2 Laboratory performance

2.1 Load data

The raw data are retrieved from the EHI database using personal access tokens only available for EHI managers. The relevant data are stored in the data/all_data.tsv file that is used for downstream analyses. This document is openly accessible, and can be loaded in the following way:

all_data <- read_tsv("data/all_data.tsv") %>% 
  filter(catalogue != "Species") %>%
  mutate(
    Taxon = factor(Taxon, levels = c(
      "Amphibian",
      "Reptile",
      "Mammal",
      "Bird",
      "Control"
    )),
    Extraction = factor(Extraction, levels = c(
      "REF",
      "DREX1",
      "DREX2"
    ))
  ) %>%
    mutate(Species=factor(Species,levels=c("Calotriton asper",
                                           "Lissotriton helveticus",
                                           "Salamandra atra",
                                           "Chalcides striatus",
                                           "Natrix astreptophora",
                                           "Podarcis muralis",
                                           "Plecotus auritus",
                                           "Sciurus carolinensis",
                                           "Trichosurus vulpecula",
                                           "Geospizopsis unicolor",
                                           "Perisoreus infaustus",
                                           "Zonotrichia capensis",
                                           "Extraction control",
                                           "Library control")))

2.2 DNA yield

Total amount of DNA extracted from the 150 ul subset of the bead-beaten sample.

all_data %>%
    select(Extraction,extract,Taxon) %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.0f±%.0f", mean(extract), sd(extract))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of total DNA nanograms")

Mean and standard deviation of total DNA nanograms

Taxon	REF	DREX1	DREX2
Amphibian	297±350	340±475	1138±1195
Reptile	102±71	162±134	250±179
Mammal	448±432	256±224	867±730
Bird	71±134	11±11	44±45
Control	0±0	2±3	1±0

all_data %>%
    select(Library,Species,Extraction,extract,Taxon) %>%
    unique() %>%
    ggplot(aes(x=Extraction, y=extract, color=Species, group=Extraction))+ 
        geom_boxplot(outlier.shape = NA, fill="#f4f4f4", color="#8c8c8c") + 
        geom_jitter() + 
        scale_color_manual(values=vertebrate_colors) +
        facet_grid(. ~ Taxon, scales = "free") +
        theme_minimal() +
        labs(y="DNA yield (ng)",x="Extraction method")

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    plot()

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    anova()

Type III Analysis of Variance Table with Satterthwaite's method
            Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
Taxon       9.7568  3.2523     3     8  3.9365 0.0537940 .  
Extraction 13.6941  6.8471     2    46  8.2877 0.0008436 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    broom.mixed::tidy() %>%
    tt()

effect	group	term	estimate	std.error	statistic	df	p.value
fixed	NA	(Intercept)	5.32423764	0.6624785	8.03684581	8.902457	2.275553e-05
fixed	NA	TaxonReptile	-0.86837312	0.9120619	-0.95209890	7.999997	3.689227e-01
fixed	NA	TaxonMammal	-0.06459683	0.9120619	-0.07082505	7.999997	9.452755e-01
fixed	NA	TaxonBird	-2.74514876	0.9120619	-3.00982728	7.999997	1.681798e-02
fixed	NA	ExtractionDREX1	-0.24661418	0.2623890	-0.93988004	46.000018	3.521886e-01
fixed	NA	ExtractionDREX2	0.77684194	0.2623890	2.96064983	46.000018	4.841350e-03
ran_pars	Sample:Species	sd__(Intercept)	0.58873659	NA	NA	NA	NA
ran_pars	Species	sd__(Intercept)	0.96787601	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	0.90894215	NA	NA	NA	NA

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    plot_model(.,type="pred",terms=c("Taxon","Extraction"),show.data = TRUE)

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    ggpredict(.,terms="Extraction")

# Predicted values of extract

Extraction | Predicted |          95% CI
----------------------------------------
REF        |    205.25 |  54.62,  771.31
DREX1      |    160.39 |  42.68,  602.74
DREX2      |    446.34 | 118.77, 1677.29

Adjusted for:
*   Taxon = Amphibian
*  Sample = 0 (population-level)
* Species = 0 (population-level)

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    ggpredict(.,terms="Taxon")

# Predicted values of extract

Taxon     | Predicted |        95% CI
-------------------------------------
Amphibian |    205.25 | 54.62, 771.31
Reptile   |     86.13 | 22.92, 323.67
Mammal    |    192.41 | 51.20, 723.06
Bird      |     13.19 |  3.51,  49.55

