Philippa Kaur

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It’s surprisingly hard to catch a sloth. Although they’re slow — very, very slow — if you climb a tree to catch one, it will move along to the next tree. Once you climb the new tree, it will move back again.My team does this regularly, as we conduct the Sloth Backpack Project, a data-logging initiative here in Costa Rica, where many sloths coexist with people. In 2017, I wanted to do more than research, so I started the Sloth Conservation Foundation.In this photograph, I’m fitting a backpack to a brown-throated three-fingered sloth (Bradypus variegatus) that we named Baguette, after a nearby bakery. The backpack will collect data on her location, movement and living patterns.We had found Baguette about 20 minutes earlier, balancing atop construction fencing as she attempted to escape two pit bulls. Baguette wasn’t all that grateful. She’s a feisty old girl. She’s old: she’s missing fingers, and she’s got scars on her face.I adore sloths, but I also envy them. They’re a powerful symbol of the slowness that our society needs more of. They don’t let anything stress them out unless it’s really important — they just get on with life.The backpack project will help us to understand sloth behaviour, so we can better protect them as the urban environment grows. This year, I received a €50,000 (US$52,220) Future For Nature award, which we will use to train a dog to detect sloth faeces. We can use faeces as a proxy for sloth numbers and locations in the region, and ultimately work out the boundaries of the species, how fast populations are declining and which conservation measures work.I’m happy I’ve moved away from academia — I can put all my energy into conservation as opposed to bashing out papers. That’s what I feel ecology should focus on — how we can use what we’re learning to give back to other species. More

