Philippa Kaur

Description of the study areaThe study covers urban, peri-urban and rural wetlands in the Bamenda Municipality of the North West Region of Cameroon that have evolved concomitantly with different stages of urbanization (Fig. 1). In this study, urbanisation is considered a loose term that is aimed at giving a geographical expression to the variation in the economic, social and cultural practices in the milieu. The central town with many economic activities is termed the urban, the fringe area with sprawls is termed peri-urban while the rural has typical peasant activities and make-shift structures. From the variation of human activities in the three sub-zones, a variety of chemical substances are discharged into drains, playing a substantial role in soil quality, and therefore plant macrophyte diversity. The Plants studied were ubiquitous in the area and verification of their IUCN conservation status in the red data book of plants of Cameroon confirmed their abundance14. Information on protected sites in Cameroon does not place the study area under conservation status. In line with that, permits are not required to undertake academic and research studies as well as do a responsible collection of plants in the study area. The urbanization rate of Bamenda is 42%, and the population grew from 48,111 inhabitants in 1976 to 488,883 inhabitants in 201015, with 150–200 inhabitants/km2.Figure 1Relief Map of Bamenda showing the Bamenda escarpment, topography and the location for quadrat sites.Full size imageThe study area is part of the Bamenda escarpment that is located between latitudes 5° 55″ N and 6° 30″ N and longitudes 10° 25″ E and 10° 67″ E. The town shows an altitudinal range of 1200–1700 m and is divided into two parts by escarpments—a low-lying and gently undulating part with altitudes ranging from 1200 to 1400 m, with many flat areas that are usually inundated for most parts of the year, and an elevated part that range from 1400 to 1700 m altitude. Most of the streams take their rise from this elevated part (Fig. 1).This area experiences two seasons—a rainy season (mid-March to mid-October) and a short dry season (mid-October to mid-March). The thermic and hyperthermic temperature regimes dominate in the area. The mean annual temperature stands at 19.9 °C. January and February are the hottest months with mean monthly temperatures of 29.1 and 29.7 °C, respectively. This area is dominated by the Ustic and Udic moisture regimes with the Udic extending to the south9. Annual rainfall ranges from 1300 to 3000 mm16. The area has a rich hydrographical network with intense human activities and a dense population along different water courses in the watershed. The area is bounded on the West, North and East by the Cameroon Volcanic Line (made up of basalts, trachytes, rhyolites and numerous salt springs). The geologic history of this area originates from the Precambrian era when there was a vast formation of geosynclinal complexes, which became filled with clay-calcareous, and sandstone sediments9. These materials, crossed by intrusions of crystalline rocks, were folded in a generally NE-SW direction and underwent variable metamorphism9. The Rocks in the area are thus of igneous (granitic and volcanic) and metamorphic (migmatites) origin17, which gives rise to ferralitic soils18.Agriculture is the principal human activity in and around this region18. The area equally harbours the commercial center that has factories ranging from soap production, and mechanic workshops to metallurgy, which may be potential sources of pollutants that can influence wetland Geochemistry. Raffia farinifera bush, which is largely limited to the wetlands, is an important vegetation type in this area. R. farinifera provides raffia wine, a vital economic resource to the inhabitants who are fighting against the cultivation of these wetlands by vegetable farmers.Methods of the studyMacrophyte diversity studyThe plant diversity of the wetlands was evaluated using quadrats in the dry season for accessibility reasons. For each of the three wetlands (the urban, peri-urban and rural areas), three transects were established on which representative quadrats, each measuring 10 m × 10 m, were mapped out in uncultivated areas for the determination of plant species cover-abundance and diversity. It is perceived that the different zones receive different mixtures of chemical substances and thus influence macrophyte diversity differently.According to a publication by14 on the vascular plants of Cameroon and a taxonomic checklist with IUCN assessment, the plants of the area are placed under the Least Concern Category(LC), and therefore not in the risky category. Diversity studies involved the identification of a specific area called “relevé” by progressively increasing test quadrat size and sampling for specific diversity until the smallest area with adequate species representation was reached. The relevé size determined here was 1 m2, making a total of 300 sub-quadrats (relevé) in the entire study ie. 100 in each main quadrat). For each site (main quadrat), 10 representative relevés were sampled and all plant species were enumerated. Most plant species in each of them were identified in the field by the Botanist, Dr Ndam Lawrence Monah using visual observation of the morphology of the leaves and flowers, a self-made field guide, the Flora of West Africa and the Flora of Cameroon. 10 unidentified plants were appropriately collected where there were in abundance, placed onto a conventional plant press and dried in the field. Voucher specimens were tagged and transported to the Limbe Botanic Gardens (SCA: Southern Cameroon, the code of the Limbe Botanic Gardens Herbarium) for identification. Mr Elias Ndive, the Taxonomist of the Limbe Botanic Gardens compared unidentified specimens with authentic herbarium specimens and other taxonomic guides and finally identified them. Voucher specimens of the 10 plants were given identification numbers and deposited in the Herbarium of the Limbe Botanic Gardens.The Braun–Banquet method was used19 for the assessment of species cover abundance. Relative abundance and abundance ratings were determined using the Braun–Banquet rating scheme (Table 1).Table 1 Braun-Blanquet rating scheme for vegetation cover-abundance, Source19.Full size tableSimpson’s diversity indexSpecies richness was evaluated using Simpson’s diversity index (D), which takes into account both species richness and the Braun-Blanquet rating scheme for vegetation cover abundance and evenness of abundance among the species present. In essence, D measures the probability that two individuals that are randomly selected from an area will belong to the same species. The formula for calculating D is presented as:$${text{D}} = frac{{sum {{text{n}}_{i} left( {{text{n}}_{i} – 1} right)} }}{{{text{N}}({text{N}} – 1)}}$$where ni = the total number of each species; N = the total number of individuals of all species.The value of D ranges from 0 to 1. With this index, 0 represents infinite diversity and 1 represents no diversity. That is, the larger the value the lower the diversity.Alternatively, Simpson’s Diversity Index, = 1–D,1-D was used as a measure of diversity because it is more logical and less likely to cause confusion. The scale then gives a score from 0 to 1 with higher scores showing higher diversity (instead of being associated with low scores).The Simpson index is a dominance index because it gives more weight to common or dominant species. In this case, a few rare species with only a few representatives will not affect the diversity.
Soil sampling and analysisSoil sampling was done in and around the three quadrats laid in the urban, peri-urban and rural wetlands for macrophytes sampling. Twenty-one (21) composite samples (0–25 cm) were randomly collected (Fig. 2) and taken to the laboratory in black plastic bags. Each composite sample was a collection of 5 dried core soil samples. Due to the observed greater heterogeneity in the urban sector, the sampling density was intensified. The soil samples were air-dried and screened through a 2-mm sieve. They were analyzed in duplicate for their physicochemical properties in the Environmental and Analytical Chemistry Laboratory of the University of Dschang, Cameroon. Particle size distribution, cation exchange capacity (CEC), exchangeable bases, electrical conductivity (EC) and pH were determined by standard procedures20. Soil pH was measured both in water and KCl (1:2.5 soil/water mixture) using a glass electrode pH meter. Part of the soil was ball-milled for organic carbon (Walkley–Black method) and total nitrogen (Macro-Kjeldahl method) as largely described by20. Available phosphorus (P) was determined by Bray I method. Exchangeable cations were extracted using 1 N ammonium acetate at pH 7. Potassium (K) and sodium (Na) in the extract were determined using a flame photometer and magnesium (Mg) and calcium (Ca) were determined by complexiometric titration. Exchange acidity was extracted with 1 M KCl followed by quantification of Al and H by titration20. Effective cation exchange capacity (ECEC) was determined as the sum of bases and exchanged acidity.Figure 2Adapted from the 1980 land use map of the Bamenda City Area showing soil sampling points: Source Bamenda City Council.Map of the study area in freshwater wetlands of Bamenda Municipality.Full size imageApparent CEC (CEC at pH 7) was determined directly as outlined by20. Based on critical values of nutrients established for vegetables, nutrients were declared sufficient or deficient.
Statistical analysisThe data were subjected to statistical analysis using Microsoft Excel 2007 and SPSS statistical package 20.0. Soil properties were assessed for their variability using the coefficient of variation (CV) and compared with variability classes (Table 2).$$CV=frac{Sd}{X}X 100$$where: Sd = standard deviation; = X arithmetic mean of soil properties.Table 2 Grouping coefficient of variation into variability classes.Full size tableThe hierarchical cluster analysis (HCA) was used to group the area under managing units. The main goal of the hierarchical agglomerative cluster analysis is to spontaneously classify the data into groups of similarity (clusters). This is done by searching objects in the n-dimensional space that is located in the closest neighborhood and separating a stable cluster from other clusters. The sampling sites were considered objects for classification. Each object was determined by a set of variables (chemical concentrations of the soils in this case). More

