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Assessing patterns of extinction risk amongst mammal species in Nigeria: A comparative analysis of human impact.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Material and methods ↔ NIGMAL Scripts/Data_Cleaning.Rmd, lines 68–122 · score 0.91 · Extant Reintroduced, Possibly Extant, extant polygons, geographic area, extinct area, Geographic range
  2. [2] § Results ↔ NIGMAL Scripts/visualization.Rmd, lines 7–96 · score 0.78 · Residential Commercial Development, Human Intrusions, Agriculture Aquaculture, Biological Resource, Climate Change Severe, Brain Mass
  3. [3] § Results ↔ NIGMAL Scripts/visualization.Rmd, lines 7–96 · score 0.76 · Energy Production Mining, Transportation Service Corridors, Agriculture Aquaculture, Climate Change Severe, Weather, human
  4. [4] § Material and methods ↔ NIGMAL Scripts/model_outputs.Rmd, lines 7–120 · score 0.75 · logistic_MPLE, phylogenetic logistic regression, IUCN Red, pruned, Threats Model, phyloglm
  5. [5] § Results ↔ NIGMAL Scripts/model_fitting.Rmd, lines 41–80 · score 0.74 · Human Intrusions, Commercial Development, Agriculture Aquaculture, Severe Weather, Biological Resource, Climate Change
  6. [6] § Results ↔ NIGMAL Scripts/model_fitting.Rmd, lines 41–80 · score 0.72 · Service Corridors, Energy Production, Agriculture Aquaculture, Severe Weather, Climate Change, Mining
  7. [7] § Material and methods ↔ NIGMAL Scripts/model_outputs.Rmd, lines 7–120 · score 0.58 · phylogenetic logistic regression, IUCN threat categories, models

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R Markdown · 238 lines · 6.9 KB · MIT · 2 matches