For a schematic overview of the experimental design, see Fig. 2.Experimental evolutionProtocolThe stock population (Stock population below) was allowed to expand for one generation and from this we established eight replicate experimental evolution populations, four selected for fighter morphs (F-lines) and four selected for scrambler morphs (S-lines). Each population was founded by 1,000 recently eclosed adults (500 random females and 500 random males of the desired morph). The classification of the morphs was based on visual inspection using a stereoscopic microscope and was unambiguous due to the discontinuous distribution of the phenotypes (Classifying male morphs below). Adults were allowed to interact freely for 6 days, all surviving adults (with previously laid eggs discarded) were transferred to a new container for 24 h of egg laying, after which adults were removed. The resulting offspring were allowed to mature over 13 days and 1,000 individuals from the newly eclosed adults selected for founding the following generation, again 500 random females and 500 random males of the desired morph, with this protocol repeated every generation (Extended Data Fig. 1). The isolation of nymphs to use virgins was unfeasible with our experimental design and population sizes. However, the period of 6 days after selecting the founders of the next generation and collecting eggs for the next generation was probably enough to displace most sperm stored by females mated with any unselected males due to the high number of remating that will be occurring over this duration (females on average remate after 80 min, ref. 88) and last male sperm precedence89. The timing of generation was chosen to reflect maturation rates from our stock population to avoid indirect selection on this trait. Moreover, a previous study90 showed there was no difference between male morphs in maturation rates and that over similar lengths of time to the protocol here the fertility of both morphs remains similar. Therefore, our protocol was not likely to impose strong differential selection on morph life histories.Tracking morph proportionWe assayed the proportion of male morph in each population every 6–7 generations, by isolating 200 larvae (ten per vial) from the container, allowing maturation within vials and recording the morph of all males that eclosed (mean n = 86 per population, per generation, range 71–109). Our selection protocol was highly effective in driving an increase in the frequency of the desired male morph to >90% after 20 generations in both treatments, with this effect considerably faster within F-lines indicted by a significant two-way interaction between proportion of the desired morph and generation (χ2 = 39.9, d.f. = 6, P 90% is probably a consequence of a longer interaction period (3 versus 6 days) in which the stored sperm of males before selection was able to be displaced and/or because selection was acting more efficiently in our larger populations. The difference between rate of changes in morph proportion between F- and S-lines in the current study, and also found by Plesnar-Bielak et al.39, may be associated with the genetic architecture of morph expression. Alternatively, selection could be less effective in scrambler lines if they are less efficient than fighters in displacing sperm of previous females’ partners, but this is unlikely as R. robini male morphs have previously been demonstrated to not differ in their sperm competiveness89.Stock populationWe established a stock population by mixing three laboratory populations that were collected from three sites in Poland (Krakόw, collected in 1998 and 2008, Kwiejce, collected in 2017 and Mosina, collected in 2017; Extended Data Fig. 1), where the line derived for material used in creating the reference genome (below) was also established from the same collections at Mosina in 2017. All populations were maintained in cultures with several hundred individuals per generation before mixing and establishment of the stock population. The mixing of distinct populations increased the genetic variance in the stock population, which otherwise would probably have been limited due to founder events and the limited population size of each of the contributing populations73, thus decreasing our power to detect the effects of SSTs on genetic variation. The newly mixed stock population was maintained with several hundred individuals per generation for roughly 12 generations before the onset of this experiment. This time period is probably enough to break linkage disequilibria that could have arisen due to mixing (for unlinked loci, linkage disequilibrium should decay by half each generation91).One generation before establishing experimental evolution populations the proportion of male morphs was determined from 176 random males, indicating a roughly equal morph ratio (95 fighters, 81 scramblers) of the stock population (Extended Data Fig. 3).General housing and husbandryThe stock population and experimental evolution populations were maintained in plastic containers (approximate length, 9.5 cm; width, 7 cm; height, 4.5 cm), filled with roughly 1 cm of plaster-of-Paris. The same containers were used when sampling mites for sequencing for the reference genome or resequencing from experimental evolution populations, but either replaced the plaster-of-Paris with 5% agarose gel or added a thin layer of 5% agarose gel above the plaster-of-Paris, respectively. The agarose gel was used to reduce the number of contaminates within our samples and on the basis of preliminary extractions that indicated that small pieces of plaster-of-Paris may reduce the quality of DNA during extractions. Individuals, pairs and small groups of ten mites were housed in glass vials (approximate height, 2 cm; diameter, 0.8 cm) and large groups of 60 or 150 mites in plastic containers (approximate height, 1.5 cm; diameter, 2 cm diameter or height, 1.5 cm; diameter, 3.5 cm diameter, respectively) all with an approximate 1 cm base of plaster-of-Paris. All plaster-of-Paris bases were completely soaked in water before mites were transferred into them. All mites were reared at a constant 23 °C, at high humidity ( >90%) and were provided an excess of powdered yeast ad libitum.Classifying male morphsTo illustrate the discontinuous distribution of the weapon and to demonstrate that this classification based on visual