Study population and study designThis is a prospective interventional cohort study of healthy S. aureus carriers and noncarriers in the Netherlands. All experiments were performed in accordance with the Dutch Medical Research Involving Human Subjects Act (WMO). The study protocol was approved by the local Medical Ethical Committee of the Erasmus University Medical Centre Rotterdam, The Netherlands (MEC-2018-091). Written informed consent was obtained for all participants. Participants were recruited through advertisements at Dutch universities and the research teams social networks. Exclusion criteria were age  8 CFU/mL for each culture. Noncarriers were defined as 2 S. aureus-negative cultures. Intermittent S. aureus carriers were excluded from further participation in the study. Eligible volunteers were enrolled on a first-come, first-served basis.Eligible participants were asked to fill out a questionnaire regarding risk factors for S. aureus acquisition. All participants received decolonization treatment. Decolonization consisted of mupirocin nasal ointment (2%, GlaxoSmithKline BV, Zeist, the Netherlands) twice daily and chlorhexidine gluconate cutaneous solution (4%w/v, Regent Medical Overseas Limited, Oldham, UK) once daily, both for 5 days.Nasal samples were taken 1 day before decolonization (D0) and 2 days (D7), 1 month (M1), 3 months (M3) and 6 months (M6) after decolonization. All participants received a personal demonstration for nasal sampling by the executive researcher. Thereafter, all specimens were taken by the participants by inserting a swab (ESwab, 490CE.A, Copan Italia, Brescia, Italy) into one nostril and rotating 5 times, repeating this in the second nostril using the same swab. Swabs were collected in a container filled with 1 ml modified Liquid Amies, a collection and transport solution, and sent through regular mail service (non-temperature controlled) or deposited at the laboratory personally.
Staphylococcus aureus quantitative cultureQuantitative S. aureus cultures were conducted to examine the dynamics of S. aureus carriage over the 6-month follow-up period after decolonization. Swab containers were vortexed for 20 s before plating. Serial dilutions of Amies medium were plated onto phenol mannitol salt agar (PHMA) and incubated for 2 days at 37 °C. Swabs were placed in phenol mannitol salt broth (PHMB) and incubated for 7 days at 37 °C for enrichment. S. aureus growth was confirmed by a latex agglutination test (Staph Plus Latex Kit, Diamondial, Vienna, Austria). Morphologically different S. aureus colonies were selected for spa typing and methicillin resistance screening using BBL CHROMagar MRSA II agar (BD, Breda, The Netherlands).
Spa typingMolecular typing of S. aureus isolates was performed to infer whether recolonization with S. aureus in decolonized carriers involved the same spa-type. Typing was limited to the last S. aureus positive culture moment and the last S. aureus positive culture moment after decolonization in recolonised carriers. S. aureus DNA lysates were prepared by boiling in 10 mM Tris–HCl, 1 mM disodium EDTA, pH 8.0 or extraction with the QIAamp DNA Mini Kit (QIAGEN, Venlo, The Netherlands) according to the manufacturer’s instructions. Amplification of the S. aureus protein A (spa) repeat region was performed by PCR with 2 sets of primers. One set consisted of forward primer spa-1113, 5′-TAAAGACGATCCTTCGGTGAGC-3′ and reverse primer spa-1514, 5′-CAGCAGTAGTGCCGTTTGCTT-3′24. The other set consisted of forward primers spa-F1, 5′-AACAACGTAACGGCTTCATCC-3′ and spa-F2 5′-AGACGATCCTTCAGTGAGC-3′ and reverse primer spa-R1 5′-GCTTTTGCAATGTCATTTACTG-3′. Amplicons were purified with ExoSAP-IT (Applied Biosystems) according to the manufacturer’s instructions and sent for sequence analysis (Baseclear, Leiden, The Netherlands). Resulting sequences were analysed using BioNumerics v7.6 (Applied Maths NV, Sint-Martens-Latem, Belgium) and the spa types were assigned by use of the RidomStaphType database (Ridom GmbH, Würzburg, Germany).16S ribosomal RNA sequencing of nasal microbiotaThe impact of decolonization on the nasal microbiome and the recovery of the microbiome structure after decolonization were examined by means of 16S rRNA metabarcoding. Amies medium from each nasal swab container was stored at − 80 °C on the day of receipt at the study laboratory in Rotterdam, NL, then sent at − 80 °C to the microbiome analysis laboratory in Lyon, FR. To properly capture the impact of decolonization on the living microbiota, metabarcoding used RNA-based 16S ribosomal RNA (rRNA, which is preserved in living cells but quickly cleared after cell death or lysis) rather than the DNA coding sequence, as DNA can persist for prolonged time periods after cell death25,26,27,28. RNA was extracted using the Mag Bind® Total RNA 96 Kit (Omega Bio-tek) tissue protocol from 150 µL of samples’ material. Cell lysis was performed using beads (Disruptor plate C plus—Omega Bio-tek) and proteinase K for 15 min at 2600 rpm, followed by 10 min at room temperature without agitation, and finished with a DNase I digestion of 20 min at room temperature. RNA was quantified using QuantiFluor RNA kit on Tecan Safire (TECAN). 10 ng total RNA was used for reverse transcription using FIREScript RT cDNA synthesis kit (Solis Biodyne) with random primers, then cDNA was purified with SPRIselect reagent (Beckman coulter) and quantified.The rRNA V1–V3 region was PCR amplified using the 5× HOT BIOAmp® BlendMaster Mix 12,5 mM MgCl 2 (Biofidal), 10× GC rich Enhancer (Biofidal) and BSA 20 mg/mL. The PCR reaction consisted of 30 cycles at 56 °C using the forward primer 27F, 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG AGAGTTTGATCCTGGCTCAG-3′ and reverse primer 534R, 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGATTACCGCGGCTGCTGG-3′ in 25 µL of solution. PCR products were purified using SPRIselect beads (Beckman Coulter) in 20 µL nuclease-free water and quantified using QuantiFluor dsDNA (Promega). Samples were indexed with lllumina’s barcodes with the same PCR reagents during a 12 cycles PCR, then purified and quantified as previously mentioned. Samples were normalized and pooled, then sequenced using Illumina MiSeq V3 Flow Cell following the constructor’s recommendations for a 2 × 300 bp paired-end application. A mean of 130 k proofread reads per sample was obtained.Experiment buffers were used as negative controls to detect contamination by out-of-sample bacterial RNA. RNA extraction was controlled using an in-house mix of live Staphylococcus aureus ATCC29213 and Escherichia coli ATCC25922 in equal proportions, allowing for assessing extraction bias in Gram-positive and -negative bacteria. PCR amplification bias was controlled using a commercial DNA mix of 8 bacterial species (ZymoBIOMICS™ Microbial Community DNA Standard).Bioinformatics and statistical analysesSequencing reads were quality checked and trimmed. Paired-ended read pairs were merged using BBMap version 38.49 (available at https://sourceforge.net/projects/bbmap/), with default options besides a minimum single size of 150 bp with an average Phred quality score higher than 10, and a total pair size of minimum 400 bp. PCR adapters were removed with cutadapt v.2.1 (Martin 2011) then dereplicated using vsearch v.2.12.029 with the sizeout option. For species assignment, reads were aligned to sequences of NCBI blast 16S_ribosomal_RNA database (version date 03.12.2020) using Blastn v.2.11.0+30,31, keeping a maximum of 20 reference targets. Read counts per bacterial species were normalized to account for taxon-specific variations of the copy number of 16S rRNA genes using NCBI rrnDB-5.5 database based on the mean gene copy number in the taxon.To optimize the resolution of sequencing read taxonomic assignment, we used in-house bioinformatic software publicly available at https://github.com/rasigadelab/taxonresolve. Briefly, when a read matches sequences from several species with identical alignment scores, taxonomic assignment pipelines typically output the higher taxonomic level such as the genus (e.g., Staphylococcus spp. when a read matches S. aureus and S. epidermidis). This loss of information can be problematic when species-level discrimination is important. To prevent losing species-level information, the taxonresolve software assigns reads with uncertain species to groups of species rather than to genera.Bacterial species deemed present from contaminating sources such as kits reagents and found in negative controls, mostly from the Bacillus genera, were removed. A total of 1376 species or group of species were retained. The rarefaction curves corresponding to the sequencing effort to assess the species richness within samples are shown in Supplementary Fig. 3. Most samples reached a plateau after 40,000 sequences.Given the small sample size compared to the number of variables and species considered in this study, no hypothesis testing was performed, and we provide a descriptive assessment of the results. In figures, 95% confidence intervals of the means were computed based on normal approximation, after log transformation for CFU/mL and log odds transformation for quantities restricted to the [0, 1] interval, such as proportions.In microbial diversity analyses, we retained the 9 most prevalent bacterial species and pooled the other species into an ‘Others’ category. To assess the disruption and possible recovery of the microbiota, the divergence of sampled microbiota relative to the initial, pre-treatment microbiota (D0) was assessed using the Bray–Curtis dissimilarity at each sampling time point relative to the first sample of the same patient.Software code of the analyses are available at https://github.com/rasigadelab/macotra-metabarcoding. Data are available at https://zenodo.org/record/6382657. Analyses and figures used R software v3.6.032 with packages dplyr33, ggplot234, vegan35, and MicrobiomAnalyst available at https://www.microbiomeanalyst.ca36,37. More

