Electronic Journal of Applied Volume 11 · 2021 Multivariate Statistics

Contents

Article 1 Hunting knowledge: harvested American black bears expand our understanding of wildlife-gut microbiome relationships

Sierra J. Gillman

Article 25 Effects of pile-driving noise on black sea bass (Centropristis striata) behavioral patterns in a small tank environment Abigail Keller

Article 44 Assessing the impact of stand thinning on restoration of old-growth forest characteristics

Kavya Pradhan

Article 57 Vegetation Changes Across the Cretaceous-Paleogene Boundary in Montana

Paige K. Wilson

Electronic Journal of Applied Multivariate Statistics Volume 11, 2021

Article

Hunting knowledge: harvested American black bears expand our understanding of wildlife-gut microbiome relationships

Sierra J. Gillman School of Environmental and Forest Sciences, University of Washington E-mail: [email protected]

Received December 2020; accepted in revised form January 2021; published May 2021

Abstract

The distal gut is home to the dynamic and influential gut microbiome, which is intimately linked to mammalian health, promoting and facilitating countless physiological functions. In a time of increased anthropogenic pressures on wildlife due to unfettered habitat destruction, loss of natural prey/foods, and rapid urbanization, the study of wildlife gut microbiomes could prove to be a valuable tool in wildlife management and conservation. Research on wildlife microbiomes is increasingly aimed at determining the evolutionary and ecological factors that govern host-microbiome dynamics. We present three studies related to this topic using samples collected from harvested American black bear (Ursus americanus), 16S rRNA gene amplicon sequencing, and stable isotope analysis. In our first case study, we compared the intrinsic factors (i.e., gut site, sex, life-stage) that influence microbial composition. We found that gut site and sexes did not significantly differ in microbial diversity, but that subadults harbored more diverse microbial communities. In case study II, we examined whether long-term consumption of corn influenced the composition of the colonic microbiome and found no difference in microbial structure. Finally, in case study III, we expand our dietary focus through comparison of the microbial communities of black bears across two states and to determine the impact of human-provisioned foods on black bears’ gut microbial diversity. We determined that black bears exposed to processed human foods were enriched in known pathogenic microbes and had decreased diversity compared to bears only consuming to corn.

Introduction host (Dominianni et al. 2015). Recent years have seen microbiome research rapidly grow Scientists now know that mammals are into wildlife ecology, gaining new insights into metagenomic, composed of both their genes and the behavior, evolution, and conservation of the collective genome of their co-evolved and wildlife species, and leading to mounting interdependent microbial communities, their evidence that microbial communities can be microbiome. The distal gut is home to the vast drastically altered by anthropogenic activities majority of mammalian microbial communities (Trevelline et al. 2019). As such, incorporating (Bäckhed et al. 2005), the gut microbiome, analyses of gut microbiome community which is intimately linked to mammalian health, composition and structure into ecological fitness, and adaption. Indeed, the gut research initiatives may aid our understanding microbiome promotes and facilitates countless of wildlife–gut microbiome co-evolution and physiological functions in mammalian hosts provide novel insights into host health (Amato including immune system maintenance, tissue 2013a). development, behavior, digestion, and vitamin To date, most wildlife-gut microbiome synthesis. Microbial community composition research has focused on colon samples (i.e., can change rapidly due to myriad factors which feces), where fiber fermentation occurs, include diet and the external environment of the understandably because collecting samples from

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other regions of the gastrointestinal tract is also shown that microbiome of captive highly invasive. However, within omnivores individuals are significantly less diverse than and carnivores nearly 90% of fats, their wild counter parts, an artifact in part to carbohydrates, and proteins are absorbed in the dietary differences (Borbón-García et al. 2017). jejunum, the middle section of the small These findings have brought into question intestine. Therefore, it leads to the question, can whether captive individuals have the we understand the overall scope of wildlife- physiological tool set necessary for re- microbiome co-evolution without studying other introduction. Yet, no study has considered the areas of the gastrointestinal tract? In ramifications from direct shifts in diet via mammalian species with complex digestive baiting on host-associated gut microbial physiology, gut microbiomes have been found communities and subsequently, host health. An to be compositionally and functionally distinct estimated 2.8x1012 metric tons of bait is used across the gastrointestinal tract, with areas, such each year across the United States to facilitate as the cecum, acting as a microbial reservoir the harvest of an array of wildlife (Oro et al. that serve as a replenishing system in times of 2013). For instance, in North Carolina hunters community disturbance. However, in mammals can use baiting to assist in hunting black bear. with less complex digestive physiology like In North Carolina, baiting is restricted to carnivores (i.e., short, simplistic gastrointestinal natural, unprocessed/raw food products (e.g., tract lacking a cecum), that might not be the grain, fruits, nut, vegetable, plant crops), with case. A recent study found that the jejunum and corn being the predominant bait source colon of American black bears (Ursus (personal communication); yet in Michigan, americanus) were not compositionally distinct, hunters are permitted to use both unprocessed which could suggests that colon/fecal samples and processed foods (e.g., pet foods, corn-based are sufficient to study overall gut microbiome products, confectioneries) in unrestricted communities (Gillman et al. 2020). As it was quantities, with over 80% of hunters using the first study to characterize the microbiome of confectionaries and baked goods. the jejunum in a wild carnivore, the findings With vastly different regulations for baiting, have not been found elsewhere, and before the the diversity of human-provisioned foods thus practice of using colon/fecal samples as a available to wildlife constitutes a measure of overall gut microbial community in “Westernization” of wildlife diets. In humans, bears can be implemented, must be expanded to the modern “Western diet” high in processed other populations exposed to different carbohydrates, trans/saturated fats, and high environmental conditions. fructose-corn syrup, has led to considerable Despite the rising call for its implementation depletion in microbiome diversity, and in conservation, wildlife-gut microbiome therefore, baiting could be responsible for shifts interactions are underexplored, with most in gut microbial community composition that studies to date focused on the indirect shifts in may alter micro-ecosystem functions and affect diet brought about through habitat degradation. host health and merits investigation (Turnbaugh Research across several taxa have shown that et al. 2006). the gut microbial consortia are largely Wild American black bears represent a dependent upon a host’s habitat quality and nontraditional carnivore that offers a unique consequently food availability with alterations evolutionary and ecological perspective to habitat reducing microbial diversity (Amato necessary to test emerging questions in wildlife- et al. 2013b). If such shifts lead to functional gut microbiome relationships. Black bears are changes in commensal community membership charismatic, and ecologically important large or composition, known as dysbiosis, nutritional carnivores with the adaptability to live in a fitness and host capacity to resist infection may variety of ecosystems. Although a carnivore due be reduced, potentially rendering the host to their digestive physiology, black bears are vulnerable to enteric pathogens. As such, there opportunistic omnivores with their diet have been increased emphasis on better consisting primarily of plants, and in areas understanding the consequences of habitat where baiting occurs, over 40% of their diet can degradation on microbiome composition and consists of bait (Kirby et al. 2017). host health. Research on captive wildlife has Nevertheless, as the black bear species has not

