data fit to a normal distribution. It does this by ordering and standardizing the sample (standardizing refers to converting the data to a distribution with mean μ = 0 and standard deviation σ = 1 ).

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39
Q

Explain why you: Using the fitContinuous() function from the ‘geiger’ package (Pennell, 2014), five evolutionary models were applied to the trait data: Brownian Motion, Trend, Lambda, Ornstein-Uhlenbeck and Early Burst. Models were ranked according to minimum Akaike Information Criterion corrected (AICc) value and the maximum log-likelihood value (lnL) with the Lambda value reflecting the strength of the phylogenetic signal for each trait.

A
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40
Q

Explain PGLS

A

The trait-specific lambda values were then used in a Phylogenetic Generalised Least Squares (PGLS) analysis to examine the relationships between the traits and climate variables while controlling for phylogenetic signal. PGLS models were fitted using the ‘gls’ function from the ‘nlme’ package (Pinheiro, Bates and R Core Team, 2023), with correlation structure specified as Pagel’s lambda.

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41
Q

What are the pros and cons?

A
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42
Q

Why did you average the models?

The models were averaged using the model.avg() function from MuMIN

A
  • acknowledge that there might be multiple models that could be used to describe our data
  • by weighting the average we can communicate how confident we are in each individual model’s view of the world.
  • model averaging usually reducing prediction errors beyond even above even the best individual component model
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43
Q

Caveats of small sample size

A

The sample size for each species was too small to build complex predictive models at the species level.

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44
Q

Why did you use different packages in the second pca?
To conduct a PCA to identify patterns in the trait data and visualize the trait space, we used factoextra (Kassambara and Mundt, 2020) and factoMineR (Le, Josse and Husson, 2008). SLA, PL and SD were used in the analysis, and the resulting PCA plot displayed individuals as points in the trait space.

A
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45
Q

Why was the size of the trait space, representing the variation in trait values for a species, hypothesized to be associated with climatic resilience?

A
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46
Q

Explain your trait PCA

A
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47
Q

Why did you log10 some of the values sometimes?

A
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48
Q

Trait x Bioregion Table
Why were there no significant differences detected for SD and invPLSD in Bioregion

A
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49
Q

Phylogenetic Model Define:
log likelihood score

A
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50
Q

Phylogenetic Model Define:
AICc.

A
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51
Q

Phylogenetic Model Define:
Lambda

A

indicates the strength of the phylogenetic signal, with 0 signalling no phylogenetic signal and 1 indicating a Brownian Motion model.

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52
Q

What is Brownian Motion

A
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53
Q

In the dry season Why does pore length increase as precipitation in the driest month increase if there are no leaves?

A
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54
Q

Why was SA_02
collected from continental
Africa?

A
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55
Q

Why only 3 namorokensis?

A
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56
Q

What are the implications of different no. individuals on statistical tests?

A
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57
Q

Explain the following: Therefore, present-day geographic distribution of the clades is thought to reflect dispersal ability and the formation of key geographic regions, such as the Central Highlands, but this affected different clades homogenously. Thus, any similarities observed for traits and species occurring in the same bioregions of Madagascar would probably be explained by a similar response to climatic conditions achieved independently in different clades, rather than following a biogeographic pattern linked to the phylogenetic topology.

A
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58
Q

Tell me about D. pteropoda

A
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59
Q

Tell me about D. sansibarensis

A

Found across Africa and Madagascar
atypical. It is often associated with watercourses and is likely to have green shoots and leaves year round in riverine forest (Wilkin, 2023). This may explain why D. sansibarensis shows a different InvPLSD trend to the other species in this region. D. sansibarensis individuals in areas with higher precipitation were found to increase pore length and decrease stomatal density.

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60
Q

Tell me about D. proteiformis

A

Humid species, found exclusively in coastal regions in sub-clade I. Varies all 3 traits in response to climatic variables.
Precipitation: Increase SLA and SD, PL decreased.
Temp seasonality: increase PL, decrease SLA
Temperature: reduce PL, increase SLA

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61
Q

Tell me about D. bako

A

Dry and Subclade III.