Kristensen, T. N. et al. A test of quantitative genetic theory using Drosophila- effects of inbreeding and rate of inbreeding on heritabilities and variance components. J. Evol. Biol. 18, 763–770 (2005).CAS 
PubMed 

Google Scholar 
Angeloni, F., Ouborg, N. J. & Leimu, R. Meta-analysis on the association of population size and life history with inbreeding depression in plants. Biol. Conserv. 144, 35–43 (2011).
Google Scholar 
Caballero, A., Bravo, I. & Wang, J. Inbreeding load and purging: implications for the short-term survival and the conservation management of small populations. Heredity 118, 177–185 (2017).CAS 
PubMed 

Google Scholar 
Park, D. S., Ellison, A. M. & Davis, C. C. Mating system does not predict niche breath. Glob. Ecol. Biogeogr. 27, 804–813 (2018).
Google Scholar 
Buckley, J., Daly, R., Cobbold, C. A., Burgess, K. & Mable, B. K. Changing environments and genetic variation: natural variation in inbreeding does not compromise short-term physiological responses. Proc. R. Soc. B Biol. Sci. 286, 20192109 (2019).
Google Scholar 
Grossenbacher, D., Briscoe Runquist, R., Goldberg, E. E. & Brandvain, Y. Geographic range size is predicted by plant mating system. Ecol. Lett. 18, 706–713 (2015).PubMed 

Google Scholar 
Wright, S. I., Lauga, B. & Charlesworth, D. Rates and patterns of molecular evolution in inbred and outbred arabidopsis. Mol. Biol. Evol. 19, 1407–1420 (2002).CAS 
PubMed 

Google Scholar 
Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Platt, A. et al. The scale of population structure in Arabidopsis thaliana. PLoS Genet. 6, e1000843 (2010).PubMed 
PubMed Central 

Google Scholar 
Lynch, M., Conery, J. & Burger, R. Mutation accumulation and the extinction of small populations. Am. Nat. 146, 489–518 (1995).
Google Scholar 
Willis, J. H. The role of genes of large effect on inbreeding depression in Mimulus guttatus. Evolution 53, 1678–1691 (1999).CAS 
PubMed 

Google Scholar 
Coron, C., Méléard, S., Porcher, E. & Robert, A. Quantifying the mutational meltdown in diploid populations. Am. Nat. 181, 623–636 (2013).PubMed 

Google Scholar 
Kyriazis, C. C., Wayne, R. K. & Lohmueller, K. E. Strongly deleterious mutations are a primary determinant of extinction risk due to inbreeding depression. Evol. Lett. https://doi.org/10.1002/evl3.209 (2020).Barrett, S. C. H. & Charlesworth, D. Effects of a change in the level of inbreeding on the genetic load. Nature 352, 522–524 (1991).ADS 
CAS 
PubMed 

Google Scholar 
Hedrick, P. W. & Garcia-Dorado, A. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31, 940–952 (2016).PubMed 

Google Scholar 
Robinson, J. A., Brown, C., Kim, B. Y., Lohmueller, K. E. & Wayne, R. K. Purging of strongly deleterious mutations explains long-term persistence and absence of inbreeding depression in island foxes. Curr. Biol. 28, 3487–3494.e4 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan, A. et al. Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. Proc. Natl. Acad. Sci. USA 118, e2023018118 (2021).Byers, D. L. & Waller, D. M. Do plant populations purge their genetic load? Effects of population size and mating history on inbreeding depression. Annu. Rev. Ecol. Syst. 30, 479–513 (1999).
Google Scholar 
Goodwillie, C., Kalisz, S. & Eckert, C. G. The evolutionary enigma of mixed mating systems in plants: occurrence, theoretical explanations, and empirical evidence. Annu. Rev. Ecol. Evol. Syst. 36, 47–79 (2005).
Google Scholar 
Hill, W. G. & Robertson, A. The effect of linkage on limits to artificial selection. Genet. Res. 8, 269–294 (1966).CAS 
PubMed 

Google Scholar 
Covert, A. W. III, Lenski, R. E., Wilke, C. O. & Ofria, C. Experiments on the role of deleterious mutations as stepping stones in adaptive evolution. Proc. Natl Acad. Sci. USA 110, E3171–E3178 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Castellano, D., Coronado-Zamora, M., Campos, J. L., Barbadilla, A. & Eyre-Walker, A. Adaptive evolution is substantially impeded by hill–robertson interference in Drosophila. Mol. Biol. Evol. 33, 442–455 (2016).CAS 
PubMed 

Google Scholar 
Anderson, J. T., Lee, C.-R., Rushworth, C. A., Colautti, R. I. & Mitchell-Olds, T. Genetic trade-offs and conditional neutrality contribute to local adaptation. Mol. Ecol. 22, 699–708 (2013).PubMed 

Google Scholar 
Hämälä, T. & Savolainen, O. Genomic patterns of local adaptation under gene flow in arabidopsis lyrata. Mol. Biol. Evol. 36, 2557–2571 (2019).
Google Scholar 
Taylor, M. A. et al. Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 116, 17890–17899 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Storz, J. F. Causes of molecular convergence and parallelism in protein evolution. Nat. Rev. Genet. 17, 239–250 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mimura, M. & Aitken, S. N. Local adaptation at the range peripheries of Sitka spruce. J. Evol. Biol. 23, 249–258 (2010).CAS 
PubMed 

Google Scholar 
Stanton-Geddes, J., Tiffin, P. & Shaw, R. G. Role of climate and competitors in limiting fitness across range edges of an annual plant. Ecology 93, 1604–1613 (2012).PubMed 

Google Scholar 
Vergeer, P. & Kunin, W. E. Adaptation at range margins: common garden trials and the performance of Arabidopsis lyrata across its northwestern European range. N. Phytol. 197, 989–1001 (2013).
Google Scholar 
Volis, S., Ormanbekova, D. & Shulgina, I. Role of selection and gene flow in population differentiation at the edge vs. interior of the species range differing in climatic conditions. Mol. Ecol. 25, 1449–1464 (2016).CAS 
PubMed 

Google Scholar 
Glémin, S., Bazin, E. & Charlesworth, D. Impact of mating systems on patterns of sequence polymorphism in flowering plants. Proc. R. Soc. B Biol. Sci. 273, 3011–3019 (2006).
Google Scholar 
Almeida‐Rocha, J. M., Soares, L. A. S. S., Andrade, E. R., Gaiotto, F. A. & Cazetta, E. The impact of anthropogenic disturbances on the genetic diversity of terrestrial species: A global meta‐analysis. Mol. Ecol. 29, 4812–4822 (2020).PubMed 

Google Scholar 
Schrader, L. et al. Transposable element islands facilitate adaptation to novel environments in an invasive species. Nat. Commun. 5, 5495 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Lu, L. et al. Tracking the genome-wide outcomes of a transposable element burst over decades of amplification. Proc. Natl Acad. Sci. USA 114, E10550–E10559 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schrader, L. & Schmitz, J. The impact of transposable elements in adaptive evolution. Mol. Ecol. 28, 1537–1549 (2019).PubMed 

Google Scholar 
Habig, M., Lorrain, C., Feurtey, A., Komluski, J. & Stukenbrock, E. H. Epigenetic modifications affect the rate of spontaneous mutations in a pathogenic fungus. Nat. Commun. 12, 1–13 (2021).
Google Scholar 
Wicker, T. et al. DNA transposon activity is associated with increased mutation rates in genes of rice and other grasses. Nat. Commun. 7, 1–9 (2016).
Google Scholar 
Dubin, M. J., Mittelsten Scheid, O. & Becker, C. Transposons: a blessing curse. Curr. Opin. Plant Biol. 42, 23–29 (2018).CAS 
PubMed 