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developed the specialized physiology and Animal Care and Use Committee because morphology required for utilizing vegetative samples were collected opportunistically from resources (i.e., chambered stomach, specialized dead bears that were legally harvested by small intestine, and modified cecum), the hunters, separate from this study. We collected microbial consortia found in the gut influences samples with permission from individual the energy uptake in bears (Sommer et al. hunters/guides under a Michigan DNR – 2016). Further, in the Upper Peninsula of Wildlife Division – Scientific Collector's Permit Michigan and Coastal region of North Carolina, (#SC 1613). All North Carolina samples were over 2,000 black bears are harvested annually, collected by NC state black bear and furbearer providing an opportunistic occasion to engage biologists and did not require a research permit. hunters as citizen scientists to collect biological All statistical analyses and visualizations were samples from regions of the gastrointestinal conducted in R version 3.6.2, Rstudio version tract that would otherwise require invasive 1.4.1032 and with packages phyloseq, lme4, collection on live specimen. Thus, as a common vegan, and python 3.7.7/2.7 for bioinformatic carnivore with a broad diet, widely harvested and Linear discriminant analysis Effect Size across much of North America, and exposed to (LEfSe) analysis. We considered a p-value a variety of baiting strategies, black bears are an threshold of 0.05 significant for each test excellent model for investigating the carnivore- performed. gut microbiome relationship in a non-captive setting. Sample collection and study areas Here, we present three comparative case We sampled black bears across the Upper studies of wild black bear gut microbiomes to Peninsula of Michigan, USA and the in Coastal address knowledge gaps within wildlife Bear Management Unit of North Carolina, USA. microbiome research. In our first case study, we During the 2018 annual fall harvest season in investigate the intrinsic factors (i.e., gut site, Michigan (MI; September through October), sex, lifestage) that might influence the gut hunters collected colon and guard hair samples microbiome and expanded upon a recent study from legally harvested black bear and one suggesting that black bear harbor simplistic roadkill bear (n=35) within 30 minutes of death. microbial communities across gastrointestinal Each guide/hunter was provided pre-packaged, sites by characterizing and comparing the color coordinated sampling kits, and all kits microbial communities of the jejunum and were packed under sterile conditions in the lab. colon of nine harvested black bears. In our Prior to collection, we met with each second case study, we used stable isotopes hunter/guide face to face to review the protocol carbon (δ13C) and nitrogen (δ15N) derived to ensure that consistent collection from guard hair to investigated whether long- methodologies were used by all hunters/guides. term corn consumption influenced gut In North Carolina (NC), colon, jejunum, and microbiome diversity. In our third case study, guard hair samples were opportunistically we maintain a diet comparison, but shift collected by the NC state black bear and attention to compare the gut microbiome of two furbearer biologist from black bears (n=39) black bear populations exposed to two distinct registered at hunting check stations using the types of baiting (i.e., processed vs unprocessed) same collection kits during the 2019 annual to show that perturbational effects of processed black bear harvest (October through November). foods can lead to increased known pathogenic After harvesting the bears, microbes. Conducting these studies highlights guides/hunters/NC biologists used sterile tongue the need to expand wildlife host-microbiome depressors to collect colon samples from the research beyond conventional anthropogenic anus. After field dressing, a pre-measured 16- disturbances and the benefits of using harvested inch string was used to sample the gut wildlife as nontraditional mechanisms for microbiome at a standardized distance from the examining host-microbiome interactions. pyloric sphincter and jejunum content was squeezed the intestine to extrude gut content Methods straight into a pre-labelled sample tube, see We received an exemption from review by the Gillman et al (2020) for detailed collection Northern Michigan University’s Institutional protocols. All colon and jejunum samples were

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stored in sterile 15mL centrifuge tubes containing 7mL of 95% ethanol and stored at Normalization room temperature until DNA was extracted. MI As diversity analyses are susceptible to Department of Natural Resources and NC differences in sequence depths, we used scaling Wildlife Resources provided age data from teeth with ranked subsampling (SRS), a they collected per their harvest registration normalization method, for each case study. SRS protocols. works by dividing all ASVs by a scaling factor in a way that the sum of scaled counts equals DNA isolation and sequencing the selected total number of counts (Cmin) and A total of 82 samples were collected from black then ranking ASVs by converting non-integer bears during the two harvest seasons (MI: 34 counts by an algorithm that minimizes colon; NC: 9 jejunum, 39 colon). We extracted subsampling error with regard to relative DNA from jejunum and colon samples using frequencies of ASVs while also keeping total DNeasy PowerSoil Kits (QIAGEN, Hilden, counts equal to Cmin (Beule and Karlovsky Germany), following the manufacturer’s 2020). protocol with the addition of (1) a heat-step of 10 minutes at 65°C prior to the beginning of the CASE STUDY I: INTRINSIC FACTORS protocol to breakdown proteins and (2) a second THAT SHAPE THE GUT MICROBIOME elution as the last step of extraction. We quantified DNA yields using a NanoDrop 2000c Community composition of the gastrointestinal (ThermoFischer Scientific, Massachusetts, tract sites | To determine if recent findings on USA) and stored extractions at -80°C. PCR black bear gut microbiome community’s amplification and paired-end DNA sequencing similarity holds across geographically distinct of the 16S rRNA V4 gene region were populations, we calculated the mean relative conducted by Argonne National Laboratory abundance of taxa for each gastrointestinal site (Lemont, IL, USA). at the genus level and quantified differences in both alpha (within community) and beta Bioinformatic analysis (between community) diversity. For We analyzed EMP-paired-end microbial characterization of microbiome community sequences supplied by Argonne National structure, we defined major taxa as representing Laboratory using the bioinformatics software ≥ 1% of the community. We created heat trees Quantitative Insights Into Microbial Ecology displaying the phylogenetic structure of each (QIIME2, version 2020.8). Within QIIME2, we community with the R package metacoder. To demultiplexed, denoised microbial sequences, reduce noise from unequal sample sizes between and assigned amplicon sequence variants groups, we only used bears that had both (ASVs) for further analysis using DADA2 jejunum and colon samples for analysis (n= 9) QIIME2 plugin. We constructed a QIIME2 which consisted of four females and five males compatible SSU SILVA 99 reference database with ages ranging between 2-12 years old. We based on the curated NR99 database (version predicted that the jejunum and colon would 138; Quast et al. 2012) using the QIIME2 harbor similar gut microbiome communities. plugin, RESCRIPt version 2020.6.1, which downloads and imports files, removes Statistical analyses sequences with excessive degenerate bases and We normalized samples sequence depth to Cmin homopolymers, and dereplicate sequences and depth of 24,032 sequences/sample. To compare taxonomy to the genus level via last common the within (alpha) diversity for each ancestor. Using the prepared reference database, gastrointestinal site, we calculated two diversity we then constructed and trained an amplicon- indices: Shannon diversity index , which region specific Naïve Bayes sklearn classifier accounts for both abundance and evenness of for V4 EMP Primers, and consequently aligned species present within a community and Faith’s the sample sequences with MAFFT. Prior to Phylogenetic Diversity (PD; Faith 1992), which analysis, we also removed all sequences related is defined as the sum of the branch lengths of a to Archaea, chloroplasts, mitochondria, and phylogenetic tree connecting all the ASVs in the unassigned sequences. community. We use linear mixed effects models