inspection is non-subjective, we performed phenotypic measurements from male mites from a population collected near Krakόw, Poland, that had previously been fixed onto microscope slides for a separate study66. The measurements taken were idiosoma (body without mouthparts) length and width of third proximal segment of the third right leg (genu). Measurements were preformed using Lecia DM5500B microscope and Lecia Application Suite v.4.6.1. We then performed an analysis to, first, determine whether the allometric relationship between idiosoma length and width of third pair of legs is best described as discontinuous and, second, to verify that classification by simple visual inspection matches the same classification from allometric analysis. One researcher performed all the measurements and classified each male as a fighter (n = 50) or scrambler (n = 50), a separate researcher was then given the measurements but not the classification of the male morph.Broadly, guidelines for the analysis of non-linear allometries92 were followed. The log–log scatterplots of idiosoma length against leg width were visualized, which showed there was clear evidence for non-linear scaling relationships. Next histograms of idiosoma length, leg width and relative leg width (leg width/idiosoma length) were visualized (Extended Data Fig. 2a–c). Where a normal distribution of idiosoma length, and a binomial distribution in leg width and relative leg width are further indications of a discontinuous relationship. On the basis of the lowest point between the two peaks of the density plot of relative leg width (Extended Data Fig. 2c) males were classified as scramblers (relative leg width 0.125). Replotting the log–log scatterplot of idiosoma length and leg width, and using the classification of morph described above clearly demonstrates the discontinuous allometric relationship of idiosoma length and leg width in R. robini (Extended Data Fig. 2d). Moreover, on the basis of the Akaike information criterion (AIC), the discontinuous model where males were assigned a morph (AIC = 646.5) clearly has a substantially better fit than a simple linear and quadratic models (AIC = 918.5 and 920.2, respectively). Further models were omitted from comparison (for example, breakpoint or sigmoidal) due to the clear discontinuous allometry observed. Finally, all 100 males were assigned the same morph by visual inspection and blind allometric analysis, demonstrating that the former is effective and accurate in classifying male morph.Phenotypic assaysFecundity assays were performed using experimental evolution females at F20 and F32. Eggs laid by females between days 4–8 were counted, encompassing the window of time of most evolutionary relevance for female fitness during maintenance of selection lines (that is, egg laying period in selection lines was between days 6–7) and also likely to capture variation in lifetime fecundity that remains largely consistent throughout the first 3 weeks of life93. Nymphs were individually isolated to gain virgin females, which on maturation females from each experimental evolution population (n = 30) were paired with a male from the stock population (15 with fighters and 15 with scramblers). Pairs were transferred to a new vial on day 4, with the pair being removed from the second vial after a further 4 days and all eggs in the second vial counted. If the male had died in the first vial, they were replaced with a stock male of the same morph. Any female deaths in the first or second vials were recorded.Longevity assays were also performed at F20 and F32. At F20, females used in fecundity assays, including the stock male they were paired with (replaced if dead), were transferred to a new vial at day 8. After this point, vials were then checked every 2 days for female deaths and pairs were moved to new vials every 4 days. Males were replaced with stock males of the same morph if found dead. Similarly, at F20, on maturation males from experimental evolution populations (n = 30) were paired with stock females, vials were checked every 2 days and changed every 4 days, with females being replaced if dead. At F32, only female longevity was determined and was performed in groups; 30 experimental evolution females and 30 stock males (15 of each morph) were placed in plastic containers, two per experimental population. This logistically easier estimate of longevity was done due to local restrictions during the SARS-CoV-2 pandemic and the imposed limitations on people working closely together. Groups were checked for dead females every other day and all remaining live mites transferred to a new container every 4 days. When mites were transferred to a new container the sex and morph ratio were balanced to that of the remaining females, by either removing or adding males of the desired morph from the stock population.To determine whether the survival of mites differed between F- and S-lines when competition between males was allowed, at F45 we created small colonies from each population and survival of males and females recorded over 6 days, the same period as used between selecting founders of the next generation and subsequent egg laying period. Colonies were at a 50:50 sex ratio, established with 150 newly eclosed mites placed into small plastic containers. This was approximately the same density after selection of the next generations founders during the maintenance of experimental evolution populations (150 mites in roughly 9.5 cm2 = 16 mites per 1 cm2; 1,000 mites in roughly 67 cm2 = 15 mites per 1 cm2). After 3 days, all colonies were checked and any dead mites identified by sex. After another 3 days, again dead mites were recorded and all surviving mites sexed and counted.Additionally, at F45 we performed further fecundity assays to obtain estimates of inbreeding depression within experimental evolution populations. To establish family groups, larvae were isolated and on maturation F0 males and females (n = 16) from within the same experimental evolution population were paired together. Pairs were allowed to produce eggs for 48 h, after which adults were removed from vials. After hatching from each pair, 12 F1 larvae were isolated into new vials. On their maturation, these F1 mites were either paired with a full sibling, that is, from the same family, or with an individual from a different family but from the same experimental