Adjusted for:
* Extraction = REF
*     Sample = 0 (population-level)
*    Species = 0 (population-level)

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    plot_model(.,type="pred",terms=c("Species"),pred.type = "re",ci.lvl = NA)+
        theme(axis.text.x = element_text(angle = 45, hjust = 1))

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(log(extract) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    r.squaredGLMM()

           R2m       R2c
[1,] 0.4053742 0.7671254

2.3 Sample purity

In common laboratory practice, DNA and RNA samples with A260/A280 and A260/A230 > 1.8 are considered to be “clean”, and suitable for use in most downstream applications

2.4 Sample purity A260/A280 Ratios

260 nm and 280 nm are the absorbance wavelengths used to assess the type of nucleic acid present (DNA or RNA) and they provide a rough indication of purity. A reduction of this ratio typically indicates protein contamination while RNA contamination can be detected by an increase of this ratio. The generally accepted 260/280 values are ~1.8 for pure DNA and ~2.0 for RNA. Lower values may indicate the presence of protein, phenol or other contaminants that have an absorbance close to 280 nm.

all_data %>%
  filter(!is.na(x260_280)) %>% 
  select(Extraction, x260_280, Taxon) %>%
  group_by(Extraction) %>%
  #group_by(Taxon, Extraction) %>%
  summarise(value = sprintf("%.2f±%.2f", mean(x260_280), sd(x260_280))) %>%
  pivot_wider(names_from = Extraction, values_from = value) %>%
  tt(caption = "Mean and standard deviation of 260/280 values")

Mean and standard deviation of 260/280 values

REF	DREX1	DREX2
1.35±0.18	1.63±0.35	1.44±0.31

all_data %>%
  filter(!is.na(x260_280)) %>% 
  mutate(d260_280=abs(1.8-x260_280)) %>% 
  select(Extraction, d260_280, Taxon) %>%
  group_by(Taxon, Extraction) %>%
  summarise(value = sprintf("%.2f±%.2f", mean(d260_280), sd(d260_280))) %>%
  pivot_wider(names_from = Extraction, values_from = value) %>%
  tt(caption = "Mean and standard deviation of the deviation of 260/280 values from 1.8")

Mean and standard deviation of the deviation of 260/280 values from 1.8

Taxon	REF	DREX1	DREX2
Amphibian	0.57±0.15	0.33±0.13	0.24±0.14
Reptile	0.40±0.22	0.25±0.16	0.25±0.11
Mammal	0.53±0.12	0.23±0.21	0.43±0.22
Bird	0.35±0.12	0.46±0.24	0.68±0.33
Control	0.33±0.28	0.52±0.16	0.15±0.12

Higher deviation from 1.8 indicates lower purity of the DNA extracts

all_data %>%
  select(Extraction, x260_280, Taxon, Species) %>%
    ggplot(aes(x=Extraction, y=x260_280, color=Species, group=Extraction))+ 
        geom_boxplot(outlier.shape = NA, fill="#f4f4f4", color="#8c8c8c") + 
        geom_jitter() + 
        scale_color_manual(values=vertebrate_colors) +
        facet_grid(. ~ Taxon, scales = "free") +
        theme_minimal() +
        labs(y="260/280 values",x="Extraction method")

all_data  %>%
  filter(!is.na(x260_280),Taxon != "Control") %>% 
  #filter(!is.na(x260_280),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_280=abs(1.8-x260_280)) %>% 
  lmerTest::lmer(rank(d260_280) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
  plot()

all_data  %>%
  filter(!is.na(x260_280),Taxon != "Control") %>% 
  #filter(!is.na(x260_280),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_280=abs(1.8-x260_280)) %>% 
  lmerTest::lmer(rank(d260_280) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    anova()

Type III Analysis of Variance Table with Satterthwaite's method
           Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
Taxon      2420.2  806.74     3    58  2.7091 0.05334 .
Extraction 2166.8 1083.38     2    58  3.6381 0.03247 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

all_data  %>%
  filter(!is.na(x260_280),Taxon != "Control") %>% 
  #filter(!is.na(x260_280),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_280=abs(1.8-x260_280)) %>% 
  lmerTest::lmer(rank(d260_280) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    broom.mixed::tidy() %>%
    tt()