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(LMM) for analyses to determine the taxonomic composition across a dietary gradient relationship between alpha diversity indices and using both weighted and unweighted UniFrac gastrointestinal tract sites. We included distance measure and stable isotopes of carbon gastrointestinal tract site, sex, and age-class (δ13C) and nitrogen (δ15N). As corn is high in (subadult 2–3, adult ≥ 4) as categorical fixed fiber, fermented by bacteria, we predicted that effects; alpha diversity indices as the response gut microbiome community within individuals variables; and since we had two samples for would be influenced by stable isotope position each black bear, we considered individuals as a (a proxy for long-term corn consumption). random effect to account for pseudo-replication. We fit models with restricted maximum Stable isotope analysis likelihood (REML), and we checked residuals to Stable isotope analysis has previously been used confirm model requirements (e.g., normality, to reconstruct the proportional contribution of homoscedasticity, residuals). We determined the human foods to bear diets (Hopkins et al. 2012, significance of main effects for models using Kirby et al. 2017). However, as we lacked Wald Chi-squared tests. Faith’s PD values were stable isotope values from local food sources, log-transformed prior to analysis, to which can vary in isotope signature regionally, accommodate their skewed values. we measured long‐term diet using isotopic For analyzing the variation between position derived from carbon (δ13C) and communities (beta) diversity, we performed nitrogen (δ15N) from guard hairs only, which ANOSIM (Analysis of similarities; Clarke represent assimilated diets. Black bears feeding 1993) on both the quantitative weighted predominantly on corn or bait can be (relative abundance) and qualitative unweighted differentiated from those feeding on natural (presence/absence) UniFrac distance metrices. vegetation based on δ13C, as corn and sugar- UniFrac distance is a beta diversity measure based foods (human foods) use the C4 that include phylogenetic information, which photosynthetic pathway while most forest plants facilitate the identification of factors that may use the C3 pathway because corn and cane- drive differences among microbial communities sugar dominated foods (i.e., human foods) are (Lozupone and Knight 2005). We chose to use enriched in δ 13C relative to C3 native plant ANOSIM as it is a nonparametric procedure that base. We removed hair follicles from guard hair compares rank similarities within groups against samples, as lipids present therein can cause bias rank similarities between groups and computes in stable isotope values of δ13C (DeNiro and a statistic, R, which is scaled to lie between 1 Epstein 1977), and hair samples were cut into and -1 (Clarke 1993). An R value of 1 indicates three equal segments, with the segment closest complete dissimilarity between groups while an to the root used. We sent guard hair samples to R of 0 indicates a high degree of community Cornell University Stable Isotope Laboratory similarity among groups. for stable carbon and nitrogen isotope analysis following standard methods using a Thermo We used LEfSe (Segata et al. 2011) using Delta V isotope ratio mass spectrometer python 2.7 to identify any genera that were interfaced to a NC2500 elemental analyzer. differentially enriched between gastrointestinal Stable isotope values are expressed in delta () sites. We designated a logarithmic Linear notation, as a ratio relative to PeeDee Belemnite Discriminate Analysis (LDA) score of 2.0 as the limestone (C) and atmospheric nitrogen (N) as cut-off for LEfSe analysis following standard parts per mil (‰), such that: protocols.

CASE STUDY II: LONG-TERM CORN

CONSUMPTION AND THE GUT MICROBIOME IN WILD BLACK BEARS Statistical analyses We normalized samples sequence depth to Cmin We centered our second case study on fecal depth of 24,032 sequences/sample. We tested samples from NC black bears (n= 39) and for an association between corn consumption focused our statistical analysis around two types (based on isotopic position) and phylogenetic of data: microbiome dissimilarity and diversity by performing Mantel test (Mantel

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1967) to compare pairwise Euclidean distance jejunum. The colon harbored 13 major taxa, matrices of δ13C and δ15N to weighted and four of which were found solely in the colon unweighted UniFrac distance matrices per the communities (Table 1). Clostridium sensu 39 colon NC samples. Mantel test were based stricto 1 dominated the jejunum, followed by on the Spearman correlation. Escherichia-Shigella, and Streptococcus. Minor genera contributed ~6% of the average jejunum CASE STUDY III: DIFFERENCES OF GUT microbiome community. Clostridium sensu MICROBIOME IN WILD BLACK BEARS stricto 1 was also the most dominant genera in ACROSS GEOGRAPHIC REGIONS the colon community followed by Sarcina and Escherichia-Shigella (Table 1). The jejunum We centered our third case study on fecal microbiome harbored an additional 51 minor samples (n= 72) from MI (n=33) and NC (n= genera while the microbiome community in the 39) black bears. We predicted that black bears’ colon harbored 41. The phylogenetic branching isotopic position would be significantly and bacterial community composition were different, that MI bears would harbor similar in the two gastrointestinal sites (Figure microbiomes significantly lower in alpha 1). diversity compared to NC bears due to the long- term consumption of processed baits, and that Alpha and beta diversity there would be significant differences in There was no significant difference in Shannon community composition between groups due to or Faith’s PD within gastrointestinal sites (χ2 = the different types of baits consumed. 0.06, p= 0.8) or sexes (χ2 = 0.3, p= 0.6). There was a significant difference in Shannon Statistical analyses diversity within age-classes (χ2 = 8.4, df=1, p= Samples were normalized to Cmin depth of 0.004), with subadults having higher Shannon 21,350 sequences/sample. We described the diversity (mean 2.44) than adults (mean 2.0). colonic microbiome community composition of Faith’s PD was not significantly different MI and NC black bears and quantified mean between gastrointestinal sites, sexes, or age- relative abundance of present phyla and genera. classes. There was no significant difference in We ran One-way ANOVA models to determine variation between gastrointestinal site for if δ13C and δ15N significantly varied between weighted (R= -0.06, p= 0.8) or unweighted (R= states and implemented ANOSIM to assess -0.01, p= 0.5) UniFrac, between sexes for differences in isotopic position (matrix of δ13C weighted (R= 0.002, p= 0.4) or unweighted (R= and δ15N) according to state. We used -0.1, p= 1) UniFrac or between age-classes for nonparametric Wilcoxon's rank sum test to weighted (R= -0.01, p=0.5) or unweighted (R = compare alpha diversity indices between each -0.2, p= 0.9). black bear population, and ANOSIM on UniFrac distance metrices. We visualized Differentially enriched genera variation between microbial communities on We identified one enriched major genus in the Non-metric Multi-dimensional Scaling (NMDS) colon: Lactobacillus (LDA= 4.8), and one ordination plots and used LEfSe to identify unidentified minor genus also in the genera that were differentially enriched between Lactobacillaceae family (LDA= 4.8). In the populations using the parameters above. jejunum one enriched major genus: Romboutsia (LDA = 4.12) and Fusobacterium, a minor Results genus (LDA =3.96).