Google Scholar 
Stapley, J., Santure, A. W. & Dennis, S. R. Transposable elements as agents of rapid adaptation may explain the genetic paradox of invasive species. Mol. Ecol. 24, 2241–2252 (2015).CAS 
PubMed 

Google Scholar 
Sultana, T., Zamborlini, A., Cristofari, G. & Lesage, P. Integration site selection by retroviruses and transposable elements in eukaryotes. Nat. Rev. Genet. 18, 292–308 (2017).CAS 
PubMed 

Google Scholar 
Baduel, P. et al. Genetic and environmental modulation of transposition shapes the evolutionary potential of Arabidopsis thaliana. Genome Biol. 22, 1–26 (2021).
Google Scholar 
Quesneville, H. Twenty years of transposable element analysis in the Arabidopsis thaliana genome. Mob. DNA 11, 28 (2020).PubMed 
PubMed Central 

Google Scholar 
Ossowski, S. et al. The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana. Science 327, 92–94 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Coulondre, C., Miller, J. H., Farabaugh, P. J. & Gilbert, W. Molecular basis of base substitution hotspots in Escherichia coli. Nature 274, 775–780 (1978).ADS 
CAS 
PubMed 

Google Scholar 
Duncan, B. K. & Miller, J. H. Mutagenic deamination of cytosine residues in DNA. Nature 287, 560–561 (1980).ADS 
CAS 
PubMed 

Google Scholar 
Linquist, S. et al. Distinguishing ecological from evolutionary approaches to transposable elements. Biol. Rev. 88, 573–584 (2013).PubMed 

Google Scholar 
Dupeyron, M., Singh, K. S., Bass, C. & Hayward, A. Evolution of Mutator transposable elements across eukaryotic diversity. Mob. DNA 10, 1–14 (2019).
Google Scholar 
Batstone, R. T. Genomes within genomes: nested symbiosis and its implications for plant evolution. New Phytol. https://doi.org/10.1111/nph.17847 (2021).Pietzenuk, B. et al. Recurrent evolution of heat-responsiveness in Brassicaceae COPIA elements. Genome Biol. 17, 209 (2016).PubMed 
PubMed Central 

Google Scholar 
Horváth, V., Merenciano, M. & González, J. Revisiting the relationship between transposable elements and the eukaryotic stress response. Trends Genet. 33, 832–841 (2017).PubMed 

Google Scholar 
Liu, S. et al. Role of H1 and DNA methylation in selective regulation of transposable elements during heat stress. N. Phytol. 229, 2238–2250 (2021).CAS 

Google Scholar 
Mao, H. et al. A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nat. Commun. 6, 1–13 (2015).ADS 
CAS 

Google Scholar 
Castelletti, S., Tuberosa, R., Pindo, M. & Salvi, S. A MITE transposon insertion is associated with differential methylation at the maize flowering time QTL vgt1. G3 Genes, Genomes, Genet. 4, 805–812 (2014).CAS 

Google Scholar 
Legrand, S. et al. Differential retention of transposable element-derived sequences in outcrossing Arabidopsis genomes. Mob. DNA 10, 30 (2019).PubMed 
PubMed Central 

Google Scholar 
Quadrana, L. et al. Transposition favors the generation of large effect mutations that may facilitate rapid adaption. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
Teschendorf, A. E. & Relton, C. L. Statistical and integrative system-level analysis of DNA methylation data. Nat. Rev. Genet. 19, 129–147 (2019).
Google Scholar 
Bonchev, G. & Willi, Y. Accumulation of transposable elements in selfing populations of Arabidopsis lyrata supports the ectopic recombination model of transposon evolution. N. Phytol. 219, 767–778 (2018).CAS 

Google Scholar 
Lockton, S., Ross-Ibarra, J. & Gaut, B. S. Demography and weak selection drive patterns of transposable element diversity in natural populations of Arabidopsis lyrata. Proc. Natl Acad. Sci. USA 105, 13965–13970 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lockton, S. & Gaut, B. S. The evolution of transposable elements in natural populations of self-fertilizing Arabidopsis thaliana and its outcrossing relative Arabidopsis lyrata. BMC Evol. Biol. 10, 10 (2010).PubMed 
PubMed Central 

Google Scholar 
Mable, B. K., Dart, A. V. R., Berardo, C., Di & Witham, L. Breakdown of self-incompatibility in the perennial Arabidopsis lyrata (Brassicaceae) and its genetic consequences. Evolution 59, 1437–1448 (2005).PubMed 

Google Scholar 
Foxe, J. P. et al. Reconstructing origins of loss of self-incompatibility and selfing in North American Arabidopsis lyrata: a population genetic context. Evolution 64, 3495–3510 (2010).PubMed 

Google Scholar 
Quadrana, L. et al. The Arabidopsis thaliana mobilome and its impact at the species level. Elife 5, e15716 (2016).Stuart, T. et al. Population scale mapping of transposable element diversity reveals links to gene regulation and epigenomic variation. Elife 5, e20777 (2016).Willi, Y. Mutational meltdown in selfing Arabidopsis lyrata. Evolution 67, 806–815 (2013).PubMed 

Google Scholar 
Joschinski, J., van Kleunen, M. & Stift, M. Costs associated with the evolution of selfing in North American populations of Arabidopsis lyrata? Evol. Ecol. 29, 749–764 (2015).
Google Scholar 
Koonin, E. V. & Krupovic, M. Evolution of adaptive immunity from transposable elements combined with innate immune systems. Nat. Rev. Genet. 16, 184–192 (2015).CAS 
PubMed 

Google Scholar 
Li, Z.-W. et al. Transposable elements contribute to the adaptation of Arabidopsis thaliana. Genome Biol. Evol. 10, 2140–2150 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Catlin, N. S. & Josephs, E. B. The important contribution of transposable elements to phenotypic variation and evolution. Curr. Opin. Plant Biol. 65, 102140 (2022).CAS 
PubMed 

Google Scholar 
Casacuberta, E. & González, J. The impact of transposable elements in environmental adaptation. Mol. Ecol. 22, 1503–1517 (2013).CAS 
PubMed 

Google Scholar 
Baduel, P., Quadrana, L., Hunter, B., Bomblies, K. & Colot, V. Relaxed purifying selection in autopolyploids drives transposable element over-accumulation which provides variants for local adaptation. Nat. Commun. 10, 1–10 (2019).
Google Scholar 
Wos, G., Choudhury, R. R., Kolář, F. & Parisod, C. Transcriptional activity of transposable elements along an elevational gradient in Arabidopsis arenosa. Mob. DNA 12, 7 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stebbins, G. L. Self fertilization and population variability in the higher plants. Am. Nat. 91, 337–354 (1957).
Google Scholar 
Takebayashi, N. & Morrell, P. L. Is self-fertilization an evolutionary dead end? Revisiting an old hypothesis with genetic theories and a macroevolutionary approach. Am. J. Bot. 88, 1143–1150 (2001).CAS 
PubMed 

Google Scholar 
Igic, B. & Busch, J. W. Is self‐fertilization an evolutionary dead end? N. Phytol. 198, 386–397 (2013).
Google Scholar 
Goldberg, E. E. et al. Species selection maintains self-incompatibility. Science 330, 493–495 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Abu Awad, D. & Billiard, S. The double edged sword: The demographic consequences of the evolution of self-fertilization. Evolution 71, 1178–1190 (2017).PubMed 

Google Scholar 
Fijarczyk, A. & Babik, W. Detecting balancing selection in genomes: limits and prospects. Mol. Ecol. 24, 3529–3545 (2015).CAS 
PubMed 

Google Scholar 
Wu, Q. et al. Long-term balancing selection contributes to adaptation in Arabidopsis and its relatives. Genome Biol. 18, 1–15 (2017).CAS 

Google Scholar 
Kerwin, R. et al. Natural genetic variation in Arabidopsis thaliana defense metabolism genes modulates field fitness. Elife 2015, 1–28 (2015).
Google Scholar 
Waller, D. M. Addressing Darwin’s dilemma: can pseudo-overdominance explain persistent inbreeding depression and load? Evolution 75, 779–793 (2021).CAS 
PubMed 

Google Scholar 
Gilbert, K. J., Pouyet, F., Excoffier, L. & Peischl, S. Transition from background selection to associative overdominance promotes diversity in regions of low recombination. Curr. Biol. 30, 101–107.e3 (2020).CAS 
PubMed 