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62
Q

Tell me about D. bemarivensis

A

Dry and Sub-arid

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63
Q

Tell me about D. seriflora

A

Humid Region
X Datapoints
Trends

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64
Q

Tell me about D. heteropoda

A
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65
Q

Which species were in the dry region?

A

D. bemarivensis
D. namorokensis
D. bako
D. maciba
D. pteropoda
D. sansibarensis

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66
Q

Which species were in the humid region?

A

Proteiformis
Madecassa
Seriflora

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67
Q

Which species were in the sub-humid region?

A

Heteropoda
Hexagona
Seriflora

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68
Q

Which species were in the sub-arid region?

A

D. bemarivensis
D. nako
D. hombuka
D. maciba
D. sansibarensis

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69
Q

Which species were found in multiple regions?

A

Seriflora - sub humid and humid
Maciba, bemarivensis and sansibarensis - dry and subarid

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70
Q

Tell me about D. madecassa

A
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71
Q

Tell me about D. hexagona

A
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72
Q

Tell me about D. namorokensis

A
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73
Q

Tell me about D. nako?

A
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74
Q

Tell me about D. namorokensis

A
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75
Q

Tell me about D. maciba

A
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76
Q

Are bioregions are good scale to investigate?

A

Yes and No.

Yes

No - E.g. dry region. Habitat variety and sub climates

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77
Q

Tell me about D. hombuka

A

Dioscorea hombuka is unique because it is found in both the Sub-Arid and Sub-Humid regions, suggesting D. hombuka may change its growth rate and habit in response to varying temperature seasonality in different local climatic contexts

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78
Q

Why would stomatal density only correlate in the sub-humid region?

A

The creation of the highlands established empty ecological niches in a milder and more stable climate compared to the other regions which may explain why stomatal density, a more complex developmental trait, is only significantly correlated with climate in this region.

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79
Q

How does dredge work?

A

Models are fitted through repeated evaluation of the modified call extracted from the global.model.

“Let the computer find out” is a poor strategy and usually reflects the fact that the researcher did not bother to think clearly about the problem of interest and its scientific setting (Burnham and Anderson,2002).

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80
Q

Can you explain what this line means?
Here, the model evaluation found that additive effects of annual precipitation + isothermality + precipitation in the driest month + annual temperature range best predicted stomatal density values.

A
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81
Q

Why is this important?
D. hexagona, the only selected species found exclusively in the sub-humid region only had one significant trait-climate relationship. It was found to increase pore length and decrease stomatal density in response to higher maximum temperatures. Unlike other EMYs, which are vines, D. hexagona is a unique in that it can show a strictly upright dwarf habit when growing in the savannahs of the Central highlands

A

Similar observations in dwarf barley and wheat. Brassinosteroids affecting physiological reactions to drought e.g. antioxidant enzymes ascorbate peroxidase (HvAPX) and superoxide dismutase (HvSOD) was BR-dependent.

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82
Q

Why is distance from centre of PCA used as a proxy for distinctiveness?

A
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83
Q

D. proteiformis found to vary all three traits in response to different climatic variables which may reflect its adaptation to its distinct coastal distribution. D. proteiformis was found to occupy one of most distinct climate niches (using distance from PCA centroid as a proxy).

A
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84
Q

Why? With a near constant abundance of water, D. proteiformis increases stomatal density and reduced pore length in areas with higher levels of precipitation, which may be an adaptation to prevent overhydration in the Humid region

A
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85
Q

What other adaptations exist to prevent overhydration?

A
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86
Q

How do you measure phenotypic plasticity?

A
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87
Q

What is phenotypic plasticity?

A

the ability of one genotype to adapt to different conditions

88
Q

Who else has calculated the size of the ellipse?

A
89
Q

Explain (Blonder, 2018)’s complex mathematical models to estimate the niche hypervolume space

A

We use a simple elipse but not points are distributed equally in the space. Blonder produces smooth boundaries that can fit data either more loosely (Gaussian kernel density estimation) or more tightly (one-classification via support vector machine). Further, the algorithms can accept abundance-weighted data, and the resulting hypervolumes can be given a probabilistic interpretation and projected into geographic space.

These new algorithms provide:
(i) a more robust approach for delineating the shape and density of n-dimensional hypervolumes;
(ii) more efficient performance on large and high-dimensional datasets;
(iii) improved measures of functional diversity and environmental niche breadth

90
Q

Explain 30 arc seconds

A

30 arc seconds (approximately 1 kilometer) ….