evolution population. When possible, we made two inbred and two outbred pairs with same family lines used. Again, pairs were allowed to produce eggs for 48 h before their removal for the vial. After a further 5 days, vials were checked for larvae, if larvae were present in the first vial six were individually isolated and the second vial discarded, if no larvae were present in the first vial the second vial was checked for larvae and, if present, they were isolated. This protocol therefore produced inbred and outbred individuals from within the same experimental evolution population. Which, as above, on maturation F2 inbred and outbred females were paired with stock males (fighter males only) and number of eggs laid between days 4 and 8 counted. Only a single female from each unique inbred or outbred family was used. Either due to pairs failing to produce offspring or there being no F2 females, samples sizes were not exactly equal. In total, 59 outbred and 55 inbred females from F-lines, and 56 outbred and 54 inbred females from S-lines were paired with stock males.Phenotypic assay statistical analysesAll phenotypic analysis was conducted using R statistical software94 (v.3.5.2) and data were visualized using ggplot2 (ref. 95).Analysis of male morph proportion was performed using a generalized linear mixed model with binomial error structure, fitted using lme4 (ref. 96). Where the proportion of desired morph was compared in model with morph selection and generation (as a factor) including their two-way interaction as explanatory variables, and population included as a random effect.All fecundity data were analysed using generalized linear mixed models with Poisson error structures, fitted using lme4. Due to the differences in stock population males used between F45 and earlier generations, and slightly different rearing conditions between females in the fecundity assays from generations F20 and F32, they were analysed separately from data collected in F45. However, we noted that the fecundity of females in Fig. 5a was comparable to the outbred females in Fig. 5b. Explanatory variables fitted to fecundity data from F20 and F32 were, morph selection treatment, generation, including their two-way interaction term, and stock male morph. The explanatory variables fitted to fecundity data from inbreeding depression data were, morph selection treatment and status of female (that is, inbred or outbred), including their two-way interaction term. In both analyses, we included population as a random effect and an observation level random effect to account for overdispersion, we omitted fitting random slopes due to issues with increasing the complexity of random effects close to reaching a singular fit. Females that died before the end of the fecundity assay and those that laid zero eggs were removed from analysis. This excluded five females from F20 (three F-line and two S-line), 20 from F32 (13 F-line and seven S-line) and 16 from F45 (three inbred and three outbred F-line, and nine inbred and one outbred S-line).Longevities of females at F20 and F32, and males at F20, were analysed separately using mixed effects Cox models, fitted using coxme97. In all analyses, we included a random effect of population, with morph selection treatment as an explanatory variable and extra variable of male morph included in female longevity analysis at F20. Survival of mites over 6 days at F45 was analysed using a GLM with counts of dead and surviving mites fitted with a quasibinomial error structure, the model included morph selection treatment and sex, including their interaction term, as explanatory variables. If individuals were lost due to handling error (that is, killed or escaped) they were right-censored during analysis.Genome assemblySample originA line of R. robini originated from a wild-collected population from the Mosina region (Wielkopolska, Poland). In October 2017, onions were collected from the field and approximately 200 individuals of R. robini were identified under dissecting microscope. The line used for DNA isolation in the genome sequencing project was developed from full sib × sib mating for 14 generations (to maximize homozygosity) following and continuing the protocol described in ref. 67.DNA extractionFor DNA extraction we used only mite eggs, that were laid by 500 females, collected in a container (see above for a description) Females were kept in this container for 3 days. After that time, they were removed, and eggs were filtered using fine sieves and washed for 1 min in 0.3% sodium hypochlorite solution and in Milli-Q water for 2 × 2 min to remove any potential foreign DNA contamination. These eggs were collected in 1.5 ml Eppendorf tube and after short centrifugation, the remains of the water (supernatant) removed with a pipette. The sample was immediately transferred to ice and prepared for DNA extraction. DNA was extracted using Bionano Prep Animal Tissue DNA Kit for HMW DNA isolation according to the manufacturer’s instructions. Briefly, eggs were smashed with a sterile pellet pestle on ice in 500 μl homogenization buffer; the sample was fixed with 500 μl cold ethanol and incubated 60 min on ice, after that time the sample was centrifuged at 1500g for 5 min at 4 °C and the supernatant was discarded. Next, after resuspension in a homogenization buffer pellet, this was cast in four agarose plugs as described in the original protocol. Agarose plugs were incubated with Proteinase K and Lysis buffer solution for 2 h with intermittent mixing. After that time, the digestion solution was replaced with a freshly made one and incubated overnight with intermittent mixing. According to the original protocol, after RNase A digestion and plug washing, DNA was recovered by incubation of the plugs in TE buffer, followed by plug melting and addition of agarase. Recovered DNA was dialysed and homogenized on a membrane for 45 min at room temperature and transferred to a clean tube with a wide bore tip.SequencingSequencing was done using Oxford Nanopore Technologies (ONT, MinION). Isolated DNA purified using AMPure XP beads and resuspended in H2O before library preparation. Two separate libraries were prepared using ligation sequencing kit, SQK-LSK109 and Rapid Sequencing Kit SQK-RAD004, respectively, according to the manufacturer’s protocols and were sequenced on a FLO-MIN106 R9.4.1 SpotON flow cell on a MinION