Case Study I: Intrinsic factors that shape the Case Study II: Long-term corn consumption gut microbiome and the gut microbiome in wild black bears

Community composition of the gastrointestinal Beta diversity tract NC bears’ diet/isotopic position appeared to We identified 18 major genera across both vary across individuals with δ13C values ranging gastrointestinal sites. The jejunum harbored 14 from -22.7 to -9.7 and 3.7 to 11.7 for δ15N. major taxa, five of which were unique to the However, beta diversity of gut microbiomes did

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not differ as a function of isotopic position were more enriched in δ13C (mean δ13C: -15.1) (Mantel test, weighted UniFrac r = - and δ15N (mean δ15N: 7) ranging from -22.7 to - 0.06, p = 0.8; unweighted UniFrac r =- 9.7 and 3.7 to 11.7 in δ13C and δ15N, 0.01, p = 0.4). respectively (Figure 2C). MI black bears were depleted in δ13C (mean δ13C: -22.5) and δ15N Case Study III: Differences of gut (mean δ15N: 4.6) ranging from -26.5 to -19.3 microbiome in wild black bears across and 2.3 to 6 in δ13C and δ15N, respectively geographic regions (Figure 2C), indicating that NC black bears consumed considerably higher proportions of Community composition of the gastrointestinal corn. Accordingly, ANOSIM results showed the tract sites geographic region predicted isotopic position Across the NC colon samples, we detected three (R=0.61, p=0.001). major phyla: Firmicutes (81.2% ± 17.8% standard deviation [SD]), Proteobacteria (16% ± Alpha and beta diversity 16.4% SD), Fusobacteriota (1.2% ± 4.4% SD). There was a significant difference in Shannon The MI gut microbiome community also had diversity for NC and MI black bears (W= 417, three major phyla: Firmicutes (60.7% ± 33.2% p=0.01; Figure 2D), but no significant SD), Proteobacteria (33.2% ± 30.2% SD), and difference in Faith’s PD. Colon microbiomes Campilobacterota (5.2% ± 10.4% SD; Figure were more similar within states than between 2A), but each population displayed a high states (weighted: R= 0.2, p= 0.001; unweighted: degree of individual variation for the major R= 0.2, p= 0.001; Figure 3). However, a test of phyla present as demonstrated by the SD. homogeneity of dispersion around the centroid At the genus level, we identified 18 for weighted UniFrac distances was significant, taxa in the NC and MI microbiome indicating higher variability in community communities. The NC community harbored 15 membership in MI compared to NC major genera, five of which were only found in (PERMDSIP: F= 31.6, p= 0.001; Appendix the NC community in major proportions (Figure Figure 1). 2B). In MI black bears, the microbiome community consisted of 13 major genera, three Differentially enriched genera | We identified of which were only found in major proportions 72 significantly enriched genera in the MI gut in the MI population (Table 2). NC and MI microbial community, including the three major bears harbored an additional 23 and 94 minor genera Escherichia-Shigella, Helicobacter, and genera, respectively. Bibersteinia. The NC gut microbial community had 35 significantly enriched taxa, including Differences in isotopic position two major genera, Clostridium sensu stricto 1, We found that δ13C (χ2 = 1001, p< 0.001) and and Cetobacterium (Table 3). δ15N (χ2 = 106, p< 0.001) values significantly differed between states. On average, NC bears

Table 1. Average major (≥ 1% relative abundance) genera of the jejunum and colon gut microbial community. Bolded taxa are unique to that community. Abunda Type Phylum Class Order Family Genus N nce SD Firmicute Clostridi Clostridia Clostridium_se 15. s a Clostridiales ceae nsu_stricto_1 9 24.83% 84% Gammap Proteobac roteobacteri Enterobacter Enterobac Escherichia- 17. Jejun teria a ales teriaceae Shigella 9 17.49% 11% um Firmicute Lactobacilla Streptoco 15. s Bacilli les ccaceae Streptococcus 9 12.97% 54% Peptostrepto Firmicute Clostridi coccales- Peptostre Paeniclostridiu 18. s a Tissierellales ptococcaceae m 9 9.63% 37%

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Firmicute Clostridi Clostridia 9.4 s a Clostridiales ceae Sarcina 9 6.01% 7% Firmicute Lactobacilla Order_La Order_Lactoba 5.5 s Bacilli les ctobacillales cillales 9 5.31% 4% Peptostrepto Firmicute Clostridi coccales- Peptostre 5.4 s a Tissierellales ptococcaceae Romboutsia 9 4.18% 9% Peptostrept Peptostre Firmicute Clostrid ococcales- ptococcacea Paraclostridiu 8.3 s ia Tissierellales e m 9 2.91% 2% Firmicute Lactobacilla Lactobaci 6.5 s Bacilli les llaceae Lactobacillus 9 2.42% 6% Firmicute Lactobacill Streptoco 3.5 s Bacilli ales ccaceae Lactococcus 9 2.27% 4% Firmicute Erysipelotri Erysipelo 4.5 s Bacilli chales trichaceae Turicibacter 9 2.12% 7% Gamma Proteoba proteobact Enterobact Enteroba Family_Entero 2.3 cteria eria erales cteriaceae bacteriaceae 9 1.55% 9% Firmicute Clostrid Clostridiale Clostridi Family_Clostri 2.3 s ia s aceae diaceae 9 1.18% 1% Peptostrepto Firmicute Clostridi coccales- Peptostre Terrisporobacte 1.9 s a Tissierellales ptococcaceae r 9 1.03% 8%

Firmicute Clostridi Clostridia Clostridium_se 19. s a Clostridiales ceae nsu_stricto_1 9 25.57% 96% Firmicute Clostridi Clostridia 18. s a Clostridiales ceae Sarcina 9 17.34% 51% Gammap Proteobac roteobacteri Enterobacter Enterobac Escherichia- 15. teria a ales teriaceae Shigella 9 13.76% 58% Firmicute Lactobacilla Streptoco 11. s Bacilli les ccaceae Streptococcus 9 10.09% 05% Firmicute Lactobacilla Lactobaci 17. s Bacilli les llaceae Lactobacillus 9 9.88% 11% Peptostrepto Firmicute Clostridi coccales- Peptostre Paeniclostridiu 8.3 s a Tissierellales ptococcaceae m 9 4.68% 3% Veillonellal es- Colo Firmicute Negativi Selenomonada Selenomo 7.8 n s cutes les nadaceae Megamonas 9 2.71% 3% Firmicute Lactobacilla Order_La Order_Lactoba 2.1 s Bacilli les ctobacillales cillales 9 2.05% 3% Peptostrept Peptostre Firmicute Clostrid ococcales- ptococcacea Family_Peptost 3.5 s ia Tissierellales e reptococcaceae 9 1.74% 8% Peptostrepto Firmicute Clostridi coccales- Peptostre Terrisporobacte 3.3 s a Tissierellales ptococcaceae r 9 1.74% 1% Bacteroid Bacteroi Bacteroidal Bacteroid 3.5 ota dia es aceae Bacteroides 9 1.24% 2% Campilob Campyl Campyloba Helicobac 1.7 acterota obacteria cterales teraceae Helicobacter 9 1.07% 8% Peptostrepto Firmicute Clostridi coccales- Peptostre 1.9 s a Tissierellales ptococcaceae Romboutsia 9 1.02% 0%