91
Q

What is the relevance of Satellite data, while comprehensive, may not capture fine-grained microclimatic variations, which could be significant for certain species

A

Microclimate - e.g. precipitation and temperature in that sq m in that year

92
Q

How will soil nutrient levels affect leaf traits?

A

Soil nutrient levels, particularly nitrogen and phosphorus, play a significant role in shaping these traits by enhancing photosynthetic capacity

93
Q

How will light availability affect leaf traits?

A

Light availability introduces complexities associated with trade-offs between light interception and water-use efficiency

94
Q

How will canopy position affect leaf traits?

A

impacts microclimates, affecting trait variation

95
Q

How will biotic interactions affect leaf traits?

A

herbivory and interspecies competition, may influence resource allocation and trait plasticity (Gorne and Diaz, 2022)

96
Q

How will altitudinal gradients affect leaf traits?

A

CO2 variations around year.

97
Q

What animals eat yams?

A
98
Q

How do plants adjust the thickness of their leaves?

A
99
Q

Is this true?
Root traits, such as depth and branching patterns, impact water and nutrient uptake, with drought-adapted species often having extensive root systems

A
100
Q

Summarise the importance of herbaria?

A
101
Q

We know the temperature will increase most in the southern parts of the island. How will this affect yams?

A
102
Q

Why do
(Schatz, Cameron and Raminosoa, 2008) estimated that climate change will affect endemic Malagasy species (not yams) in different bioregions differently?
Endemic species in the sub-arid region may expand their niche size, whereas species in the dry, the Sub-Humid and the humid regions are predicted range contractions.

A
103
Q

What is the most important finding of your research?

A

Proof of Concept, small sample size
- herbarium
- traits vary within species, between species and bioregion
- species level is most insightful
- environment impacts these traits more than phylogeny

104
Q

Define type 1 and type 2 for post-hoc error risk

A

Type 1 Risk = low risk
Type 2 Risk = high risk reasonability conservative

105
Q

Cohen’s D

A
106
Q

Stats: Anova

A

Independent Variable

107
Q

Fundamental niche is the entire set of conditions under which an animal (population, species) can survive and reproduce itself. Realized niche is the set of conditions actually used by given animal (pop, species), after interactions with other species (predation and especially competition) have been taken into account.

A

conditions, resources, and interactions it needs

108
Q

What is the definition of “climate-induced” famine? Why was Madagascar the first?

A
109
Q

What is the criterion to be classified as threatened? And Near threatened?

A
110
Q

Definition of climatic niche? Theoretical vs realised?

A
111
Q

Difference monocot and dicot? What is a monocot?

A
112
Q

Fig 2: why is water and O2 going out of closed stomata?

A
113
Q

What is the formula of photosynthesis?

A
114
Q

What is a “analogously oriented epidermal cell files of stomata”?

A
115
Q

In one sentence for each, what are the expectations for SD, SS, and SLA?

A
116
Q

Explain why the predictions change dep on bioregion, why is this not explained in the report?
(SD and prec, PL with prec)

A
117
Q

What bias may you expect when using herbarium samples to get leaf traits?

A

Physical Degradation:

Loss of Water Content:
- This dehydration may influence traits such as specific leaf area (SLA) leading to underestimations

Limited Ecological Information:
Herbarium specimens often lack detailed ecological information, such as precise location, soil type, and local environmental conditions. Without this information, it may be challenging to contextualize the observed leaf traits in relation to the ecological context.

Sampling Bias:
Herbarium collections may have been historically biased towards certain regions, habitats, or taxonomic groups. This bias can affect the representativeness of the sampled species and influence the generalizability of the findings.

Inconsistencies in Data Recording:
Variability in the methods used to collect and preserve herbarium specimens may introduce inconsistencies in the recorded data. These inconsistencies can impact the reliability and comparability of trait measurements across specimens.

118
Q

What is the difference between a hypothesis and a question?

A

Open-ended inquiry or interrogative statement prompting investigation. While not always directly testable, questions guide research and can lead to the formulation of hypotheses.

Hypotheses. Specific, testable statement predicting the outcome of an experiment Designed to be tested through experimentation or data collection.