Mk 1B sequencer (ONT). The total yields from sequencing were 484,700 reads (2,417,068,187 nt) with a read-N50 of 10,044 nt (ranging from 216,403 to 100). Base calling of the raw reads was done using Guppy (v.3.3) resulting in a total sum of the reads 7,979,616,172, equivalent to 26× coverage aiming for a genome of 300 megabases (Mb). The reads N50/N90 were estimated at 7,958/1,719.Assembling reference genomeReads aligning with the Mitochondrion genome were identified using BLASTN and filtered from the raw reads before assembling the genome. The remaining ONT reads were assembled using the Flye software (v.2.6), with –min-overlap 3,000 to increase stringency at the initial overlay step, and default parameters including five rounds of polishing through consensus, contigs were additionally polished two times with Medaka (v.0.11.2). Illumina paired-end RNA dataset is assembled using CLC Assembler (CLC Assembly Cell). Both RNA assemblies and paired-end 10X genomic dataset (unpublished data) were mapped onto the contigs using minimap2 (v.2.16) and BWA mapper (v.0.7.17), respectively, and the assembly was further polished using PILON (v.1.20) to error correct potential low-quality regions. The resulting assembly yielded a genome of 307 Mb, assembled into 1,533 contigs ranging from 10,840,357 to 100 basepairs (bp) and an assembly-N50 of 1.670 Mb. Moreover, the BUSCO completeness analysis using the Arachnida (odb10) reference set confirmed our assembly represents the complete genome C:94.8%(S:89.1%,D:5.7%),F:0.9%,M:4.3%,n:2934 (=arachnida_odb10), only missing 126 genes from the whole reference set. Knowing that BUSCO only gives a rough estimation, we remain confident that this assembly represents well the bulb mite genome.Flow-cytometryWhole individual R. robini were homogenized in 500 μl of ice-cold LB01 detergent buffer along with the head of a male Drosophila melanogaster (1 C = 0.18 pg) as an internal standard. The homogenized tissue was filtered through a 30-μm nylon filter. Then 12 μl of propidium iodide with 2 μl of RNase was added, and stained for 1 h on ice in the dark. All samples were run on an FC500 flow cytometer (Beckman-Coulter) using a 488-nm blue laser, providing output as single-parameter histograms showing relative fluorescence between the standard nuclei and the R. robini nuclei. Six replicate samples were run to account for variation in fluorescence outputs. The genome size of R. robini was estimated at 0.30 pg, or about 293 Mb, and consistent with estimation of size from the genome assembly described above.Mitochondrial genomeONT reads aligned with R. robini mitochondrion genome were de novo assembled with Flye (v.2.6) assembler and polished with Racon. Mitochondrion genome is assembled in one single contig with a size of 15,335 bases.Gene predictionOn the polished final genome, protein coding genes have been predicted. For this, AUGUSTUS was used including hints coming from R. robini RNA-sequencing (RNA-seq) (samples SRR3934324, SRR3934325, SRR3934326, SRR3934327, SRR3934328, SRR3934329, SRR3934330, SRR3934331, SRR3934332, SRR3934333, SRR3934335, SRR3934337, SRR3934338 and SRR3934339 from the PRJNA330592 BioProject deposited at the National Center for Biotechnology Information (NCBI) Short Read Archive) and proteins coming from highly curated Tetranychus urticae (v.2020-03-20) as well as proteins from the previous version of the unpublished, Illumina-sequenced R. robini genome (https://public-docs.crg.eu/rguigo/Data/fcamara/bulbmite.v4a/). The PE RNA-seq reads were mapped on the genome using HISAT2 (-k 1 —no-unal) and further processed with Regtools to extract junction hints and filtered for junctions with a minimum coverage of 10. All the RNA-seq reads were also assembled with CLC Assembly Cell (v.5.2.0) software, setting the word size for the Bruijn graph at 50 and maximum bubble size at 31. The reads were assembled into 689,563 contigs (ranging from 10,675 to 180 bp), which were later mapped on the genome with GenomeTheader to generate complementary DNA hints. Protein hints were generated by using with Exonerate (v.2.2) with Protein2Genome model. To reduce the amount of overprediction due to repeated elements (transposable elements, simple sequence repeats) we de novo predicted high abundant repeats using RepeatModeler. The accompanying parameter file for extrinsic data for AUGUSTUS was adapted to include these hints as well as the softmasking of the genomic sequence. The resulting gene predictions from AUGUSTUS were further curated with EvidenceModeler using the same extrinsic data. The BUSCO analysis confirmed that our gene prediction indeed captured the expected genes well (C:94.6%(S:86.3%,D:8.3%),F:0.4%,M:5.0%,n:2934 (=arachnida_odb10)). The final predicted gene set was subsequently processed to be uploaded into ORCAE (https://bioinformatics.psb.ugent.be/orcae/overview/Rhrob)98.ResequencingGenomic sampling and mappingFor genomic analyses we sampled material from each of the morph selection lines (n = 8) at F1, F12 and F29. Following the experimental evolution protocols, after the first 24 h of egg laying all adults were transferred to a new container (described above) for a second 24 h to lay eggs and from these second dishes genomic material was sampled. On maturation, adults were transferred to and kept for 3 days in containers. Adults were then randomly selected and placed into Digestion Solution for MagJET gDNA Kit (F1&12) or ATL buffer (F29) before freezing at −20 °C. From each population two samples were collected consisting of 100 individuals (1 × 100 females and 1 × 100 males of random morph), the two samples separated by sex were used as technical replicates. The tissue from the 100 individuals within each sample was homogenized and DNA was extracted by Proteinase K digestion (24 h) followed by standard procedures using MagJET Genomic DNA Kit (ThermoScientific, F1&12) or DNeasy Blood and Tissue (Qiagen, F29). DNA concentration was controlled with the Qubit double-stranded DNA HS Assay Kit and DNA quality was assessed on agarose gels. The library preparation was performed using NEBNext Ultra II FS DNA Library Prep kit for Illumina.Whole-genome resequencing was carried out by National Genomics Infrastructure (Uppsala, Sweden) using the Illumina Nova-Seq 6000 platform with S4 flow cell to produce 2 × 150 bp reads (average 160.7 × 106: range 130.7 × 106 − 189.9 × 106). Adaptors