  1. ---
  2. title: "visualization"
  3. author: "Bernard Asanbe"
  4. date: "2025"
  5. ---
  7. ```{r}
  9. # Load required libraries
  10. library(ggplot2)
  11. library(dplyr)
  12. library(readr)
  13. library(scales)
  15. # Read the CSV file
  16. data <- read_csv("insert_file_path_here/Phyloglm_modelling_results.csv")
  18. # Filter out intercept rows
  19. data <- data %>% filter(Predictor != "(Intercept)")
  21. # Add significance labels: multiple asterisks based on p-values
  22. data <- data %>%
  23. mutate(Significance = case_when(
  24. Sig < 0.001 ~ "***",
  25. Sig < 0.01 ~ "**",
  26. Sig < 0.05 ~ "*",
  27. TRUE ~ ""
  28. ))
  30. # Order models, keeping "Total_Threat_Model" first
  31. models <- unique(data$Model)
  32. models <- c("Total_Threats_Model", setdiff(models, "Total_Threats_Model"))
  34. # Create a high-contrast color palette using varying luminance
  35. # Mix of dark and light tones for better contrast
  36. n_other <- length(models) - 1
  37. hues <- seq(15, 375, length.out = n_other + 1)[-1]
  38. luminances <- rep(c(40, 70), length.out = n_other) # alternate luminance for contrast
  39. colorful_palette <- hcl(h = hues, c = 100, l = luminances)
  41. # Assign black to Total Threat Model, add colorful palette to others
  42. colors <- c("Total_Threats_Model" = "black")
  43. colors <- c(colors, setNames(colorful_palette, models[-1]))
  45. # Set factor levels to control order
  46. data$Model <- factor(data$Model, levels = models)
  48. # Clean up Predictor labels (x-axis)
  49. data$Predictor <- recode(data$Predictor,
  50. "Scaled_CurrentByTotalArea_km2" = "Historical Range Contraction",
  51. "Scaled_Log_Brain_Mass_g" = "Brain Mass",
  52. "Scaled_Log_CurrentArea_km2" = "Current Area (km²)",
  53. "Scaled_Log_GenerationTime_d" = "Generation Time",
  54. "Scaled_Log_Mass_g" = "Body Mass",
  56. )
  58. # Clean up Model labels (legend)
  59. data$Model <- recode(as.character(data$Model),
  60. "Total_Threats_Model" = "Total Threats Model",
  61. "Agriculture_&_aquaculture" = "Agriculture & Aquaculture",
  62. "Biological_resource_use" = "Biological Resource Use",
  63. "Residential_&_commercial_development" = "Residential & Commercial Development",
  64. "Energy_production_&_mining" = "Energy Production & Mining",
  65. "Transportation_&_service_corridors" = "Transportation & Service Corridors",
  66. "Human_intrusions_&_disturbance" = "Human Intrusions & Disturbance",
  67. "Climate_change_&_severe_weather" = "Climate Change & Severe Weather",
  68. "Pollution" = "Pollution"
  69. )
  71. # Re-apply factor order after recoding (Total Threats must stay first)
  72. data$Model <- factor(data$Model, levels = c(
  73. "Total Threats Model",
  74. "Agriculture & Aquaculture",
  75. "Biological Resource Use",
  76. "Residential & Commercial Development",
  77. "Energy Production & Mining",
  78. "Transportation & Service Corridors",
  79. "Human Intrusions & Disturbance",
  80. "Climate Change & Severe Weather",
  81. "Pollution"
  82. ))
  84. # Update colors to match new model names
  85. colors <- c("Total Threats Model" = "black")
  86. colors <- c(colors, setNames(colorful_palette,
  87. c("Agriculture & Aquaculture",
  88. "Biological Resource Use",
  89. "Residential & Commercial Development",
  90. "Energy Production & Mining",
  91. "Transportation & Service Corridors",
  92. "Human Intrusions & Disturbance",
  93. "Climate Change & Severe Weather",
  94. "Pollution")))
  96. ```
  98. Plotting of Raw graph
  99. ```{r}
  101. # Compute vertical position for asterisks
  102. data <- data %>%
  103. mutate(asterisk_y = ifelse(Effect_size >= 0,
  104. Effect_size + SE + 0.05,
  105. Effect_size - SE - 0.1))
  107. # Build the final plot
  108. p1 <- ggplot(data, aes(x = Predictor, y = Effect_size, fill = Model)) +
  109. geom_bar(stat = "identity",
  110. position = position_dodge(width = 0.8),
  111. width = 0.7) +
  112. geom_errorbar(aes(ymin = Effect_size - SE,
  113. ymax = Effect_size + SE),
  114. position = position_dodge(width = 0.8),
  115. width = 0.25) +
  116. geom_text(aes(y = asterisk_y, label = Significance),
  117. position = position_dodge(width = 0.8),
  118. angle = 90,
  119. size = 6,
  120. fontface = "bold",
  121. color = "red3",
  122. hjust = -0.05) +
  123. labs(
  124. y = "Effect Size",
  125. x = "Predictor",
  126. fill = "Threat Models"
  127. ) +
  128. scale_fill_manual(values = colors) +
  129. theme_minimal(base_size = 18) +
  130. theme(
  131. axis.text.x = element_text(
  132. angle = 52,
  133. size = 14,
  134. hjust = 1,
  135. face = "plain"
  136. ),
  137. axis.line = element_line(color = "black"),
  138. axis.ticks = element_line(color = "black"),
  139. panel.grid.major.x = element_blank(),
  140. panel.grid.minor.x = element_blank(),
  141. legend.position = "right"
  142. )
  144. p1
  146. ggsave(
  147. "Fig1_raw_effects.jpeg",
  148. plot = p1,
  149. width = 14,
  150. height = 8,
  151. dpi = 300,
  153. )
  155. ```
  157. Plotting of Standardized (scaled) graph
  158. ```{r}
  160. # Compute vertical position for asterisks
  161. data <- data %>%
  162. mutate(asterisk_y = ifelse(Std_Effect_size >= 0,
  163. Std_Effect_size + SE + 0.05,
  164. Std_Effect_size - SE - 0.1))
  166. # Build the final plot
  167. p2 <- ggplot(
  168. data_std,
  169. aes(x = Predictor,
  170. y = Std_Effect_size,
  171. fill = Model)
  172. ) +
  173. geom_bar(
  174. stat = "identity",
  175. position = position_dodge(width = 0.8),
  176. width = 0.7
  177. ) +
  178. geom_errorbar(
  179. aes(
  180. ymin = Std_Effect_size - Std_SE,
  181. ymax = Std_Effect_size + Std_SE
  182. ),
  183. position = position_dodge(width = 0.8),
  184. width = 0.25
  185. ) +
  186. geom_text(
  187. aes(
  188. y = asterisk_y,
  189. label = Significance
  190. ),
  191. position = position_dodge(width = 0.8),
  192. angle = 90,
  193. size = 6,
  194. fontface = "bold",
  195. color = "red3",
  196. hjust = -0.05
  197. ) +
  198. labs(
  199. y = "Standardized Effect Size",
  200. x = "Predictor",
  201. fill = "Threat Models"
  202. ) +
  203. scale_fill_manual(values = colors) +
  204. theme_minimal(base_size = 18) +
  205. theme(
  206. axis.text.x = element_text(
  207. angle = 52,
  208. size = 14,
  209. hjust = 1,
  210. face = "plain"
  211. ),
  212. axis.line = element_line(color = "black"),
  213. axis.ticks = element_line(color = "black"),
  214. panel.grid.major.x = element_blank(),
  215. panel.grid.minor.x = element_blank(),
  216. legend.position = "right"
  217. )
  219. # Display plot
  220. p2
  222. # Save Figure 2
  223. ggsave(
  224. filename = "Fig2_std_effects.jpeg",
  225. plot = p2,
  226. width = 14,
  227. height = 8,
  228. dpi = 300,
  229. )
  232. ```
  234. ```{r}
  238. ```