119
Q

How is including trait data across the phylogeny opening the possibility to detect adaptive speciation?

A
120
Q

Why is this study important? What if drought resilience had not much to do with leaf traits? What other traits could be interesting?

A
121
Q

Why did you spend time on palms and you do not mention them in your report?

A
122
Q

What is a clade?

A

A clade (also known as a monophyletic group) is a group of organisms that includes a single ancestor and all of its descendents. Clades represent unbroken lines of evolutionary descent.

123
Q

Did you use human observations records from GBIF?

A

No

124
Q

Why do you remove correlated variables before doing a PCA?

A

Address issues associated with collinearity,
Dominance of a Few Variables:

In the presence of highly correlated variables, the PCA may give more weight to those variables and emphasize their contributions disproportionately. This can lead to a situation where a few variables dominate the principal components, potentially obscuring the overall structure of the data.
Unstable Component Loadings:

Multicollinearity can result in unstable estimates of the component loadings. The loadings indicate the contribution of each variable to each principal component. When variables are highly correlated, small changes in the data can lead to large changes in the loadings, making them less reliable for interpretation.
Reduced Interpretability:

High multicollinearity can make it challenging to interpret the principal components accurately. Instead of representing distinct patterns in the data, the components may reflect the redundancy of information shared among correlated variables.
Inflated Standard Errors:

Multicollinearity inflates the standard errors of the estimated coefficients, making it difficult to assess the statistical significance of individual variables or components. This can lead to unreliable hypothesis testing and confidence interval estimates.

125
Q

Is it ok to take only one leaf sample per individual?

A

Intraindividual in 12

126
Q

How does an SEM work?

A
127
Q

Why is normality important for your tests?

A
128
Q

You mention sometimes only 3 samples per species, in this case, is ANOVA really ok? What should be the minimum number of samples per category?

A
129
Q

Fig 3 (tree) what is the scale?

A

Scale bar refers to a phylogenetic distance of 0.05 nucleotide substitutions per site. Numbers on the branches indicate bootstrap percentage after 1000 replications in constructing the tree.

130
Q

How did you select the best model? What is Lambda? A model or a value in each model?

A
131
Q

DREDGE: was it always one trait ~ one predictor? If yes why? If not, where can we see the different models tested?

A
132
Q

What is delta AIC, and where does the “equivalently the best” quote come from?

A
133
Q

Species tests: why pearson and not for instance Spearman?

A
134
Q

ANOVA: significant or no significant difference between clades? (The text is contradictory)

A
135
Q

Trait and Clim PC correlation: how significant? Give coeff and p-value!! Is this accounting for phylogenetic signal? Would results change if it was? (no since no phylo signal)

A
136
Q

Do you understand why it is in fact useless to provide the lnL and AIC in Table 5?

A
137
Q

Why do you use pgls if the phylogenetic signal is so low?

A
138
Q

Fig 8: what is a “Sig correlation”?

A
139
Q

What is InvPLSD and why does it suddenly appear ? (Species correlations Table 6)

A

Variation in size and density of stomata may arise due to genetic factors and/or growth under different environmental conditions. A negative correlation has frequently been suggested between these two stomatal traits. This inverse relationship has been observed in plastic developmental responses to changes in environment and also during long-term evolutionary adaptation

140
Q

What is the use of Fig 9 vs Table 6? Where is its legend?

A
141
Q

Please explain the SLA results

A
142
Q

Niche section. It is good to lay out caveats but they were not sufficiently related to your study and your results.

A
143
Q

What is the difference between causation and correlation?

A
144
Q

With so many other traits and environmental variables that could be confounding factors, how can you trust your results, how can you go from description to interpretation? What can we say, what can we not say?

A
145
Q

What can help you compensate for lack of information about leaf microsite in herbarium specimens?

A
146
Q

You mention how D. proteiformis and madecassa may be affected by climate change - what about the other species?

A
147
Q

Is your original hypothesis/question answered?

A
148
Q

How do you conduct statistical power analysis?

A

Statistical Power (Power): The probability that a statistical test will correctly reject a false null hypothesis. It is the complement of Type II error (false negative rate) and is often denoted as 1−β, where β is the probability of a Type II error.