Google Scholar 
Buckley, J. et al. R-gene variation across Arabidopsis lyrata subspecies: effects of population structure, selection and mating system. BMC Evol. Biol. 16, 93 (2016).PubMed 
PubMed Central 

Google Scholar 
Schmickl, R., Jørgensen, M. H., Brysting, A. K. & Koch, M. A. Phylogeographic implications for the north american boreal-arctic Arabidopsis lyrata complex. Plant Ecol. Divers. 1, 245–254 (2008).
Google Scholar 
Buckley, J., Holub, E. B., Koch, M. A., Vergeer, P. & Mable, B. K. Restriction associated DNA-genotyping at multiple spatial scales in Arabidopsis lyrata reveals signatures of pathogen-mediated selection. BMC Genomics 19, 1–21 (2018).
Google Scholar 
Flutre, T., Duprat, E., Feuillet, C. & Quesneville, H. Considering transposable element diversification in de novo annotation approaches. PLoS ONE 6, e16526 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Luu, K., Bazin, E. & Blum, M. G. B. pcadapt: an R package to perform genome scans for selection based on principal component analysis. in. Mol. Ecol. Resour. 17, 67–77 (2017).CAS 
PubMed 

Google Scholar 
Privé, F., Luu, K., Vilhjálmsson, B. J. & Blum, M. G. B. Performing highly efficient genome scans for local adaptation with R Package pcadapt Version 4. Mol. Biol. Evol. 37, 2153–2154 (2020).PubMed 

Google Scholar 
Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 

Google Scholar 
Charlesworth, J. & Eyre-Walker, A. The McDonald-Kreitman test and slightly deleterious mutations. Mol. Biol. Evol. 25, 1007–1015 (2008).CAS 
PubMed 

Google Scholar 
Hernandez, R. D. et al. Ultrarare variants drive substantial cis heritability of human gene expression. Nat. Genet. 51, 1349–1355 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson, R. J. et al. Evidence for widespread positive and negative selection in coding and conserved noncoding regions of capsella grandiflora. PLoS Genet. 10, e1004622 (2014).PubMed 
PubMed Central 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).
Google Scholar 
Mattila, T. M., Tyrmi, J., Pyhäjärvi, T. & Savolainen, O. Genome-wide analysis of colonization history and concomitant selection in Arabidopsis lyrata. Mol. Biol. Evol. 34, 2665–2677 (2017).CAS 
PubMed 

Google Scholar  More

G.M., L.O.A., L.V.G. and L.E.O.C.A. thank the São Paulo Research Foundation (FAPESP) for funding (grants 2019/25701-8, 2020/08916-8, 2016/02018-2 and 2020/15230-5, respectively). L.O.A. and L.E.O.C.A. thank the National Council for Scientific and Technological Development (CNPq) for funding (grants 314473/2020-3 and 314416/2020-0, respectively). G.d.O. thanks the University of South Alabama Faculty Development Council Grant for funding (grant 279600-2022). More

Tsai, C.-W., Lai, C.-F., Chao, H.-C. & Vasilakos, A. V. Big data analytics: a survey. J. Big Data 2, 21 (2015).
Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
CAS 

Google Scholar 
Manzoni, C. et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19, 286–302 (2018).CAS 

Google Scholar 
Lichtman, J. W., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat. Neurosci. 17, 1448–1454 (2014).CAS 

Google Scholar 
Altaf-Ul-Amin, M., Afendi, F. M., Kiboi, S. K. & Kanaya, S. Systems biology in the context of big data and networks. Biomed. Res. Int. 2014, 428570 (2014).
Google Scholar 
Xia, J., Wang, J. & Niu, S. Research challenges and opportunities for using big data in global change biology. Glob. Change Biol. 26, 6040–6061 (2020).ADS 

Google Scholar 
Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).ADS 
CAS 

Google Scholar 
Hussey, N. E. et al. Ecology. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642 (2015).
Google Scholar 
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).
Google Scholar 
Sherub, S., Fiedler, W., Duriez, O. & Wikelski, M. Bio-logging, new technologies to study conservation physiology on the move: a case study on annual survival of Himalayan vultures. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203, 531–542 (2017).
Google Scholar 
Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).CAS 

Google Scholar 
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. 89, 161–172 (2020).
Google Scholar 
Patterson, A., Gilchrist, H. G., Chivers, L., Hatch, S. & Elliott, K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol. Evol. 9, 3030–3045 (2019).
Google Scholar 
Masello, J. F. et al. How animals distribute themselves in space: energy landscapes of Antarctic avian predators. Mov. Ecol. 9, 24 (2021).
Google Scholar 
Shepard, E. L. C. et al. Energy landscapes shape animal movement ecology. Am. Nat. 182, 298–312 (2013).
Google Scholar 
Elliott, K. H., Le Vaillant, M., Kato, A., Speakman, J. R. & Ropert-Coudert, Y. Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol. Lett. 9, 20120919 (2013).
Google Scholar 
Nickel, B. A., Suraci, J. P., Nisi, A. C. & Wilmers, C. C. Energetics and fear of humans constrain the spatial ecology of pumas. Proc. Natl. Acad. Sci. USA 118, e2004592118 (2021).
Eisaguirre, J. M., Booms, T. L., Barger, C. P., Lewis, S. B. & Breed, G. A. Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds. J. Anim. Ecol. 89, 2567–2583 (2020).
Google Scholar 
Wittemyer, G., Northrup, J. M. & Bastille-Rousseau, G. Behavioural valuation of landscapes using movement data. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180046 (2019).
Google Scholar 
Chimienti, M. et al. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data. Ecol. Evol. 6, 727–741 (2016).
Google Scholar 
Hounslow, J. L. et al. Assessing the effects of sampling frequency on behavioural classification of accelerometer data. J. Exp. Mar. Bio. Ecol. 512, 22–30 (2019).
Google Scholar 
Glass, T. W., Breed, G. A., Robards, M. D., Williams, C. T. & Kielland, K. Accounting for unknown behaviors of free-living animals in accelerometer-based classification models: Demonstration on a wide-ranging mesopredator. Ecol. Inf. 60, 101152 (2020).
Google Scholar 
Wang, Y. et al. Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements. Mov. Ecol. 3, 2 (2015).
Google Scholar 
Chakravarty, P., Cozzi, G., Ozgul, A. & Aminian, K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13172 (2019).Article 

Google Scholar 
Clarke, T. M. et al. Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish. Mov. Ecol. 9, 26 (2021).
Google Scholar 
Zhang, J., O’Reilly, K. M., Perry, G. L. W., Taylor, G. A. & Dennis, T. E. Extending the functionality of behavioural change-point analysis with k-Means clustering: a case study with the little penguin (Eudyptula minor). PLoS ONE 10, e0122811 (2015).
Google Scholar 
Korpela, J. et al. Machine learning enables improved runtime and precision for bio-loggers on seabirds. Commun. Biol. 3, 633 (2020).
Google Scholar 
Jeantet, L. et al. Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. R. Soc. Open Sci. 7, 200139 (2020).ADS 

Google Scholar 
Wang, G. Machine learning for inferring animal behavior from location and movement data. Ecol. Inf. 49, 69–76 (2019).
Google Scholar 
Dunford, C. E. et al. Surviving in steep terrain: a lab-to-field assessment of locomotor costs for wild mountain lions (Puma concolor). Mov. Ecol. 8, 34 (2020).
Google Scholar 
Jeanniard-du-Dot, T., Guinet, C., Arnould, J. P. Y., Speakman, J. R. & Trites, A. W. Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets. Funct. Ecol. 31, 377–386 (2017).
Google Scholar 
Hicks, O. et al. Acceleration predicts energy expenditure in a fat, flightless, diving bird. Sci. Rep. 10, 21493 (2020).ADS 
CAS 

Google Scholar 
Dentinger, J. E. et al. A probabilistic framework for behavioral identification from animal-borne accelerometers. Ecol. Model. 464, 109818 (2022).
Google Scholar 
Chakravarty, P., Maalberg, M., Cozzi, G., Ozgul, A. & Aminian, K. Behavioural compass: animal behaviour recognition using magnetometers. Mov. Ecol. 7, 28 (2019).
Google Scholar 
Hammond, T. T., Palme, R. & Lacey, E. A. Ecological specialization, variability in activity patterns and response to environmental change. Biol. Lett. 14, 20180115 (2018).
Google Scholar 
Lynch, H. J. & LaRue, M. A. First global census of the Adélie Penguin. Auk 131, 457–466 (2014).
Google Scholar 
Riaz, J., Bestley, S., Wotherspoon, S., Freyer, J. & Emmerson, L. From trips to bouts to dives: temporal patterns in the diving behaviour of chick-rearing Adélie penguins East Antarctica. Mar. Ecol. Prog. Ser. 654, 177–194 (2020).ADS 