were trimmed from reads using Trimmomatic99 software (v.0.39) and unpaired reads discarded. Fastq files were mapped to the assembled genome with bwa mem100 (v.0.7.17-r1188) using default settings. Sam files were converted to bam files, sorted, duplicates marked and ambiguously mapped reads removed using samtools101 (v.1.9). On average, 90% (range, 86–93%) of the reads from each sample were mapped successfully, of which an average of 17% (range, 15–19%) were marked as duplicates. This left us with an average of 117.7 × 106 pair end reads per sample, ranging between 99.6 × 106 and 145.9 × 106 (Supplementary Table 1).Genomic analysisFile preparation and filteringPreparation of files used in genomic analysis was done as follows: bam files were converted to a pile-up file using samtools, following which indels and surrounding windows (5 bp either side) were filtered, using identify-genomic-indel-regions.pl and filter-pileup-by-gtf.pl in PoPoolation102 (v.1.2.2) to avoid false SNPs, with the resulting filtered pile-up file converted to a sync file using mpileup2sync.pl in PoPoolation2 (ref. 103) (v.1.201). Using custom python scripts, the distribution of coverage from each sample (single sex) was determined by recording the coverage of positions every 10 kb across the genome from the sync file to give information on expected coverage (Supplementary Fig. 1). On the basis of this, we filtered the sync and pile-up files to contain only regions within a range of informative coverage, where the mean coverage of all samples at every position was between 50% of the expected coverage and 200% of the expected coverage (56×, range 23−112×). The pile-up and sync files containing individual male and female samples (48 in total) were then merged by sex to give files containing allele frequencies from 24 samples (eight populations across three generations), each consisting of allele frequencies of 200 individuals (100 males and 100 females, above) and used in all subsequent analysis (unless stated otherwise). Similarly, we drew coverage of a position every 10 kb from each sample in the sex-merged sync file to determine a distribution from which we decided to subsample to (Supplementary Fig. 1). We putatively identified X-linked contigs (below) and excluded them autosomal analysis. A similar, but, separate analysis on genes and SNPs from X-linked contigs was performed by using different parameters (below).Estimating nucleotide diversityUsing PoPoolation we determined various estimates of genetic diversity per sample (that is, 24 sex-merged samples). The pile-up file from each sample was subsampled using subsample-pileup.pl to a coverage of 63× (max coverage, 252×) to standardize estimations of genetic diversity across the genome, between populations and across generations. First, nucleotide diversity (Tajima’s Pi, π) and number of segregating sites (Watterson’s theta, ϴ) were estimated within genes. We performed analysis of exons using Syn-nonsyn-at-position.pl, in which genetic diversity of synonymous and non-synonymous positions were determined. Further analysis of overall genetic diversity within exons and introns were performed using Variance-at-position.pl, Tajima’s D (D) also estimated in the former. We used a minimum count of three (equal to a minor allele frequency of roughly 5%) for a SNP to be called, and a phred score >30 and a pool size of 400. Further analysis using 10 kb sliding windows (step size 10 kb) across the genome were performed using Variance-sliding.pl, and also included estimation of D. Estimates of D require the minimum count to be 2, but otherwise all the same parameters were used.We filtered genes to be included in our analysis (and all subsequent analysis) on the basis of a number of criteria. On the basis of extensive RNA-seq data from both males and females (Plesnar-Bielak, unpublished data with NCBI accession number PRJNA796800), we only included genes in our analyses that were expressed at a mean level of fragments per kilobase of transcript per million mapped reads >1 across 72 samples originated from both sexes and both morphs rearing in three different temperatures (18, 23 and 28 °C). A further filtering step was performed to remove genes with inconsistent mapping between samples, only genes with >60% exons mapped to (calculated from positions used to calculate parameters in the Syn-nonsyn-at-position.pl π outputs), with 63−252× coverage, in all 24 samples were included in the analysis. The final dataset contained 13,389 autosomal genes and subsequently used to filter other datasets to retain this set of genes only (see Supplementary Table 8 for a list of genes). Similarly, windows were discarded from outputs if 60% of genes being mapped to in all 24 samples) and reducing the final X-linked dataset to contain fewer than 200 genes. We therefore opted to reduce the target coverage further to 40×, in an attempt to retain more genes. This slight reduction of target coverage increased the number of genes in the final dataset substantially to 587 genes. We therefore opted to use a minimum coverage of 40× in all analysis of X-linked SNPs, genes and windows.Diverging SNPsTo determine divergent SNPs between F- and S-lines, we extracted the allele frequencies of all samples from the sex combined sync file. Samples from F29 were then used to filter the entire dataset to only contain SNPs on the basis of a number of criteria. First, positions within all samples were required to have a coverage >63× and 5% (that is, the average of all samples but not necessarily above >5% in all samples). Thus, our dataset contained only positions with the target coverage in all F29 samples and in which polymorphisms were unlikely to be a consequence of sequencing errors. After this filtering we were left with roughly 6 million SNPs used in further analysis. We performed a GLM, at each position by comparing the count of the major allele against counts of minor alleles at F29, to determine consistent allele frequency changes between treatments70. If any population had minor or major allele count of 0, +1 was added to minor and major alleles from all samples. To correct for multiple testing, we converted P values to q values using the qvalues R package (v.2.14.1)104 and applied a FDR with a q 900,000), GLMs were performed (identical to above) on the simulated major and minor allele counts. Using a FDR with a q  More