  • Type I Error (False Positive): Incorrectly rejecting a true null hypothesis. The probability of Type I error is denoted as α.
  • Type II Error (False Negative): Failing to reject a false null hypothesis
149
Q

What is a statistical power analysis?

A
150
Q

Did you find more variation within species or between species?

A
151
Q

How do you mitigate different herbarium conditions?

E.g. time taken to dry

A
152
Q

Why didn’t you do living specimens?

A

Long generation time.

1 year = 1 cotyledon
Years till full individual

153
Q

Different ways of estimating centre of climate niche

A

Lynn - mean estimate of values

154
Q

PCA

A
155
Q

How do you generate SDMs?

A
156
Q

Dredge

A

A high risk of Type I error means rejecting the null hypothesis when it’s actually true. It means concluding that results are statistically significant when, in reality, they came about purely by chance or because of unrelated factors. The risk of committing this error is the significance level (alpha or α) you choose.

157
Q

Type II Error

A

A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one fails to reject a null hypothesis that is actually false. A type II error produces a false negative, also known as an error of omission.

158
Q

What is a p-value?

A

probability value. strength of evidence against the null hypothesis, the smaller the p-value the stronger the evidence.

The percentage accounts for random noise

159
Q

What are the conditions to use an ANOVA?

A

Data is normally distributed
Distributions have the same variance.
Data are independent.

160
Q

What are the caveats of using a PCA?

A
161
Q

Leaf shrinkage and SLA

A
162
Q

Why would longer living leaves have a lower SLA

A

If strategy is to have longer living leaves, we want them to last as long as possible. We want thick tough leaves to protect from damage

163
Q

What is the most significant traits for a vine?

A

Stem traits e.g. Xylem conductance

164
Q

Explain the mechanisms for stomatal conductance?

A
165
Q

Difference between drought deciduous and evergreen?

A
166
Q

Why smaller pore length in humid areas?

A

Few hypothesis:
- increasing conductance = better temp regulation (cooling)
- reduce nutritional demand (N+P)
- reduced evaporative demand. Smaller stomata = more manipulative ability, ability to react to air points.

167
Q

What is the independent and dependent variables in your anova?

A

The independent variable is the cause. Its value is independent of other variables in your study.
The dependent variable is the effect. Its value depends on changes in the independent variable.

Independent - climate variable

Effect - traits

168
Q

Herbarium Traits

A

Leaf stomata are well preserved on herbarium specimens and are especially poised for measurement. Standard specimen preparation protocol includes mounting specimens to sheets such that the adaxial side of some leaves and the abaxial side of other leaves are exposed for measurement. Parkhurst (1978) used specimens to assess which species have stomata on one or both sides of leaves, exploring the adaptive significance of this trait across phylogenies and environmental contexts. Similarly, a classic study by Woodward (1987) measured the stomatal density of seven woody species in Europe, reporting strong declines in stomatal numbers as a result of atmospheric CO2 increases, a phenomenon that has since been repeated across many studies. Of special interest are the historical specimens measured for stomatal traits, with findings of inverse correlations with CO2 levels similar to those found by Woodward (1987). These include specimens collected by Lewis and Clark in North America in the early 1800s (described above with leaf chemistry traits; Teece et al. 2002) and by Joseph Banks and David Solander during James Cook’s voyage to the South Pacific in 1769 (Large et al. 2017). The trait has even been used to reconstruct several millennia of CO2 levels from ancient Egypt to today using preserved olive leaves dating to 1327 BCE from King Tutankhamun’s tomb alongside a chronosequence of traditional herbarium specimens (Beerling and Chaloner 1993). More commonly, paleobotanists now routinely rely on both fossils and herbarium specimens, alongside living material, to assess the functional significance of stomatal density and index in reconstructions of paleoenvironments (Hu et al. 2019).

169
Q

Collector Bias

A

Potential collection biases and inherent data use limitations have long been recognized (Merrill 1916; Anderson and Turrill 1935; Fogg 1940; Fernald 1950), and recent analyses with digitized collections have shown that herbaria are indeed not random samples (Daru et al. 2018). These “biases” are often intentional, with field botanists and ecologists often interested in different types of questions and using different methods of surveying or collecting (Alba et al. 2021). While it is not possible to definitively know the motivation or sampling strategy of each collector, there are well-established approaches to addressing these potential biases (table 2). Many uses of specimens that are now mainstream (e.g., phenology) were undoubtedly met with skepticism that hindered the progress of methodological development before they became an accepted specimen use. Newly realized herbarium specimen uses push these limits, enabling new insight into old specimens.