visualization.Rmd, under MIT · at the source

Overview

Authors: Bernard Asanbe1
  1. University of Gothenburg, Göteborg, Sweden, University of Gothenburg, Göteborg, Sweden, https://ror.org/01tm6cn81
Institutions: University of Gothenburg (Sweden)
Journal: Biodiversity data journal, volume 14, article e191439
Dates: received 13 March 2026; accepted 15 June 2026; published online 21 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3897/bdj.14.e191439 · PMID 42668897 · PMCID PMC13525407 · OpenAlex W7203953010
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Machine learning, Preprocessing, Statistics, Physiology & signal measures
Keywords: brain mass, extinction risk, human impact, Nigerian mammals
Topic: Primate Behavior and Ecology (Social Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

This study aimed to evaluate how biological traits influence extinction risk amongst mammal species in Nigeria and how these traits interact with specific anthropogenic threats, such as agriculture, urbanisation and climate change. Focusing on mammal species in Nigeria, we used phylogenetic logistic regression to test the influence of five biological traits: body mass, brain mass, generation time, current geographic range and historical range contraction, on extinction risk across nine IUCN threat categories. Standardised models were used to compare trait sensitivity across threats. Brain mass emerged as the most consistent and influential predictor of extinction risk across threat categories, including agriculture, biological resource use, urban development and, notably, climate change, where it was the strongest predictor of all models. Species with larger brains, often primates and carnivores, were highly vulnerable. Geographic range size was a strong negative predictor of risk across most models, with range-restricted species more susceptible to habitat loss and fragmentation. Generation time was positively associated with risk under direct human pressures, but inversely linked under climate threats. Body mass showed weak and inconsistent effects overall, though it was significantly and negatively associated with extinction risk under agriculture and aquaculture. The number of species affected was highest under direct human pressures, compared to indirect anthropogenic threats, such as climate change and pollution. Extinction risk in Nigerian mammals is shaped by intrinsic traits that interact predictably with human pressures. Species with large brains, small ranges and slow reproduction are at greatest risk. Trait-based models can improve conservation planning by identifying vulnerable species before population declines become critical, especially in regions facing intensive land-use change.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Zenodo 20646160

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Material and methods”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), car (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.20646160

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 1 author, 4 keywords, 38 references.

Cite

This paper

Asanbe, B. (2026). Assessing patterns of extinction risk amongst mammal species in Nigeria: A comparative analysis of human impact. Biodiversity data journal, 14, e191439. https://doi.org/10.3897/bdj.14.e191439

BibTeX

@article{asanbe2026assessing,
author = {Asanbe, Bernard},
title = {{Assessing patterns of extinction risk amongst mammal species in Nigeria: A comparative analysis of human impact}},
journal = {Biodiversity data journal},
year = {2026},
month = aug,
volume = {14},
pages = {e191439},
publisher = {Pensoft Publishers},
issn = {1314-2828},
doi = {10.3897/bdj.14.e191439},
url = {https://doi.org/10.3897/bdj.14.e191439},
pmid = {42668897},
pmcid = {PMC13525407}
}

RIS

TY - JOUR
AU - Asanbe, Bernard
TI - Assessing patterns of extinction risk amongst mammal species in Nigeria: A comparative analysis of human impact
T2 - Biodiversity data journal
J2 - Biodivers Data J
PY - 2026
DA - 2026/08/21
VL - 14
SP - e191439
SN - 1314-2828
PB - Pensoft Publishers
DO - 10.3897/bdj.14.e191439
UR - https://doi.org/10.3897/bdj.14.e191439
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13525407",
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"date-parts": [
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]
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