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Historically, species distributions have been modeled independently from each other due to unavailability of multispecies datasets and computational restraints. However, ecological datasets that provide insights about collections of organisms have become prevalent over the last decade thanks to efforts like Long Term Ecological Research Network (LTER), National Ecological Observatory Network (NEON), and citizen science surveys1. In addition, technology has improved our ability to fit modern statistical models to these datasets that account for both species environmental preferences and interspecies dependence. These advancements have allowed for the development of joint species distribution models (JSDM)2,3,4 that can model dependence among species simultaneously with environmental drivers of occurrence and/or abundance.Species distributions are shaped by both interspecies dynamics and environmental preferences5,6,7,8. JSDMs integrate both sources of variability and adjust uncertainty to reflect that multiple confounded factors can contribute to similar patterns in species distributions. Some have proposed that JSDMs not only account for biotic interactions but also correct estimates of association between species distributions and environmental drivers3,9, while others claim JSDMs cannot disentangle the roles of interspecies dependence and environmental drivers5. We address why JSDMs can provide inference distinct from their concomitant independent SDMs, how certain parameterizations of a JSDM induce confounding between the environmental and random species effects, and when deconfounding these effects may be appealing for computation and interpretation.Because of the prevalence of occupancy data for biomonitoring in ecology, we focus our discussion of community confounding in JSDMs on occupancy models, although we also consider a JSDM for species density data in the simulation study. The individual species occupancy model was first formulated by MacKenzie et al.10 and has several joint species extensions4,11,12,13,14,15,16. We chose to investigate the impacts of community confounding on the probit model since it has been widely used in the analysis of occupancy data4,13,17. We also developed a joint species extension to the Royle-Nichols model18 and consider community confounding in that model.We use the probit and Royle-Nichols occupancy models to improve our understanding of montaine mammal communities in what follows. We show that including unstructured random species effects in either occupancy model induces confounding between the fixed environmental and random species effects. We demonstrate how to orthogonalize these effects in the model and compare the resulting inference compared to models where species are treated independently.Unlike previous approaches that have applied restricted regression techniques similar to ours, we use it in the context of well-known ecological models for species occupancy and intensity. While such approaches have been discussed in spatial statistics and environmental science, they have not been adopted in settings involving the multivariate analysis of community data. We draw parallels between restricted spatial regression and restricted JSDMs but also highlight where the methods differ in goals and outcomes. We find that the computational benefits conferred by performing restricted spatial regression also hold for some joint species distribution models.Royle-Nichols joint species distribution modelWe present a JSDM extension to the Royle-Nichols model18. The Royle-Nichols model accounts for heterogeneity in detection induced by the species’ latent intensity, a surrogate related to true species abundance. Abundance, density, and occupancy estimation often requires an explicit spatial region that is closed to emmigration and immigration. In our model, the unobservable intensity variable helps us explain heterogeneity in the frequencies we observe a species at different sites without making assumptions about population closure. In the “Model” section, we further discuss the distinctions between abundance and intensity in the Royle-Nichols model.The Royle-Nichols model utilizes occupancy survey data but provides inference distinct from the basic occupancy model10. In the Royle-Nichols model, we estimate individual detection probability for homogeneous members of the population, whereas in an occupancy model, we estimate probability of observing at least one member of the population given that the site is occupied. Furthermore, the Royle-Nichols model allows us to relate environmental covariates to the latent intensity associated with a species at a site, while in an occupancy model, environmental covariates are associated with the species latent probability of occupancy at a site. Species intensity and occupancy may be governed by different mechanisms, and inference from an intensity model can be distinct from that provided by an occupancy model19,20,21. Cingolani et al.20 proposed that, in plant communities, certain environmental filters preclude species from occupying a site and an additional set of filters may regulate if a species can flourish. Hence, certain covariates that were unimportant in an occupancy model may improve predictive power in an intensity model.Community confoundingSpecies distributions are shaped by environment as well as competition and mutualism within the community8,22,23. Community confounding occurs when species distributions are explained by a convolution of environmental and interspecies effects and can lead to inferential differences between a joint and single species distribution model as well as create difficulties for fitting JSDMs. Former studies have incorporated interspecies dependence into an occupancy model4,11,12,13,14,15,16, and others have addressed spatial confounding1,17,24,25, but none of these explicitly addressed community confounding. However, all Bayesian joint occupancy models naturally attenuate the effects of community confounding due to the prior on the regression coefficients. The prior, assuming it is proper, induces regularization on the regression coefficients26 that can lessen the inferential and computational impacts of confounding27. Furthermore, latent factor models like that described by Tobler et al.4 restrict the dimensionality of the random species effect which should also reduce confounding with the environmental effects.We address community confounding by formulating a version of our model that orthogonalizes the environmental effects and random species effects. Orthogonalizing the fixed and random effects is common practice in spatial statistics and often referred to as restricted spatial regression27,28,29,30,31. Restricted regression has been applied to spatial generalized linear mixed models (SGLMM) for observations (varvec{y},) which can be expressed as$$begin{aligned} varvec{y}&sim [varvec{y}|varvec{mu }, varvec{psi }], end{aligned}$$
(1)
$$begin{aligned} g(varvec{mu })&= varvec{X}varvec{beta } + varvec{eta }, end{aligned}$$
(2)
$$begin{aligned} varvec{eta }&sim mathcal {N}(varvec{0}, varvec{Sigma }), end{aligned}$$
(3)
where (g(cdot )) is a link function, (varvec{psi }) are additional parameters for the data model, and (varvec{Sigma }) is the covariance matrix of the spatial random effect. In the SGLMM, prior information facilitates the estimation of (varvec{eta },) which would not be estimable otherwise due to its shared column space with (varvec{beta })30. This is analogous to applying a ridge penalty to (varvec{eta },) which stabilizes the likelihood. Another method for fitting the confounded SGLMM is to specify a restricted version:$$begin{aligned} varvec{y}&sim [varvec{y}|varvec{mu }, varvec{psi }], end{aligned}$$
(4)
$$begin{aligned} g(varvec{mu })&= varvec{X}varvec{delta } + (varvec{I}-varvec{P}_{varvec{X}})varvec{eta }, end{aligned}$$
(5)
$$begin{aligned} varvec{eta }&sim mathcal {N}(varvec{0}, varvec{Sigma }), end{aligned}$$
(6)
where (varvec{P}_{varvec{X}}=varvec{X}(varvec{X}varvec{X})^{-1}varvec{X}’) is the projection matrix onto the column space of (varvec{X}.) In the unrestricted SGLMM, the regression coefficients (varvec{beta }) and random effect (varvec{eta }) in (1) compete to explain variability in the latent mean (varvec{mu }) in the direction of (varvec{X})27. In the restricted model, however, all variability in the direction of (varvec{X}) is explained solely by the regression coefficients (varvec{delta }) in (4)31, and (varvec{eta }) explains residual variation that is orthogonal to (varvec{X}). We refer to (varvec{beta }) as the conditional effects because they depend on (varvec{eta }), and (varvec{delta }) as the unconditional effects.Restricted regression, as specified in (4), was proposed by Reich et al.28. Reich et al.28 described a disease-mapping example in which the inclusion of a spatial random effect rendered one covariate effect unimportant that was important in the non-spatial model. Spatial maps indicated an association between the covariate and response, making inference from the spatial model appear untenable. Reich et al.28 proposed restricted spatial regression as a method for recovering the posterior expectations of the non-spatial model and shrinking the posterior variances which tend to be inflated for the unrestricted SGLMM.Several modifications of restricted spatial regression have been proposed30,32,33,34,35. All restricted spatial regression methods seek to provide posterior means (text {E}left( delta _j|varvec{y}right)) and marginal posterior variances (text {Var}left( delta _j|varvec{y}right)), (j=1,…,p) that satisfy the following two conditions36:

1.

(text {E}left( varvec{delta }|varvec{y}right) = text {E}left( varvec{beta }_{text {NS}}|varvec{y}right)) and,

2.

(text {Var}left( beta _{text {NS,}j}|varvec{y}right) le text {Var}left( delta _{j}|varvec{y}right) le text {Var}left( beta _{text {Spatial,}j}|varvec{y}right)) for (j=1,…,p),

where (varvec{beta }_{NS}) and (varvec{beta }_{Spatial}) are the regression coefficients corresponding to the non-spatial and unrestricted spatial models, respectively.The inferential impacts of spatial confounding on the regression coefficients has been debated. Hodges and Reich29 outlined five viewpoints on spatial confounding and restricted regression in the literature and refuted the two following views:

1.

Adding the random effect (varvec{eta }) corrects for bias in (varvec{beta }) resulting from missing covariates.

2.

Estimates of (varvec{beta }) in a SGLMM are shrunk by the random effect and hence conservative.

The random effect (varvec{eta }) can increase or decrease the magnitude of (varvec{beta }), and the change may be galvanized by mechanisms not related to missing covariates. Therefore, we cannot assume the regression coefficients in the SGLMM will exceed those of the restricted model, nor should we regard the estimates in either model as biased due to misspecification. Confounding in the SGLMM causes (text {Var}left( beta _j|varvec{y}right) ge text {Var}left( delta _j|varvec{y}right)), (j=1,…,p), because of the shared column space of the fixed and random effects. Thus, we refer to the conditional coefficients as conservative with regard to their credible intervals, not their posterior expectations.Reich et al.28 argued that restricted spatial regression should always be applied because the spatial random effect is generally added to improve predictions and/or correct the fixed effect variance estimate. While it may be inappropriate to orthogonalize a set of fixed effects in an ordinary linear model, orthogonalizing the fixed and random effect in a spatial model is permissible because the random effect is generally not of inferential interest. Paciorek37 provided the alternative perspective that, if confounding exists, it is inappropriate to attribute all contested variability in (varvec{y}) to the fixed effects. Hanks et al.31 discussed factors for deciding between the unrestricted and restricted SGLMM on a continuous spatial support. The restricted SGLMM leads to improved computational stability, but the unconditional effects are less conservative under model misspecification and more prone to type-S errors: The Bayesian analogue of Type I error. Fitting the unrestricted SGLMM when the fixed and random effects are truly orthogonal does not introduce bias, but it will increase the fixed effect variance. Given these considerations, Hanks et al.31 suggested a hybrid approach where the conditional effects, (varvec{beta }), are extracted from the restricted SGLMM. This is possible because the restricted SGLMM is a reparameterization of the unrestricted SGLMM. This hybrid approach leads to improved computational stability but yields the more conservative parameter estimates. We describe how to implement this hybrid approach for joint species distribution models in the “Community confounding” section.Restricted regression has also been applied in time series applications. Dominici et al.38 debiased estimates of fixed effects confounded by time using restricted smoothing splines. Without the temporal random effect, Dominici et al.38 asserted all temporal variation in the response would be wrongly attributed to temporally correlated fixed effects. Houseman et al.39 used restricted regression to ensure identifiability of a nonparametric temporal effect and highlighted certain covariate effects that were more evident in the restricted model (i.e., the unconditional effects’ magnitude was greater). Furthermore, restricted regression is implicit in restricted maximum likelihood estimation (REML). REML is often employed for debiasing the estimate of the variance of (varvec{y}) in linear regression and fitting linear mixed models that are not estimable in their unrestricted format40. Because REML is generally applied in the context of variance and covariance estimation, considerations regarding the effects of REML on inference for the fixed effects are lacking in the literature.In ecological science, JSDMs often include an unstructured random effect like (varvec{eta }) in (1) to account for interspecies dependence, and hence can also experience community confounding between (varvec{X}) and (varvec{eta }) analogous to spatial confounding. Unlike a spatial or temporal random effect, we consider random species effects to be inferentially important, rather than a tool solely for improving predictions or catch-all for missing covariates. An orthogonalization approach in a JSDM attributes contested variation between the fixed effects (environmental information) and random effect (community information) to the fixed effect.We describe how to orthogonalize the fixed and random species effects in a suite of JSDMs and present a method for detecting community confounding. In the simulation study, we test the efficacy of our method for detecting confounding, show that community confounding can lead to computational difficulties similar to those caused by spatial confounding31, and highlight that, for some models, restricted regression can improve model fitting. We also investigate the inferential implications of community confouding and restricted regression in JSDMs by comparing outputs from the SDM, unrestricted JSDM, and restricted JSDM of the Royle-Nichols and probit occupancy models fit to mammalian camera trap data. Lastly, we discuss other inferential and computational methods for confounded models and consider their appropriateness for joint species distribution modeling. More

Abiotic parametersThe temperature regime experienced by the embryos was purposefully natural and therefore varied between the three air exposure treatments. The subtidal treatment, where embryos were continuously submerged in water, remained around 9.5 °C for the duration of the experiment, while the intertidal treatments experienced dips in temperature during outside air exposure down to 2.5 °C and 0.8 °C, for the low and high intertidal respectively (Fig. 3A). Accumulated thermal units (ATU; days × temperature post collection until hatch) for each air exposure treatment were 79.6, 75.4 and 65.3 for subtidal, low intertidal and high intertidal, respectively. Despite differences in thermal regime, peak hatch was on the same day (March 14, 2021) for all air exposure and CO2 treatments, estimated at 11 dpf.Figure 3Temperature and pH experienced by the herring embryos and larvae throughout the experiment. Hourly measurements of (A) air/water temperature experienced by herring embryos in each of the tidal treatments (subtidal: continuous immersion in 9.5 °C water; low intertidal: 2 × 2 h air exposure; high intertidal: 5 + 9 h air exposure) and (B) pH levels in the tanks for each of the CO2 treatments (greens = control, 400 µatm CO2, yellows = medium, 1600 µatm CO2, reds = high, 3000 µatm CO2); dots are pH levels measured in the individual jars during larval incubation.Full size imageThe pH levels in the tanks were measured hourly and were stable over the course of the embryonic incubation period, with no overlap between treatments, although there was some overlap between individual jars. Control treatment was consistently around a pH of 8, the medium treatment had a pH of 7.4 and the high CO2 treatment had a mean pH of 7.1 (Table 1). After hatch, when the larvae were transferred to the jars, circulation and gas exchange between jars and tank were not as high and CO2 accumulated in the jars over time, leading to pH levels deviating from tank pH levels (Fig. 3B). Although oxygen levels remained high (7–9 mg/L), the pH dropped from a mean 8–7.6 in the control on two occasions, and was brought back up with a partial water exchange from the incubation tank water. The pH in the medium and high CO2 treatments were not as affected (Fig. 3B), however, final water chemistry measurements after completion of the experiment (2 days post water exchange) revealed much higher CO2 levels in all treatments (Table 1: day 15).Table 1 Mean water parameters for each treatment (mean of 3 tanks ± S.D.) at the beginning (day 1, 2021-03-06) and end (day 6, 2021-03-12) of embryonic incubation and mean parameters in the jars (N = 9) at the end of larval incubation (day 15, 2021-03-19); Temperature (T), salinity, pCO2, total CO2 (TCO2) measured at distinct sampling intervals with the BoL; total alkalinity (TA) and pH (on the total scale) calculated with CO2SYS.Full size tableEffect of air exposure and CO2 treatment during embryonic developmentNeither embryonic survival nor growth were significantly affected by treatment in our experiment. Percent daily embryonic mortality was low and not significantly affected by CO2 treatment or air exposure (CO2: p = 0.088, F2 = 2.45; Tide: p = 0.11, F2 = 2.19; CO2*Tide: p = 0.18, F2 = 1.59) . Egg diameter at 6 dpf was also not significantly affected by treatment (CO2: p = 0.38, X2 (2, N = 30) = 1.92; Tide: p = 0.83, X2 (2, N = 30) = 0.33; CO2*Tide: p = 0.08, X2 (2, N = 30) = 8.25). Metabolic rate, as indicated by embryonic heart rate, was significantly affected by air exposure at 6 dpf (p  *; 0.1  >).Full size image More

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