170
Q

Herbarium - microsite

A

Unknown microsite conditions of whole plant or plant parts (e.g., unclear whether sun or shade leaves collected):
  Ensure large sample size Woodson 1947
  Use habitat information on labels Hanan-A. et al. 2016
  Deliberate species selection (e.g., only open shaded habitat species; include species-level habitat affinities in analysis) Koski et al. 2020

171
Q

Trait shifts over time may arise from acclimation (phenotypic plasticity) and/or evolution (adaptation):

A

Compare focal species with null control congenerics or ecological analogues Case et al. 2007; Flores-Moreno et al. 2015
  Include ancillary data (e.g., historical climate records) in analysis Buswell et al. 2011
  Compare responses in home and away ranges (in case of invasion studies) Buswell et al. 2011
  Use specimens collected repeatedly from the same individual in botanical gardens Miller-Rushing et al. 2009
  Conduct common-garden work with known germplasm accessions to link genotype to phenotype Exposito-Alonso et al. 2018
  Extract genetic data from specimens to corroborate evolutionary shift Lamar and Partridge 2020

172
Q

Specimen drying, pressing, age, and storage conditions may affect trait measurement values:

A

Limit analysis to only herbarium-derived traits or compare herbarium-derived and non-herbarium-derived trait measurements on proportional change (i.e., measurement precision robust, accuracy may differ) Many studies
  Experimentally simulate storage or preparation conditions to quantify potential effects Miller et al. 2020
  Compare herbarium-derived traits with standard protocols and apply necessary correction factors Onstein et al. 2016; Perez et al. 2020

173
Q

Robustness of PCA

A

Robustness of the PC’s increases with increasing sample size but not with the number of traits. Borkland 2019

174
Q

Seasonal Variation Traits

A

C. Römermann et al. 2016 Looked at same leaf traits in summer green woody species.

The results showed that all traits varied significantly throughout the year in a species-specific manner.

We concluded that the seasonal timing of trait measurements is crucial.

Most notably SLA and stomatal size were the most robust traits in terms of small intraspecific and large interspecific variation and showed largely consistent species rankings across seasons.

175
Q

Why pore size not stomatal size?

A

Stomatal size (S), as defined by guard cell length (L) and width (W).

176
Q

SLA Evergreen v Deciduous

A

Evergreen species may show less variation in SLA throughout the seasons compared to deciduous species. Evergreens can maintain leaves year-round, whereas deciduous species undergo seasonal leaf shedding.

177
Q

Bako and Bemarivensis both have similar distributions. Why do we see correlations in some bako but not bemarivensis?

A
178
Q

Pearson Correlation Test

A

Measures strength and direction of a linear correlation
- R = 1: line passes through centre of all points perfectly
- R2 = amount of variance shared by two variables
- e.g. 93% variable of X is explained by Y, 7% explained by other factors
- 2-tailed hypothesis
Doesn’t matter which variable on which axis

179
Q

Pearson Assumptions

A

Random-samples
Variables are continuous
Data contains paired samples
Independence of observations
Variables are approximately normally distributed
A linear associations exists
Absence of outliers

180
Q

Spearman Correlation Test

A

Measures strength and direction of monotonic association between two ranked variables
- rank variables than perform pearson correlation test
- outputs coefficient value (Rs) and p value
- null hypothesis = no correlation
- p value smaller than 0.05 so reject null hypothesis

181
Q

Monotonic Relationship

A

1) As one variable increases, the other variable never decreases
2) As one variable increases, the other variable never increases
Can be linear

182
Q

Spearman Assumptions

A

1) Random sample
2) Monotonic Association
3) Both variables are at least ordinal (not nominal)
4) Data contains paired samples
5) Independence of observations

Don’t need normal data e.g non-parametric

183
Q

Were there specific aspects of the SEM images that provided insights not achievable through other methods?