Google Scholar 
Cherel, Y. Isotopic niches of emperor and Adélie penguins in Adélie Land, Antarctica. Mar. Biol. 154, 813–821 (2008).
Google Scholar 
Little Penguin (Eudyptula minor) – BirdLife species factsheet. at Carroll, G., Harcourt, R., Pitcher, B. J., Slip, D. & Jonsen, I. Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird. Proc. Biol. Sci. 285, 20180788 (2018).
Google Scholar 
Meyer, X. et al. Oceanic thermal structure mediates dive sequences in a foraging seabird. Ecol. Evol. 10, 6610–6622 (2020).
Google Scholar 
Cavallo, C. et al. Quantifying prey availability using the foraging plasticity of a marine predator, the little penguin. Funct. Ecol. https://doi.org/10.1111/1365-2435.13605 (2020).Article 

Google Scholar 
Ropert-Coudert, Y., Chiaradia, A. & Kato, A. An exceptionally deep dive by a Little Penguin Eudyptula minor. Mar. Ornithol 34, 71–74 (2006).
Google Scholar 
Ropert-Coudert, Y., Kato, A., Wilson, R. P. & Cannell, B. Foraging strategies and prey encounter rate of free-ranging Little Penguins. Mar. Biol. 149, 139–148 (2006).
Google Scholar 
Rodríguez, A., Chiaradia, A., Wasiak, P., Renwick, L. & Dann, P. Waddling on the dark side: ambient light affects attendance behavior of little penguins. J. Biol. Rhythms 31, 194–204 (2016).
Google Scholar 
Ropert-Coudert, Y. et al. Happy feet in a hostile world? the future of penguins depends on proactive management of current and expected threats. Front. Mar. Sci. 6, 248 (2019).
Google Scholar 
Shuert, C. R., Pomeroy, P. P. & Twiss, S. D. Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal. Anim. Biotelemetry 6, 14 (2018).
Google Scholar 
Dickinson, E. R. et al. Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids. Mov. Ecol. 9, 28 (2021).
Google Scholar 
Conway, A. M., Durbach, I. N., McInnes, A. & Harris, R. N. Frame-by-frame annotation of video recordings using deep neural networks. Ecosphere 12, e03384 (2021).
Google Scholar 
Ravindran, S. Five ways deep learning has transformed image analysis. Nature 609, 864–866 (2022).ADS 
CAS 

Google Scholar 
Del Caño, M. et al. Fine-scale body and head movements allow to determine prey capture events in the Magellanic Penguin (Spheniscus magellanicus). Mar. Biol. 168, 84 (2021).
Google Scholar 
Johnson, J. M. & Khoshgoftaar, T. M. Survey on deep learning with class imbalance. J. Big Data 6, 27 (2019).
Google Scholar 
Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).
Google Scholar 
Sánchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).ADS 

Google Scholar 
Patrick, S. C., Martin, J. G. A., Ummenhofer, C. C., Corbeau, A. & Weimerskirch, H. Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Glob. Change Biol. https://doi.org/10.1111/gcb.15735 (2021).Article 

Google Scholar 
Bonar, M. et al. Geometry of the ideal free distribution: individual behavioural variation and annual reproductive success in aggregations of a social ungulate. Ecol. Lett. 23, 1360–1369 (2020).
Google Scholar 
Michelot, C., Kato, A., Raclot, T. & Ropert-Coudert, Y. Adélie penguins foraging consistency and site fidelity are conditioned by breeding status and environmental conditions. PLoS ONE 16, e0244298 (2021).CAS 

Google Scholar 
Mahoney, P. J. et al. Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties. Ecol. Appl. 28, 1715–1729 (2018).
Google Scholar 
Watanabe, Y. Y., Ito, K., Kokubun, N. & Takahashi, A. Foraging behavior links sea ice to breeding success in Antarctic penguins. Sci. Adv. 6, eaba4828 (2020).ADS 

Google Scholar 
Lescroël, A. et al. Working less to gain more: when breeding quality relates to foraging efficiency. Ecology 91, 2044–2055 (2010).
Google Scholar 
Zimmer, I., Ropert-Coudert, Y., Kato, A., Ancel, A. & Chiaradia, A. Does foraging performance change with age in female little penguins (Eudyptula minor)?. PLoS ONE 6, e16098 (2011).ADS 
CAS 

Google Scholar 
Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
Google Scholar 
Dickinson, E. R., Stephens, P. A., Marks, N. J., Wilson, R. P. & Scantlebury, D. M. Best practice for collar deployment of tri-axial accelerometers on a terrestrial quadruped to provide accurate measurement of body acceleration. Anim. Biotelemetry 8, 9 (2020).
Google Scholar 
Garde, B. et al. Ecological inference using data from accelerometers needs careful protocols. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13804 (2022).Article 

Google Scholar 
Watanabe, Y. Y., Ito, M. & Takahashi, A. Testing optimal foraging theory in a penguin-krill system. Proc. Biol. Sci. 281, 20132376 (2014).
Google Scholar 
Grémillet, D. et al. Energetic fitness: Field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics. Funct. Ecol. 32, 1203–1213 (2018).
Google Scholar 
Chimienti, M. et al. Quantifying behavior and life-history events of an Arctic ungulate from year-long continuous accelerometer data. Ecosphere 12, e03565 (2021).
Google Scholar 
Sutton, G. J., Botha, J. A., Speakman, J. R. & Arnould, J. P. Y. Validating accelerometry-derived proxies of energy expenditure using the doubly-labelled water method in the smallest penguin species. Biol. Open 10, bio055475 (2021).
Google Scholar 
Pagano, A. M. & Williams, T. M. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol. Evol. 9, 4210–4219 (2019).
Google Scholar 
Ballance, L. T., Ainley, D. G., Ballard, G. & Barton, K. An energetic correlate between colony size and foraging effort in seabirds, an example of the Adélie penguin Pygoscelis adeliae. J. Avian Biol. 40, 279–288 (2009).
Google Scholar 
Wilson, R. P. et al. Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl. Soc. Bull. 25, 101–106 (1997).
Google Scholar 
Shepard, E. L. C. et al. Identification of animal movement patterns using tri-axial accelerometry. Endanger. Species Res. 10, 47–60 (2008).ADS 

Google Scholar 
Kato, A., Ropert-Coudert, Y., Grémillet, D. & Cannell, B. Locomotion and foraging strategy in foot-propelled and wing-propelled shallow-diving seabirds. Mar. Ecol. Prog. Ser. 308, 293–301 (2006).ADS 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://url.org/www.R-project.org/ (2021).
Ainley, D. The Adélie Penguin: Bellwether of Climate Change (New York: Columbia University Press) (2006).Langrognet, F. et al. Rmixmod: Classification with Mixture Modelling. (2020).Bishop, C. M. Pattern Recognition and Machine Learning. Springer Science+Business Media, LLC, New
York, NY. (2006).Amélineau, F. et al. Intra- and inter-individual changes in little penguin diving and isotopic composition over the breeding season. Mar. Biol. 168, 62 (2021).
Google Scholar 
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 
Kuhn, M. & Wickham, H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org (2020).Wright, M. N. & Ziegler, A. Ranger : A fast implementation of random forests for high dimensional data in C++ andR. J. Stat. Softw. 77, 1–17 (2017).
Google Scholar  More

Williams, G. C. Natural selection, the costs of reproduction, and a refinement of lack’s principle. Am. Nat. 100, 687–690 (1966).
Google Scholar 
Stearns, S. C. The evolution of life histories. (Oxford University Press, 1992).Kirkwood, T. B. L. Evolution of ageing. Nature 270, 301–304 (1977).ADS 
CAS 
PubMed 

Google Scholar 
Partridge, L., Prowse, N. & Pignatelli, P. Another set of responses and correlated responses to selection on age at reproduction in Drosophila melanogaster. Proc. R. Soc. B. 266, 255–261 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
Metcalfe, N. Growth versus lifespan: Perspectives from evolutionary ecology. Exp. Gerontol. 38, 935–940 (2003).PubMed 

Google Scholar 
Lee, W.-S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate–lifespan trade-off. Proc. R. Soc. B. 280, 20122370 (2013).PubMed 
PubMed Central 

Google Scholar 
Lemaître, J.-F. et al. Early-late life trade-offs and the evolution of ageing in the wild. Proc. R. Soc. B. 282, 20150209 (2015).PubMed 
PubMed Central 