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Table 2. Average major (≥ 1% relative abundance) genera of the NC MI gut microbial community. Bold is unique to that community. Abun State Phylum Class Order Family Genus N dance SD Gammaproteo 26.93 Proteobacteria bacteria Enterobacterales Enterobacteriaceae Escherichia-Shigella 33 % 27.60% 16.22 Firmicutes Clostridia Clostridiales Clostridiaceae Sarcina 33 % 28.74% Clostridium_sensu_s 9.90 Firmicutes Clostridia Clostridiales Clostridiaceae tricto_1 33 % 12.28% 9.01 Firmicutes Bacilli Erysipelotrichales Erysipelotrichaceae Turicibacter 33 % 19.88% 8.18 Firmicutes Bacilli Lactobacillales Streptococcaceae Streptococcus 33 % 17.67% Campylobact 5.06 Campilobacterota eria Campylobacterales Helicobacteraceae Helicobacter 33 % 10.37% Gammaprote 2.95 MI Proteobacteria obacteria Pasteurellales Pasteurellaceae Bibersteinia 33 % 7.84% 2.81 Firmicutes Bacilli Lactobacillales Lactobacillaceae Lactobacillus 33 % 11.13% Peptostreptococcales- Peptostreptococcace 2.49 Firmicutes Clostridia Tissierellales ae Romboutsia 33 % 4.35% 2.19 Firmicutes Clostridia Lachnospirales Lachnospiraceae Cellulosilyticum 33 % 7.23% Peptostreptococcales- Peptostreptococcace 1.87 Firmicutes Clostridia Tissierellales ae Terrisporobacter 33 % 3.03% Order_Lactobacillal Order_Lactobacillal 1.23 Firmicutes Bacilli Lactobacillales es es 33 % 1.93% Family_Clostridiace 1.20 Firmicutes Clostridia Clostridiales Clostridiaceae ae 33 % 3.13%

Clostridium_sensu_s 25.2 Firmicutes Clostridia Clostridiales Clostridiaceae tricto_1 39 % 18.90% Gammaproteo 13.0 NC Proteobacteria bacteria Enterobacterales Enterobacteriaceae Escherichia-Shigella 39 % 14.30% 12.5 Firmicutes Clostridia Clostridiales Clostridiaceae Sarcina 39 % 15.40%

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11.0 Firmicutes Bacilli Lactobacillales Streptococcaceae Streptococcus 39 % 14.30% Firmicutes Bacilli Lactobacillales Lactobacillaceae Lactobacillus 39 8.3% 15.30% Firmicutes Bacilli Erysipelotrichales Erysipelotrichaceae Turicibacter 39 5.2% 11.80% Peptostreptococcales- Peptostreptococcac Firmicutes Clostridia Tissierellales eae Paeniclostridium 39 4.4% 10.20% Firmicutes Bacilli Lactobacillales Streptococcaceae Lactococcus 39 2.9% 6.00% Peptostreptococcales- Peptostreptococcace Firmicutes Clostridia Tissierellales ae Romboutsia 39 2.2% 4.30% Order_Lactobacillal Order_Lactobacillal Firmicutes Bacilli Lactobacillales es es 39 2.0% 2.60% Family_Clostridiace Firmicutes Clostridia Clostridiales Clostridiaceae ae 39 1.8% 2.70% Peptostreptococcales- Peptostreptococcace Firmicutes Clostridia Tissierellales ae Terrisporobacter 39 1.6% 3.00% Peptostreptococcales- Peptostreptococcac Family_Peptostrepto Firmicutes Clostridia Tissierellales eae coccaceae 39 1.3% 3.20% Gammaprote Family_Enterobacte Proteobacteria obacteria Enterobacterales Enterobacteriaceae riaceae 39 1.3% 2.80% Fusobacteriota Fusobacteriia Fusobacteriales Fusobacteriaceae Cetobacterium 39 1.0% 4.30%

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Table 3. Microbial taxa significantly enriched in the gut microbiomes of black bears from Michigan (MI) and North Carolina (NC) and the taxa’s associated pathogenic/mutualistc status

State Phylum Class Order Family Genus LDA MI Actinobacteriota Acidimicrobiia Microtrichales unassigned unassigned 3.43 Actinobacteriota Thermoleophilia Solirubrobacterales Solirubrobacteraceae unassigned 3.34 Actinobacteriota Actinobacteria Propionibacteriales Nocardioidaceae Nocardioides 3.32 Actinobacteriota Actinobacteria Propionibacteriales unassigned unassigned 3.22 Actinobacteriota Thermoleophilia Gaiellales unassigned unassigned 3.11 Actinobacteriota Thermoleophilia GaiellalesColon unassigned unassigned 3.1 Actinobacteriota Actinobacteria Propionibacteriales Nocardioidaceae unassigned 3.08 Actinobacteriota Thermoleophilia Gaiellales unassigned unassigned 2.97 Family_Microbacteria Actinobacteriota Actinobacteria Micrococcales Microbacteriaceae ceae 2.87 Actinobacteriota Thermoleophilia Solirubrobacterales unassigned unassigned 2.85 Actinobacteriota Actinobacteria Micrococcales unassigned unassigned 2.82 Actinobacteriota Actinobacteria Micrococcales Microbacteriaceae unassigned 2.76 Actinobacteriota Thermoleophilia unassigned unassigned unassigned 2.7 Actinobacteriota Actinobacteria Corynebacteriales unassigned unassigned 2.62 Actinobacteriota Actinobacteria Corynebacteriales Corynebacteriaceae unassigned 2.59 Actinobacteriota Actinobacteria Corynebacteriales Corynebacteriaceae Corynebacterium 2.56 Bacteroidota Bacteroidia Sphingobacteriales Sphingobacteriaceae unassigned 3.18 Bacteroidota Bacteroidia Sphingobacteriales Sphingobacteriaceae Mucilaginibacter 3.11 Bacteroidota Bacteroidia Sphingobacteriales unassigned unassigned 3.02 Campilobacterota Campylobacteria Campylobacterales Helicobacteraceae unassigned 4.34 Campilobacterota Campylobacteria Campylobacterales Helicobacteraceae Helicobacter 4.34