A
184
Q

Can you describe the sample preparation process for SEM imaging?

A
185
Q

Did SEM allow for three-dimensional visualization of leaf structures, and if so, how did this enhance your analysis?

A
186
Q

Were there specific features or structures that were particularly well-characterized with SEM?

A
187
Q

How did the detailed surface information from SEM contribute to the interpretation of associations between leaf traits and climatic variables?

A
188
Q

How did you ensure the reproducibility and validation of your microscopy-based measurements?

A
189
Q

How did scanning electron microscopy contribute to the understanding of the surface morphology of yam leaves?

A
190
Q

Were there any steps taken to account for potential biases or variability in the microscopy data?

A
191
Q

Can you elaborate on the fixation techniques used for SEM sample preparation, and how did these techniques impact the preservation of fine structures on the leaf surface?

A
192
Q
A

It is a herbaceous, climbing, tropical monocot that looks rather like a dicot, and is part of a lineage that is relatively closely related to the phylogenetically derived group containing the grasses. Therefore, it represents an important biological link between the eudicots and grasses–groups that contain all the model flowering plant species–and has the potential to fill gaps in our knowledge of plant biology and evolution. Yams also offer us the possibility to gain new insights into processes such as tuberization and sex determination, which cannot be studied in current model organisms.

193
Q

What makes Dioscorea a monocot

A

Despite reticulate leaves
- embryonic structure
- vascular bundles
- leaf venation
- floral parts

Embryonic Structure:
- Yams, like other monocots, develop from seeds with a single cotyledon.

Vascular Bundle Arrangement:
- scattered vascular bundle pattern characteristic of monocots.

Floral Parts:
Yams conform to the monocot pattern with floral parts in threes.
Dicots usually have floral parts in multiples of four or five.

Root Development:
Yams typically have fibrous root systems, consistent with monocot characteristics. In contrast to dicots often develop a taproot

Secondary Growth:
Yams, being monocots, generally do not exhibit secondary growth.

194
Q

How did you validate your microscopic observations with other independent methods or datasets?

A
195
Q

Were there instances where beam damage influenced the interpretation of surface features?

A
196
Q

Were there alternative fixation methods considered, and what influenced your final choice?

A
197
Q

Were there specific regions of the leaf where stomatal density was notably higher or lower?

A
198
Q

Leaf selection

A

Large leaves from herbarium specimens
Mature where possible

199
Q

Can you discuss the distribution of stomata on the leaf surface and its relevance to water conservation or efficient gas exchange?

A
200
Q

Describe the types of trichomes present on the leaves of Malagasy yams and their potential functions.

A
201
Q

How might these patterns influence nutrient and water transport?

A
202
Q

Discuss the role of leaf anatomy, particularly vascular bundle characteristics, in facilitating or restricting water transport within the plant.

A
203
Q

How did the cuticle contribute to water retention and protection against desiccation?

A
204
Q

Describe the structure of a Dioscorea stomata

A

Dioscorea are randomly orientated and not distributed in cell lines

205
Q

How does intraspecific variability contribute to the ecological success of these plants in different environments?

A
206
Q

Can you describe the differentiation of tissues within the leaf, including the epidermis, mesophyll, and vascular bundles?

A
207
Q

How do leaf cells respond at the cellular level to changes in water availability?

A
208
Q

Were there observable changes in cell size, shape, or arrangement in response to varying water conditions?

A
209
Q

Cartereau 2019

A

The Dioscorea genus of yams is a diverse and important group of plants, with many species found in Madagascar. In this study, we investigate the rapid radiation of Malagasy endemic yams and the role of biogeographic history and dispersal evolution in this process. We collected and analyzed molecular data from multiple species of yams, and used phylogenetic and biogeographic methods to reconstruct their evolutionary history. Our results suggest that the rapid radiation of Malagasy yams was driven by a combination of factors, including geological events, climate change, and the evolution of dispersal mechanisms. We also discuss the implications of our findings for the conservation of biodiversity in Madagascar.

210
Q

What is a species distribution model?

A

identification of areas of likely species occurrence by learning a
relationship between knownoccurrences and environmental variables
(covariates). The ‘learning’ component of species-environmentrelationships can be accomplished by common machine learning algorithms,
such as random forest or MaximumEntropy (MaxEnt).