Google Scholar 
Jehan, C., Sabarly, C., Rigaud, T. & Moret, Y. Late-life reproduction in an insect: Terminal investment, reproductive restraint or senescence. J. Anim. Ecol. 90, 282–297 (2021).PubMed 

Google Scholar 
Pawelec, G. Age and immunity: What is “immunosenescence”?. Exp. Gerontol. 105, 4–9 (2018).CAS 
PubMed 

Google Scholar 
Schwenke, R. A., Lazzaro, B. P. & Wolfner, M. F. Reproduction–immunity trade-offs in insects. Annu. Rev. Entomol. 61, 239–256 (2016).CAS 
PubMed 

Google Scholar 
Maklakov, A. A. & Chapman, T. Evolution of ageing as a tangle of trade-offs: Energy versus function. Proc. R. Soc. B. 286, 20191604 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hamel, S. et al. Fitness costs of reproduction depend on life speed: empirical evidence from mammalian populations: Fitness costs of reproduction in mammals. Ecol. Lett. 13, 915–935 (2010).PubMed 

Google Scholar 
Graham, A. L., Allen, J. E. & Read, A. F. Evolutionary causes and consequences of immunopathology. Annu. Rev. Ecol. Evol. Syst. 36, 373–397 (2005).
Google Scholar 
Sorci, G. & Faivre, B. Inflammation and oxidative stress in vertebrate host–parasite systems. Phil. Trans. R. Soc. B. 364, 71–83 (2009).PubMed 

Google Scholar 
Ashley, N. T., Weil, Z. M. & Nelson, R. J. Inflammation: Mechanisms, costs, and natural variation. Annu. Rev. Ecol. Evol. Syst. 43, 385–406 (2012).
Google Scholar 
Babin, A., Moreau, J. & Moret, Y. Storage of carotenoids in crustaceans as an adaptation to modulate immunopathology and optimize immunological and life history strategies. BioEssays 41, 1800254 (2019).
Google Scholar 
Vasto, S. et al. Inflammatory networks in ageing, age-related diseases and longevity. Mech. Ageing Dev. 128, 83–91 (2007).CAS 
PubMed 

Google Scholar 
Finch, C. E. & Crimmins, E. M. Inflammatory exposure and historical changes in human life-spans. Science 305, 1736–1739 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Licastro, F. et al. Innate immunity and inflammation in ageing: A key for understanding age-related diseases. Immun. Ageing 2, 8 (2005).PubMed 
PubMed Central 

Google Scholar 
Pawelec, G., Goldeck, D. & Derhovanessian, E. Inflammation, ageing and chronic disease. Curr. Opin. Immunol. 29, 23–28 (2014).CAS 
PubMed 

Google Scholar 
Pursall, E. R. & Rolff, J. Immune responses accelerate ageing: Proof-of-principle in an insect model. PLoS ONE 6, e19972 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan, I., Agashe, D. & Rolff, J. Early-life inflammation, immune response and ageing. Proc. R. Soc. B. 284, 20170125 (2017).PubMed 
PubMed Central 

Google Scholar 
Vigneron, A., Jehan, C., Rigaud, T. & Moret, Y. Immune defenses of a beneficial pest: The mealworm beetle, Tenebrio molitor. Front. Physiol. 10, 138 (2019).PubMed 
PubMed Central 

Google Scholar 
Jehan, C., Chogne, M., Rigaud, T. & Moret, Y. Sex-specific patterns of senescence in artificial insect populations varying in sex-ratio to manipulate reproductive effort. BMC Evol. Biol. 20, 18 (2020).PubMed 
PubMed Central 

Google Scholar 
Jehan, C., Sabarly, C., Rigaud, T. & Moret, Y. Age-specific fecundity under pathogenic threat in an insect: Terminal investment versus reproductive restraint. J. Anim. Ecol. 91, 101–111 (2022).PubMed 

Google Scholar 
Chung, K.-H. & Moon, M.-J. Fine structure of the hemopoietic tissues in the mealworm beetle, Tenebrio molitor. Entomol. Res. 34, 131–138 (2004).
Google Scholar 
Urbański, A., Adamski, Z. & Rosiński, G. Developmental changes in haemocyte morphology in response to Staphylococcus aureus and latex beads in the beetle Tenebrio molitor L.. Micron 104, 8–20 (2018).PubMed 

Google Scholar 
Vommaro, M. L., Kurtz, J. & Giglio, A. Morphological characterisation of haemocytes in the mealworm beetle Tenebrio molitor (Coleoptera, Tenebrionidae). Insects 12, 423 (2021).PubMed 
PubMed Central 

Google Scholar 
Söderhäll, K. & Cerenius, L. Role of the prophenoloxidase-activating system in invertebrate immunity. Curr. Opin. Immunol. 10, 23–28 (1998).PubMed 

Google Scholar 
Siva-Jothy, M. T., Moret, Y. & Rolff, J. Insect immunity: an evolutionary ecology perspective. in Advances in Insect Physiology vol. 32 1–48 (Elsevier, 2005).Nappi, A. J. & Ottaviani, E. Cytotoxicity and cytotoxic molecules in invertebrates. BioEssays 22, 469–480 (2000).CAS 
PubMed 

Google Scholar 
Sadd, B. M. & Siva-Jothy, M. T. Self-harm caused by an insect’s innate immunity. Proc. R. Soc. B. 273, 2571–2574 (2006).PubMed 
PubMed Central 

Google Scholar 
Daukšte, J., Kivleniece, I., Krama, T., Rantala, M. J. & Krams, I. Senescence in immune priming and attractiveness in a beetle: Immunosenescence in a beetle. J. Evol. Biol. 25, 1298–1304 (2012).PubMed 

Google Scholar 
Krams, I. et al. Trade-off between cellular immunity and life span in mealworm beetles Tenebrio molitor. Curr. Zool. 59, 340–346 (2013).
Google Scholar 
Moon, H. J., Lee, S. Y., Kurata, S., Natori, S. & Lee, B. L. Purification and molecular cloning of cDNA for an inducible antibacterial protein from larvae of the coleopteran, Tenebrio molitor. J. Biochem. 116, 53–58 (1994).CAS 
PubMed 

Google Scholar 
Lee, Y. J. et al. Structure and expression of the tenecin 3 gene in Tenebrio molitor. Biochem. Biophys. Res. Comm. 218, 6–11 (1996).CAS 
PubMed 

Google Scholar 
Kim, D. H. et al. Bacterial expression of tenecin 3, an insect antifungal protein isolated from Tenebrio molitor, and its efficient purification. Mol. Cells 8, 786–789 (1998).CAS 
PubMed 

Google Scholar 
Roh, K.-B. et al. Proteolytic cascade for the activation of the insect toll pathway induced by the fungal cell wall component. J. Biol. Chem. 284, 19474–19481 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Park, J.-W. et al. Beetle Immunity. in Invertebrate Immunity (ed. Söderhäll, K.) vol. 708 163–180 (Springer US, 2010).Chae, J.-H. et al. Purification and characterization of tenecin 4, a new anti-Gram-negative bacterial peptide, from the beetle Tenebrio molitor. Dev. Comp. Immunol. 36, 540–546 (2012).CAS 
PubMed 

Google Scholar 
Haine, E. R., Pollitt, L. C., Moret, Y., Siva-Jothy, M. T. & Rolff, J. Temporal patterns in immune responses to a range of microbial insults (Tenebrio molitor). J. Insect Physiol. 54, 1090–1097 (2008).CAS 
PubMed 

Google Scholar 
Dhinaut, J., Chogne, M. & Moret, Y. Immune priming specificity within and across generations reveals the range of pathogens affecting evolution of immunity in an insect. J. Anim. Ecol. 87, 448–463 (2018).PubMed 

Google Scholar 
Hoffmann, J. A., Reichhart, J.-M. & Hetru, C. Innate immunity in higher insects. Curr. Opin. Immunol. 8, 8–13 (1996).CAS 
PubMed 

Google Scholar 
Moret, Y. Explaining variable costs of the immune response: selection for specific versus non-specific immunity and facultative life history change. Oikos 102, 213–216 (2003).
Google Scholar 
Khan, I., Prakash, A. & Agashe, D. Immunosenescence and the ability to survive bacterial infection in the red flour beetle Tribolium castaneum. J. Anim. Ecol. 85, 291–301 (2016).PubMed 