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Campilobacterota Campylobacteria unassigned unassigned unassigned 4.32 Campilobacterota Campylobacteria Campylobacterales unassigned unassigned 4.32 Campilobacterota unassigned unassigned unassigned unassigned 4.32 Firmicutes Clostridia Peptostreptococcales_Tissierellales Peptostreptococcales_Tissierellales unassigned 3.32 Family_Streptococcac Firmicutes Bacilli Lactobacillales Streptococcaceae eae 3.25 Firmicutes Bacilli Mycoplasmatales Mycoplasmataceae Mycoplasma 3.24 Firmicutes Clostridia Peptostreptococcales_Tissierellales Peptostreptococcales_Tissierellales Anaerococcus 3.24 Firmicutes Clostridia Lachnospirales Lachnospiraceae Epulopiscium 2.91 Fusobacteriota Fusobacteriia Fusobacteriales Fusobacteriaceae Fusobacterium 2.97 Myxococcota unassigned unassigned unassigned unassigned 2.99 Patescibacteria Saccharimonadia Saccharimonadales Saccharimonadales unassigned 3.36 Patescibacteria Saccharimonadia Saccharimonadales Saccharimonadales Saccharimonadales 3.33 Patescibacteria Saccharimonadia Saccharimonadales unassigned unassigned 3.05 Patescibacteria Saccharimonadia unassigned unassigned unassigned 3 Planctomycetota Planctomycetes Pirellulales Pirellulaceae Pir4_lineage 3.59 Planctomycetota Planctomycetes Gemmatales Gemmataceae unassigned 2.71 Planctomycetota Planctomycetes Gemmatales unassigned unassigned 2.69 Planctomycetota Planctomycetes Gemmatales Gemmataceae unassigned 2.66 Planctomycetota unassigned unassigned unassigned unassigned 2.62 Planctomycetota Planctomycetes unassigned unassigned unassigned 2.49 Proteobacteria unassigned unassigned unassigned unassigned 4.95 Proteobacteria Gammaproteobacteria unassigned unassigned unassigned 4.93 Proteobacteria Gammaproteobacteria Enterobacterales Enterobacteriaceae Escherichia_Shigella 4.84 Proteobacteria Gammaproteobacteria Enterobacterales Enterobacteriaceae unassigned 4.79

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Proteobacteria Gammaproteobacteria Pasteurellales unassigned unassigned 4.25 Proteobacteria Gammaproteobacteria Pasteurellales Pasteurellaceae unassigned 4.25 Proteobacteria Gammaproteobacteria Pasteurellales Pasteurellaceae Bibersteinia 4.21 Proteobacteria Alphaproteobacteria Rhizobiales Order_Rhizobiales Order_Rhizobiales 3.84 Proteobacteria Alphaproteobacteria Rhizobiales Order_Rhizobiales unassigned 3.67 Proteobacteria Gammaproteobacteria Burkholderiales Neisseriaceae Neisseria 3.56 Proteobacteria Alphaproteobacteria Rickettsiales unassigned unassigned 3.51 Proteobacteria Alphaproteobacteria unassigned unassigned unassigned 3.5 Proteobacteria Gammaproteobacteria Burkholderiales Neisseriaceae unassigned 3.47 Proteobacteria Gammaproteobacteria Burkholderiales Nitrosomonadaceae unassigned 3.38 Proteobacteria Gammaproteobacteria Pseudomonadales unassigned unassigned 3.34 Proteobacteria Gammaproteobacteria Pseudomonadales Order_Pseudomonadales unassigned 3.26 Proteobacteria Gammaproteobacteria Enterobacterales Morganellaceae Moellerella 3.24 Order_Pseudomonada Proteobacteria Gammaproteobacteria Pseudomonadales Order_Pseudomonadales les 3.18 Methylobacterium_M Proteobacteria Alphaproteobacteria Rhizobiales Beijerinckiaceae ethylorubrum 3.08 Proteobacteria Gammaproteobacteria Diplorickettsiales Diplorickettsiaceae unassigned 3.07 Allorhizobium_Neorh izobium_Pararhizobiu Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae m_Rhizobium 3.07 Proteobacteria Gammaproteobacteria Diplorickettsiales unassigned unassigned 3.04 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae unassigned 2.98 Proteobacteria Alphaproteobacteria Rhizobiales unassigned unassigned 2.91 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas 2.83 Proteobacteria Alphaproteobacteria Rhizobiales Xanthobacteraceae unassigned 2.76

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Proteobacteria Gammaproteobacteria Diplorickettsiales Diplorickettsiaceae unassigned 2.71 Proteobacteria Alphaproteobacteria Rhizobiales Beijerinckiaceae unassigned 2.57 Candidatus_Udaeobac Verrucomicrobiota Verrucomicrobiae Chthoniobacterales Chthoniobacteraceae ter 3.28 Verrucomicrobiota Chlamydiae Chlamydiales cvE6 unassigned 3.03 Verrucomicrobiota Chlamydiae Chlamydiales cvE6 cvE6 3.03 NC Actinobacteriota Actinobacteria Actinomycetales Actinomycetaceae unassigned 2.73 Actinobacteriota Actinobacteria Actinomycetales Actinomycetaceae Actinomyces 2.68 Actinobacteriota Actinobacteria Actinomycetales 2.63 Prevotellaceae_Ga6A Bacteroidota Bacteroidia Bacteroidales Prevotellaceae 1_group 3.34 Bacteroidota Bacteroidia Bacteroidales Bacteroidaceae Bacteroides 3.29 Bacteroidota Bacteroidia Bacteroidales Bacteroidaceae unassigned 3.29 Bacteroidota Bacteroidia Bacteroidales unassigned unassigned 3.23 Bacteroidota Bacteroidia Bacteroidales Prevotellaceae unassigned 3.04 Campilobacterota Campylobacteria Campylobacterales Campylobacteraceae Campylobacter 3.04 Campilobacterota Campylobacteria Campylobacterales Campylobacteraceae unassigned 3.03 Firmicutes unassigned unassigned unassigned unassigned 5.02 Firmicutes Clostridia unassigned unassigned unassigned 4.92 Clostridium_sensu_str Firmicutes Clostridia Clostridiales Clostridiaceae icto_1 4.9 Firmicutes Clostridia Clostridiales unassigned unassigned 4.86 Firmicutes Clostridia Clostridiales Clostridiaceae unassigned 4.86 Firmicutes Bacilli Lactobacillales unassigned 4.64 Firmicutes Bacilli Lactobacillales Lactobacillaceae Lactobacillus 4.33 Firmicutes Bacilli Lactobacillales Lactobacillaceae unassigned 4.31