effect	group	term	estimate	std.error	statistic	df	p.value
fixed	NA	(Intercept)	40.307144	5.621064	7.1707318	58	1.514226e-09
fixed	NA	TaxonReptile	-7.547479	6.014439	-1.2548933	58	2.145505e-01
fixed	NA	TaxonMammal	1.592086	6.110570	0.2605462	58	7.953653e-01
fixed	NA	TaxonBird	9.812092	6.204784	1.5813754	58	1.192307e-01
fixed	NA	ExtractionDREX1	-14.625757	5.434118	-2.6914685	58	9.280304e-03
fixed	NA	ExtractionDREX2	-9.026627	5.442600	-1.6585137	58	1.026129e-01
ran_pars	Sample:Species	sd__(Intercept)	0.000000	NA	NA	NA	NA
ran_pars	Species	sd__(Intercept)	0.000000	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	17.256500	NA	NA	NA	NA

all_data  %>%
  filter(!is.na(x260_280),Taxon != "Control") %>% 
  #filter(!is.na(x260_280),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_280=abs(1.8-x260_280)) %>% 
  lmerTest::lmer(rank(d260_280) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    plot_model(.,type="pred",terms=c("Taxon","Extraction"))

all_data  %>%
filter(!is.na(x260_280),Taxon != "Control") %>% 
  #filter(!is.na(x260_280),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
    mutate(d260_280=abs(1.8-x260_280)) %>% 
  lmerTest::lmer(rank(d260_280) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    ggpredict(.,terms="Extraction")

# Predicted values of d260_280

Extraction | Predicted |       95% CI
-------------------------------------
REF        |     40.31 | 29.04, 51.57
DREX1      |     25.68 | 15.36, 36.00
DREX2      |     31.28 | 20.95, 41.61

Adjusted for:
*   Taxon = Amphibian
*  Sample = 0 (population-level)
* Species = 0 (population-level)

all_data  %>%
  filter(!is.na(x260_280),Taxon != "Control") %>% 
  mutate(d260_280=abs(1.8-x260_280)) %>% 
  lmerTest::lmer(rank(d260_280) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
    r.squaredGLMM()

           R2m       R2c
[1,] 0.1957896 0.1957896

2.5 Sample purity A260/A230 Ratios

The A260/A230 is a sensitive indicator of contaminants that absorb at 230 nm. These contaminants are significantly more numerous than those absorbing at 280 nm, and include chaotropic salts such as guanidine thiocyanate (GTC) and guanidine hydrochloride (GuHCl), EDTA, non-ionic detergents like Triton™ X-100 and Tween® 20, proteins, and phenol. Substances like polysaccharides or free floating solid particles like silica fibers absorb at this wavelength, but will have a weaker effect.

Low 260/230 values indicates the presence of residual extraction reagent (ex. Carbohydrates, Chaotropic salts, phenol).

all_data %>%
  filter(!is.na(x260_230)) %>% 
  select(Extraction, x260_230, Taxon) %>%
  group_by(Extraction) %>%
  #group_by(Taxon, Extraction) %>%
  summarise(value = sprintf("%.2f±%.2f", mean(x260_230), sd(x260_230))) %>%
  pivot_wider(names_from = Extraction, values_from = value) %>%
  tt(caption = "Mean and standard deviation of 260/230 values")

Mean and standard deviation of 260/230 values

REF	DREX1	DREX2
0.09±0.11	0.17±0.13	0.20±0.27

all_data %>%
  filter(!is.na(x260_230)) %>% 
  mutate(d260_230=abs(1.8-x260_230)) %>% 
  select(Extraction, d260_230, Taxon) %>%
  group_by(Taxon, Extraction) %>%
  summarise(value = sprintf("%.2f±%.2f", mean(d260_230), sd(d260_230))) %>%
  pivot_wider(names_from = Extraction, values_from = value) %>%
  tt(caption = "Mean and standard deviation of the deviation of 260/230 values from 1.8")

Mean and standard deviation of the deviation of 260/230 values from 1.8

Taxon	REF	DREX1	DREX2
Amphibian	1.72±0.09	1.53±0.15	1.61±0.17
Reptile	1.76±0.04	1.66±0.13	1.63±0.17
Mammal	1.62±0.17	1.63±0.12	1.38±0.52
Bird	1.72±0.09	1.69±0.10	1.67±0.10
Control	1.79±0.00	1.72±0.09	1.79±0.00