Google Scholar 
Rolff, J. Effects of age and gender on immune function of dragonflies (Odonata, Lestidae) from a wild population. Can. J. Zool. 79, 2176–2180 (2001).
Google Scholar 
Doums, C., Moret, Y., Benelli, E. & Schmid-Hempel, P. Senescence of immune defence in Bombus workers. Ecol. Entomol. 27, 138–144 (2002).
Google Scholar 
Schmid, M. R., Brockmann, A., Pirk, C. W. W., Stanley, D. W. & Tautz, J. Adult honeybees (Apis mellifera L.) abandon hemocytic, but not phenoloxidase-based immunity. J. Insect Physiol. 54, 439–444 (2008).CAS 
PubMed 

Google Scholar 
Moret, Y. & Schmid-Hempel, P. Immune responses of bumblebee workers as a function of individual and colony age: senescence versus plastic adjustment of the immune function. Oikos 118, 371–378 (2009).
Google Scholar 
Armitage, S. A. O. & Boomsma, J. J. The effects of age and social interactions on innate immunity in a leaf-cutting ant. J. Insect Physiol. 56, 780–787 (2010).CAS 
PubMed 

Google Scholar 
Korner, P. & Schmid-Hempel, P. In vivo dynamics of an immune response in the bumble bee Bombus terrestris. J. Invert. Pathol. 87, 59–66 (2004).CAS 

Google Scholar 
Li, T., Yan, D., Wang, X., Zhang, L. & Chen, P. Hemocyte changes during immune melanization in Bombyx Mori infected with Escherichia coli. Insects 10, 301 (2019).PubMed Central 

Google Scholar 
Chase, M. R., Raina, K., Bruno, J. & Sugumaran, M. Purification, characterization and molecular cloning of prophenoloxidases from Sarcophaga bullata. Insect Biochem. Mol. Biol. 30, 953–967 (2000).CAS 
PubMed 

Google Scholar 
Kanost, M. R. & Gorman, M. J. Phenoloxidases in insect immunity. in Insect Immunology 69–96 (Elsevier, 2008).Sadd, B. M. et al. Modulation of sexual signalling by immune challenged male mealworm beetles (Tenebrio molitor L.): Evidence for terminal investment and dishonesty. J. Evol. Biol. 19, 321–325 (2006).CAS 
PubMed 

Google Scholar 
Gálvez, D. & Chapuisat, M. Immune priming and pathogen resistance in ant queens. Ecol. Evol. 4, 1761–1767 (2014).PubMed 
PubMed Central 

Google Scholar 
Armitage, S. A. O. & Siva-Jothy, M. T. Immune function responds to selection for cuticular colour in Tenebrio molitor. Heredity 94, 650–656 (2005).CAS 
PubMed 

Google Scholar 
Armitage, S. A. O., Thompson, J. J. W., Rolff, J. & Siva-Jothy, M. T. Examining costs of induced and constitutive immune investment in Tenebrio molitor. J. Evol. Biol. 16, 1038–1044 (2003).CAS 
PubMed 

Google Scholar 
Kokoza, V. A. et al. Transcriptional regulation of the mosquito vitellogenin gene via a blood meal-triggered cascade. Gene 274, 47–65 (2001).CAS 
PubMed 

Google Scholar 
Isaac, P. G. & Bownes, M. Ovarian and fat-body vitellogenin synthesis in Drosophila melanogaster. Europ. J. Biochem. 123, 527–534 (2005).
Google Scholar 
Hoffmann, J. A. The immune response of Drosophila. Nature 426, 33–38 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Tzou, P. et al. Tissue-specific inducible expression of antimicrobial peptide genes in Drosophila surface epithelia. Immunity 13, 737–748 (2000).CAS 
PubMed 

Google Scholar 
Haine, E. R., Moret, Y., Siva-Jothy, M. T. & Rolff, J. Antimicrobial defense and persistent infection in insects. Science 322, 1257–1259 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Moret, Y. & Siva-Jothy, M. T. Adaptive innate immunity? Responsive-mode prophylaxis in the mealworm beetle, Tenebrio molitor. Proc. R. Soc. B. 270, 2475–2480 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Du Rand, N. & Laing, M. D. Determination of insecticidal toxicity of three species of entomopathogenic spore-forming bacterial isolates against Tenebrio molitor L. (Coleoptera: Tenebrionidae). Afr. J. Microbiol. Res. 5, 2222–2228 (2011).
Google Scholar 
Jurat-Fuentes, J. L. & Jackson, T. Bacterial entomopathogens. In Insect Pathology 2nd edn (eds Kaya, H. & Vera, F.) 265–349 (Elsevier Academic Press, Cambridge, Mass, 2012).
Google Scholar 
Dhinaut, J., Balourdet, A., Teixeira, M., Chogne, M. & Moret, Y. A dietary carotenoid reduces immunopathology and enhances longevity through an immune depressive effect in an insect model. Sci. Rep. 7, 12429 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Moreau, J., Martinaud, G., Troussard, J.-P., Zanchi, C. & Moret, Y. Trans-generational immune priming is constrained by the maternal immune response in an insect. Oikos 121, 1828–1832 (2012).
Google Scholar 
Lee, H. S. et al. The pro-phenoloxidase of coleopteran insect, Tenebrio molitor, larvae was activated during cell clump/cell adhesion of insect cellular defense reactions. FEBS Lett. 444, 255–259 (1999).CAS 
PubMed 

Google Scholar 
Zanchi, C., Troussard, J.-P., Martinaud, G., Moreau, J. & Moret, Y. Differential expression and costs between maternally and paternally derived immune priming for offspring in an insect. J. Anim. Ecol. 80, 1174–1183 (2011).PubMed 

Google Scholar 
Moret, Y. ‘Trans-generational immune priming’: Specific enhancement of the antimicrobial immune response in the mealworm beetle, Tenebrio molitor. Proc. R. Soc. B. 273, 1399–1405 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dubuffet, A. et al. Trans-generational immune priming protects the eggs only against gram-positive bacteria in the mealworm beetle. PLoS Pathog. 11, e1005178 (2015).PubMed 
PubMed Central 

Google Scholar  More

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) last month released its annual report on elephant poaching. It reveals a downward trend across African range states, based on data from its Monitoring the Illegal Killing of Elephants programme. The decline correlates with reduced ivory trading over the period, particularly in the Chinese market.
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Bacterial density and growth potential in the rearing water were related to the microbial carrying capacityQuantifying the bacterial density in each tank verified that we obtained a higher bacterial load in the systems with added organic material. The bacterial density was, on average, 7.8× higher in the systems with high compared to low bacterial carrying capacity. This difference was particularly evident at 2 (34.8×, Kruskal–Wallis p = 0.0008) and 9 DPH (9.1×, Kruskal–Wallis p = 0.0007) (Fig. 1). The bacterial density increased throughout the experiment for the tanks with low microbial carrying capacity (treatment group MMS−, FTS−), reflecting increased larval feeding and defecation. Contrastingly, the bacterial density was relatively stable over time in the MMS+ treatment and even decreased over time in the FTS+ treatment. When averaging the densities at 11 and 15 DPH within each rearing treatment, we observed that the ‘MMS+ to FTS+’ had a considerable difference in the bacterial density between incoming and rearing water (24.2×). In contrast, this difference was below 8.2× in all other treatment tanks. Such differences in density indicated that some communities were below the microbial carrying capacity of the systems. We thus investigated the growth potential to determine if carrying capacity was reached in the rearing water.Figure 1Bacterial density (million bacterial cells mL−1) at various days post-hatching (DPH) in incoming and rearing tank water. Note that the y-axis is log scaled. Colours indicate the rearing treatment, and shape signifies rearing (filled circle) and incoming water (filled triangle).Full size imageThe bacterial net growth potential in the intake and rearing water was quantified as the number of cell doublings after incubation for 3 days11. Generally, the FTS− and MMS− rearing water had net growth potential with an average of 0.2 and 0.1, respectively (Supplementary Fig. 2). In contrast, the rearing water of the FTS+ and MMS+ had a negative net growth potential with averages of −0.2 and −0.06, respectively. In the case of negative net growth potential, the bacterial density decreased during the incubation. A negative net growth potential suggested that the rearing water bacterial communities were at the tank’s microbial carrying capacity at the time of sampling. Thus, the bacterial communities were at the carrying capacity of the high (+) carrying capacity systems and below in the low (−) systems. To gain a deeper understanding of the bacterial community characteristics the 16S rRNA gene of the bacterial community was sequenced at 1 and 9 DPH.Initial rearing condition did not leave a legacy effect on bacterial α-diversityThe bacterial α-diversity of the rearing water was investigated at 1 and 12 DPH (Fig. 2). At 1 DPH, the richness was comparable between the FTS−, FTS+ and MMS+ treatments, but on average, 1.5× higher for the MMS− treatment (307 vs 205 ASVs, Tukey’s test p  More