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Firmicutes Clostridia Clostridiales Clostridiaceae Family_Clostridiaceae 3.58 Firmicutes Negativicutes Veillonellales_Selenomonadales Selenomonadaceae Megamonas 3.53 Firmicutes Negativicutes Veillonellales_Selenomonadales Selenomonadaceae unassigned 3.52 Firmicutes Clostridia Peptostreptococcales_Tissierellales Peptostreptococcaceae Paraclostridium 3.44 Firmicutes Bacilli Erysipelotrichales Erysipelatoclostridiaceae unassigned 3.18 Family_Erysipelatocl Firmicutes Bacilli Erysipelotrichales Erysipelatoclostridiaceae ostridiaceae 3.17 Firmicutes Bacilli Lactobacillales Catellicoccaceae unassigned 3.16 Firmicutes Bacilli Lactobacillales Catellicoccaceae Catellicoccus 2.99 Firmicutes Bacilli Mycoplasmatales unassigned unassigned 2.7 Firmicutes Bacilli Mycoplasmatales Mycoplasmataceae unassigned 2.69 Fusobacteriota Fusobacteriia Fusobacteriales Fusobacteriaceae unassigned 3.67 Fusobacteriota unassigned unassigned unassigned unassigned 3.66 Fusobacteriota Fusobacteriia Fusobacteriales unassigned unassigned 3.66 Fusobacteriota Fusobacteriia unassigned unassigned unassigned 3.65 Fusobacteriota Fusobacteriia Fusobacteriales Fusobacteriaceae Cetobacterium 3.65 Proteobacteria Gammaproteobacteria Enterobacterales Enterobacteriaceae Plesiomonas 3.56 Hafnia_Obesumbacter Proteobacteria Gammaproteobacteria Enterobacterales Hafniaceae ium 2.84

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Genus_Clostridium_sensu_stricto_1 Genus_Tyzzerella Genus_Cellulosilyticum Genus_Clostridium_sensu_stricto_11 Family_Clostridiaceae Family_Lachnospiraceae