Higher deviation from 1.8 indicates lower purity of the DNA extracts

all_data %>%
  filter(!is.na(x260_230)) %>% 
  select(Extraction, x260_230, Taxon, Species) %>%
    ggplot(aes(x=Extraction, y=x260_230, color=Species, group=Extraction))+ 
        geom_boxplot(outlier.shape = NA, fill="#f4f4f4", color="#8c8c8c") + 
        geom_jitter() + 
        scale_color_manual(values=vertebrate_colors) +
        facet_grid(. ~ Taxon, scales = "free") +
        theme_minimal() +
        labs(y="Deviation of 260/230 values from 1.8",x="Extraction method")

all_data  %>%
  #  filter(!is.na(x260_230),Taxon != "Control") %>% 
  filter(!is.na(x260_230),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_230=abs(1.8-x260_230)) %>% 
  lmerTest::lmer(rank(d260_230) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
  plot()

all_data  %>%
  #  filter(!is.na(x260_230),Taxon != "Control") %>% 
  filter(!is.na(x260_230),Taxon != "Control",!dna_extract %in% c("AGI41E1")) %>% 
  #filter(!is.na(x260_230),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_230=abs(1.8-x260_230)) %>% 
  lmerTest::lmer(rank(d260_230) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
  anova()

Type III Analysis of Variance Table with Satterthwaite's method
            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
Taxon       633.23  211.08     3  7.947  0.9068 0.479655   
Extraction 2535.12 1267.56     2 40.454  5.4455 0.008049 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

all_data  %>%
  #  filter(!is.na(x260_230),Taxon != "Control") %>% 
  filter(!is.na(x260_230),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_230=abs(1.8-x260_230)) %>% 
  lmerTest::lmer(rank(d260_230) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
  broom.mixed::tidy() %>% 
  tt()

effect	group	term	estimate	std.error	statistic	df	p.value
fixed	NA	(Intercept)	32.4284615	6.339797	5.1150630	12.163079	0.0002446977
fixed	NA	TaxonReptile	6.8963418	8.091541	0.8522903	8.223421	0.4181819448
fixed	NA	TaxonMammal	-5.7184280	8.091541	-0.7067168	8.223421	0.4992748629
fixed	NA	TaxonBird	8.7727143	8.273361	1.0603567	8.888935	0.3169426589
fixed	NA	ExtractionDREX1	-9.6666667	4.231586	-2.2844072	33.999693	0.0287121253
fixed	NA	ExtractionDREX2	-13.0000000	4.231586	-3.0721338	33.999693	0.0041659257
ran_pars	Sample:Species	sd__(Intercept)	0.8608917	NA	NA	NA	NA
ran_pars	Species	sd__(Intercept)	7.6823091	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	12.6947594	NA	NA	NA	NA

all_data  %>%
  #  filter(!is.na(x260_230),Taxon != "Control") %>% 
  filter(!is.na(x260_230),Taxon != "Control", !Sample %in% c("ABD27", "AJP51", "AFO83","AAZ65","AGI41", "AIV55")) %>% 
  mutate(d260_230=abs(1.8-x260_230)) %>% 
  lmerTest::lmer(rank(d260_230) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
  plot_model(.,type="pred",terms=c("Taxon","Extraction"),show.data = TRUE)

all_data  %>%
  filter(!is.na(x260_230),Taxon != "Control") %>% 
  mutate(d260_230=abs(1.8-x260_230)) %>% 
  lmerTest::lmer(rank(d260_230) ~ Taxon + Extraction + (1 | Species/Sample), data = ., REML = TRUE) %>%
  r.squaredGLMM()

          R2m       R2c
[1,] 0.178054 0.4098904

2.6 Amplification performance

all_data %>%
  filter(!is.na(ct_d_rn)) %>% 
  select(Extraction, ct_d_rn, Taxon) %>%
  group_by(Taxon, Extraction) %>%
  summarise(value = sprintf("%.2f±%.2f", mean(ct_d_rn), sd(ct_d_rn))) %>%
  pivot_wider(names_from = Extraction, values_from = value) %>%
  tt(caption = "Mean and standard deviation of Cts (cycle threshold) values")