The output of our model is a % estimate of the likelihood of
each pixel in ourcalibration area being a presence. This percentage is
calculated by counting the number of different
decision trees
that decided a pixel was either a presence or an absence.

211
Q

Why coat

A

Non-conductive ones like biological samples, insulating polymers, or certain minerals, can build up a charge when exposed to the electron beam in the SEM. This charge can lead to distorted images and affect the quality of the data. Coating the specimen with a thin layer of conductive material, such as gold or platinum, helps dissipate the charge, enabling better imaging.

Conductive coatings provide a uniform surface that reflects electrons back into the detector, enhancing the signal and providing sharper images with better contrast.

We can achieve this by applying a thin metal coating, localizing the response from the sample, and improving the contrast and the yield of secondary electrons. Coating a sample will also reduce localized heating, reducing beam damage.

Charging-induced damage can occur in non-conductive samples due to the accumulation of electrons on the sample surface. Coating helps to prevent this damage by providing a conductive path for charge dissipation.

Coatings reduce the charge of the sample, making it easier to analyze. When the sample is coated, the electrons are removed from the top of the sample, and the sample is grounded, meaning that charge accumulates, forming a potential on top of the sample.

If the sample is not coated, that charge accumulates, potentially resulting in negative charging, positive charging or even traveling charge.

The accumulation of charge is related to microscope operating conditions, and charge effects may vary between microscope models, including edge charging, line by line charging, area charging or residual charging

212
Q

bootstrap percentage

A

Bootstrapping statistics is a form of hypothesis testing that involves resampling a single data set to create a multitude of simulated samples. Those samples are used to calculate standard errors, confidence intervals and for hypothesis testing. This approach allows you to generate a more accurate sample from a smaller data set than the traditional method.

the proportion of bootstrap replicates (resampled trees) in which a particular branch is supported.

213
Q

leaf economic spectrum (LES)

A

concept in plant ecology that describes a set of correlated leaf traits observed across various plant species. These traits are thought to be indicative of fundamental trade-offs in resource allocation and physiological strategies employed by plants to adapt to their environments. The leaf economic spectrum provides insights into the functional and ecological strategies that plants employ to optimize their growth and survival in different ecological niches. H

214
Q

LES - Components

A
  • Leaf Longevity: The duration a leaf remains attached to a plant.
  • Leaf Mass per Area (LMA): The mass of a leaf per unit leaf area, providing information about leaf thickness and structural density.
  • Photosynthetic Rate (Amax): The maximum rate of photosynthesis, indicating the leaf’s capacity for carbon assimilation.
  • Leaf Nitrogen Content (N): The concentration of nitrogen in the leaf, reflecting the plant’s nutrient status and photosynthetic capacity.
  • Leaf Phosphorus Content (P): The concentration of phosphorus in the leaf, influencing nutrient utilization and growth.
215
Q

Evolution Models

A

Brownian Motion:
- stochastic model where trait evolution is described as a random walk with no specific direction. It assumes that changes in traits accumulate over time due to random processes.
- implies continuous and random fluctuations in trait values across the branches of a phylogenetic tree. (null model against which other models are compared)

Trend:
- introduces a directional component to evolution. It suggests that there is a consistent trend in trait evolution, indicating a gradual shift in trait values over time.

Lambda (λ):
- Pagel’s λ model, is a scaling model that assumes a constant rate of trait evolution across the phylogeny. It introduces a scaling parameter (λ) that represents the degree of phylogenetic signal in the trait.
- λ value of 0 indicates no phylogenetic signal (similar to a Brownian Motion model), while a λ value of 1 indicates strong phylogenetic signal, with closely related species having more similar trait values.

Ornstein-Uhlenbeck (OU):
- stochastic and deterministic components. It assumes that trait evolution is influenced by both random processes and an attraction toward an optimal or equilibrium trait value.
- introduces an optimum trait value and a parameter (α) that measures the strength of attraction toward this optimum. Used to model traits that are subject to stabilising selection.

Early Burst:
- suggests the rate of trait evolution is not constant across the entire phylogeny. Instead, it proposes an early period of rapid evolution, followed by a slowdown in the rate of change.

Plant Science (5 decks)

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