Mean and standard deviation of Cts (cycle threshold) values

Taxon	REF	DREX1	DREX2
Amphibian	18.19±2.11	15.57±3.29	14.42±2.53
Reptile	16.16±3.91	14.56±3.36	16.33±3.37
Mammal	15.21±4.13	14.75±1.84	14.45±2.06
Bird	20.36±3.58	20.83±2.31	18.91±2.51
Control	23.86±2.69	23.39±0.76	26.23±4.24

all_data %>%
  #filter(!Taxon=="Control") %>% 
    select(Library,Species,Extraction, ct_d_rn,Taxon) %>%
    unique() %>%
    ggplot(aes(x=Extraction, y=ct_d_rn, color=Species, group=Extraction))+ 
        geom_boxplot(outlier.shape = NA, fill="#f4f4f4", color="#8c8c8c") + 
        geom_jitter() + 
        scale_color_manual(values=vertebrate_colors) +
        facet_grid(. ~ Taxon, scales = "free") +
        theme_minimal() +
        labs(y="Cts (cycle threshold) values",x="Extraction method")

all_data %>%
    select(dna_extract, ct_d_rn, ng_ul_lib, Species, Extraction) %>% 
    ggplot(aes(x=log10(ng_ul_lib), y=ct_d_rn)) + 
      geom_point(aes(color=Extraction)) + geom_smooth(method = "lm", se=FALSE) 

all_data  %>%
  filter(Taxon != "Control") %>%
  lmerTest::lmer(ct_d_rn ~ Taxon + Extraction + log10(ng_ul_lib) + (1 | Species/Sample), data = ., REML = TRUE) %>%
  plot()

all_data  %>%
  filter(Taxon != "Control") %>%
  lmerTest::lmer(ct_d_rn ~ Taxon + Extraction + log10(ng_ul_lib) + (1 | Species/Sample), data = ., REML = TRUE) %>%
  anova()

Type III Analysis of Variance Table with Satterthwaite's method
                  Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Taxon             22.392   7.464     3  8.709  1.2818    0.3402    
Extraction        26.585  13.292     2 45.423  2.2826    0.1136    
log10(ng_ul_lib) 146.211 146.211     1 39.839 25.1082 1.154e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

all_data  %>%
  filter(Taxon != "Control") %>%
  lmerTest::lmer(ct_d_rn ~ Taxon + Extraction + log10(ng_ul_lib) + (1 | Species/Sample), data = ., REML = TRUE) %>%
  broom.mixed::tidy() %>% 
  tt()

effect	group	term	estimate	std.error	statistic	df	p.value
fixed	NA	(Intercept)	17.5438706	0.8220013	21.3428733	13.276020	1.154989e-11
fixed	NA	TaxonReptile	-1.0851748	1.0070686	-1.0775579	7.629471	3.141183e-01
fixed	NA	TaxonMammal	-1.2919091	0.9970232	-1.2957662	7.438002	2.338163e-01
fixed	NA	TaxonBird	0.5700030	1.2068017	0.4723253	11.583255	6.454686e-01
fixed	NA	ExtractionDREX1	-1.4247190	0.7005353	-2.0337576	44.938946	4.790546e-02
fixed	NA	ExtractionDREX2	-1.0904678	0.7003063	-1.5571298	44.905296	1.264602e-01
fixed	NA	log10(ng_ul_lib)	-3.2269503	0.6439979	-5.0108085	39.838561	1.154229e-05
ran_pars	Sample:Species	sd__(Intercept)	0.7960002	NA	NA	NA	NA
ran_pars	Species	sd__(Intercept)	0.4512907	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	2.4131358	NA	NA	NA	NA

all_data  %>%
  filter(Taxon != "Control") %>%
  lmerTest::lmer(ct_d_rn ~ Taxon + Extraction + log10(ng_ul_lib) + (1 | Species/Sample), data = ., REML = TRUE) %>%
  plot_model(.,type="pred",terms=c("Taxon","Extraction"),show.data = TRUE)

all_data  %>%
    filter(Taxon != "Control") %>%
    mutate("log_extract"=log(extract))%>%
    lmerTest::lmer(ct_d_rn ~ Taxon + Extraction + log10(ng_ul_lib) + (1 | Species/Sample), data = ., REML = TRUE) %>%
    ggeffects::ggpredict(.,terms="Extraction")

# Predicted values of ct_d_rn

Extraction | Predicted |       95% CI
-------------------------------------
REF        |     16.75 | 15.13, 18.37
DREX1      |     15.32 | 13.69, 16.96
DREX2      |     15.66 | 14.03, 17.28

Adjusted for:
*     Taxon = Amphibian
* ng_ul_lib =      1.76
*    Sample = 0 (population-level)
*   Species = 0 (population-level)

all_data  %>%
  filter(Taxon != "Control") %>%
  lmerTest::lmer(ct_d_rn ~ Taxon + Extraction + log10(ng_ul_lib) + (1 | Species/Sample), data = ., REML = TRUE) %>%
  r.squaredGLMM()

           R2m       R2c
[1,] 0.5145871 0.5756074