Tyzzerella Family_Peptostreptococcaceae Family_Clostridiaceae Clostridium_sensu_stricto_1 Genus_Veillonella Genus_Sarcina Genus_Anaerosporobacter Cellulosilyticum Genus_Anaerosporobacter Clostridium_sensu_stricto_11 Family_Peptostreptococcaceae Family_Lachnospiraceae Anaerosporobacter Genus_Romboutsia Genus_Cellulosilyticum Family_Clostridiaceae Genus_Gemella B. Colon A. Jejunum Lachnospiraceae Sarcina Veillonella Family_Clostridiaceae Anaerosporobacter Genus_RF39 Ruminococcaceae Family_Lachnospiraceae Genus_Terrisporobacter Family_Peptostreptococcaceae Romboutsia Cellulosilyticum Genus_uncultured Family_Lachnospiraceae Family_Peptostreptococcaceae Gemella Lachnospiraceae Genus_uncultured Genus_Paraclostridium Genus_Turicibacter Epulopiscium Genus_Terrisporobacter Genus_Romboutsia Genus_Megamonas Genus_Pediococcus RF39 Clostridiaceae uncultured Clostridiaceae Epulopiscium Terrisporobacter Terrisporobacter uncultured Genus_NK4A214_group Clostridium_sensu_stricto_13 Turicibacter Genus_Paeniclostridium Sarcina Romboutsia Genus_Clostridia_UCG−014 Peptostreptococcaceae Veillonellaceae Gemellaceae Genus_Epulopiscium Paeniclostridium Pediococcus Paraclostridium Oscillospirales Ruminococcaceae Megamonas Genus_Clostridium_sensu_stricto_1 Genus_[Eubacterium]_coprostanoligenes_group Genus_Epulopiscium Genus_Lactobacillus Genus_Clostridium_sensu_stricto_13 Erysipelotrichaceae RF39 Clostridia_UCG−014 Clostridium_sensu_stricto_1 NK4A214_group Family_Streptococcaceae Genus_Incertae_Sedis Genus_Sarcina Peptostreptococcaceae Oscillospiraceae Lactobacillus Lachnospirales [Eubacterium]_coprostanoligenes_group Incertae_Sedis Family_Micrococcaceae Genus_Weissella Family_Streptococcaceae Genus_Acidothermus Paeniclostridium Staphylococcales [Eubacterium]_coprostanoligenes_group Genus_Lactococcus Eubacteriaceae Oscillospirales Lactobacillaceae Clostridia_UCG−014 Clostridiales Intestinimonas Lachnospirales Veillonellales−Selenomonadales Peptostreptococcales−Tissierellales Family_Micrococcaceae Lactococcus Peptostreptococcales−Tissierellales Weissella Acidothermus Genus_Intestinimonas Clostridiales Genus_Paeniclostridium Family_Lactobacillaceae Erysipelotrichales Genus_Exiguobacterium Genus_StreptococcusStreptococcaceae Clostridia_UCG−014 Genus_Staphylococcus Genus_Gemella Genus_Clostridia_UCG−014 Streptococcus RF39 Genus_Pseudonocardia Genus_IMCC26256 Family_Lactobacillaceae Genus_Clostridia_vadinBB60_group Genus_Actinomyces Clostridia_vadinBB60_group Peptostreptococcales−Tissierellales Leuconostocaceae Eubacteriales Staphylococcus Exiguobacterium IMCC26256 Micrococcaceae Pseudonocardia Gemella Clostridia_UCG−014 Acidothermaceae Clostridia Actinomyces Clostridia_UCG−014 IMCC26256 Leuconostoc Order_Lactobacillales Genus_RF39 Genus_Leuconostoc Genus_Christensenellaceae_R−7_group Clostridia_UCG−014 Negativicutes Genus_Pseudonocardia Micrococcales Genus_Corynebacterium RF39 Order_Lactobacillales Lactobacillales Bacilli Pseudonocardiaceae Genus_Turicibacter Staphylococcaceae Clostridia Genus_Corynebacterium Actinomycetaceae Corynebacterium Gemellaceae Pseudonocardia Christensenellaceae_R−7_group Order_Lactobacillales Turicibacter Christensenellales Corynebacteriaceae Corynebacterium Frankiales Corynebacteriales RF39 Corynebacteriaceae IMCC26256 Microbacteriaceae Micrococcales Christensenellaceae Actinomycetales Pseudonocardiales Pseudonocardiaceae Genus_Bifidobacterium Genus_WD2101_soil_group Genus_uncultured Family_Microbacteriaceae Erysipelotrichaceae Corynebacteriales uncultured Bacillales Bifidobacterium Family_Microbacteriaceae Staphylococcales Bifidobacteriaceae WD2101_soil_group Firmicutes Genus_Ureaplasma uncultured RF39 Pseudonocardiales Actinobacteria Bifidobacteriales WD2101_soil_group Acidimicrobiia Ureaplasma Genus_uncultured uncultured Actinobacteria Erysipelotrichales Tepidisphaerales Genus_Aquisphaera Eggerthellaceae Firmicutes Genus_Enterorhabdus uncultured Aquisphaera Coriobacteriia Coriobacteriales Enterorhabdus Family_Lactobacillaceae Genus_Leuconostoc Coriobacteriia Coriobacteriales Gemmataceae Isosphaeraceae Genus_Pediococcus Bacilli Eggerthellaceae Isosphaerales 67−14 Family_Lactobacillaceae Actinobacteriota Gemmatales Genus_67−14 Leuconostoc Phycisphaerae Planctomycetes Actinobacteriota Pediococcus Genus_Pajaroellobacter 67−14 WPS−2 Genus_WPS−2 Weissella Pajaroellobacter Thermoleophilia Genus_Weissella Solirubrobacterales WPS−2 Polyangiaceae Planctomycetota WPS−2 Leuconostocaceae Polyangiales Genus_Haliangium Lactobacillaceae WPS−2 Lactobacillus Gastranaerophilales Gastranaerophilales Haliangiales Genus_Lactobacillus Haliangium Haliangiaceae Lactococcus Cyanobacteria Gastranaerophilales Genus_Gastranaerophilales Polyangia Lactobacillales Planctomycetes Vampirivibrionia WPS−2 Genus_Lactococcus Streptococcaceae uncultured Pirellulales Genus_uncultured Fusobacteriales Streptococcus Myxococcota Planctomycetota Genus_uncultured Cetobacterium Fusobacteriia uncultured Genus_uncultured Genus_uncultured Family_Streptococcaceae Pirellulaceae uncultured Genus_Cetobacterium Genus_Streptococcus Myxococcota Fusobacteriaceae Family_Streptococcaceae uncultured Acetobacterales Acetobacteraceae Fusobacterium Fusobacteriota Genus_Fusobacterium uncultured uncultured Gemmatimonadota Gemmatimonadaceae Rhodospirillales Bacteria Order_Lactobacillales Gemmatimonadetes Gemmatimonadales Desulfobacterota Genus_uncultured Polyangia Fusobacteriota Genus_uncultured uncultured Bacteria Campilobacterota uncultured Acetobacteraceae Alphaproteobacteria Genus_Methylobacterium−Methylorubrum Order_Lactobacillales Haliangiales Acetobacterales Order_Lactobacillales Fusobacteriia Methylobacterium−Methylorubrum Genus_SM2D12 Elsterales Acidobacteriota Kingdom_Bacteria SM2D12 Campilobacterota Rhodospirillales Beijerinckiaceae Genus_uncultured Genus_Roseiarcus Acidobacteriota Proteobacteria Genus_Bryobacter Bryobacteraceae SM2D12 uncultured Bryobacterales uncultured Bryobacter Rickettsiales Roseiarcus Fusobacteriales Genus_uncultured Campylobacteria Genus_Haliangium Haliangium Candidatus_Solibacter Solibacteraceae Bdellovibrionota Caulobacteraceae uncultured Family_Xanthobacteraceae Proteobacteria Haliangiaceae Patescibacteria Rhizobiales Caulobacterales Family_Xanthobacteraceae Genus_Candidatus_Solibacter Solibacterales Alphaproteobacteria Beijerinckiaceae Cetobacterium Xanthobacteraceae Fusobacteriaceae Campylobacteria uncultured Acidobacteriae Kingdom_Bacteria Chloroflexi Methylobacterium−Methylorubrum Genus_Leptotrichia Vicinamibacteria Genus_uncultured Genus_Cetobacterium Bacteroidota uncultured Rhizobiales Family_Xanthobacteraceae Acidobacteriae Family_Acidobacteriaceae_(Subgroup_1) Xanthobacteraceae Genus_Methylobacterium−Methylorubrum Fusobacterium Subgroup_2 Family_Xanthobacteraceae Verrucomicrobiota Acidobacteriales Sphingomonadales Genus_Fusobacterium Rhizobiaceae Family_Acidobacteriaceae_(Subgroup_1) Campylobacterales Bacteroidota Genus_Acinetobacter Koribacteraceae Gammaproteobacteria Acinetobacter Verrucomicrobiota Subgroup_2 Devosiaceae Subgroup_17 Candidatus_Koribacter Helicobacteraceae Campylobacterales Saccharimonadia Bdellovibrionia Legionellales Allorhizobium−Neorhizobium−Pararhizobium−Rhizobium Nodes Genus_Candidatus_Koribacter Pseudomonadales Kingdom_Bacteria Nodes Acidobacteriales Acidobacteriaceae_(Subgroup_1) Subgroup_17 Moraxellaceae Helicobacter Legionellaceae Occallatibacter Genus_Treponema Order_Enterobacterales Campylobacteraceae Gammaproteobacteria Sphingomonadaceae Devosia Genus_Allorhizobium−Neorhizobium−Pararhizobium−Rhizobium Chlamydiae Verrucomicrobiae Moraxella Genus_Occallatibacter Subgroup_17 Xanthomonadales Genus_Subgroup_17 Campylobacteraceae Genus_Helicobacter KD4−96 Helicobacteraceae Genus_Devosia Subgroup_2 Diplorickettsiales Acidobacteriaceae_(Subgroup_1) Pseudomonadaceae 1.00 1.0 Bdellovibrionales Order_Enterobacterales 1.00 1.0 Genus_Moraxella Bacteroidia Pedosphaerales Pasteurellales Campylobacter Verrucomicrobiales Diplorickettsiaceae Saccharimonadales Genus_Campylobacter Pseudomonadales Occallatibacter Campylobacter Steroidobacterales Aquicella Family_Sphingomonadaceae Bacteroidia Genus_Subgroup_2 Kingdom_Bacteria Legionella Chlamydiales Genus_Campylobacter Order_Enterobacterales Genus_Occallatibacter Helicobacter Chitinophagales Sphingobacteriales Genus_Aquicella Burkholderiales Pasteurellaceae Genus_Proteus Pseudomonas Genus_Legionella 1.78 30.8 Verrucomicrobiae Family_Sphingomonadaceae Pedosphaeraceae 1.78 39.4 Cytophagales Bibersteinia Bdellovibrionaceae Pasteurellales Genus_Helicobacter Bacteroides Enterobacterales Proteus Steroidobacteraceae Genus_Pseudomonas KD4−96 Parachlamydiaceae Enterobacterales Rubritaleaceae Genus_Bacteroides Sphingobacteriaceae Genus_Bibersteinia Morganellaceae Burkholderiales Bacteroidaceae Genus_uncultured Edaphobaculum Family_Pasteurellaceae Providencia Moraxellaceae Bdellovibrio Kingdom_Bacteria Pedosphaeraceae Comamonadaceae 2.78 120.0 uncultured Bacteroidales Order_Enterobacterales 2.78 155.0 Enterobacteriaceae Chthoniobacterales Genus_Providencia Chitinophagaceae Prevotellaceae_Ga6A1_group Saccharimonadaceae Acinetobacter Bacteroidales Genus_uncultured Genus_Edaphobaculum Sutterellaceae Genus_Bdellovibrio Comamonadaceae Neochlamydia Genus_Acinetobacter Luteolibacter Prevotellaceae Bacteroidaceae Genus_Escherichia−Shigella Escherichia−Shigella Xiphinematobacteraceae Genus_Pedosphaeraceae KD4−96 Morganellaceae Genus_Prevotella Family_Pasteurellaceae Porphyromonadaceae Genus_Prevotellaceae_Ga6A1_group Pedosphaerales Family_Comamonadaceae Prevotella Escherichia−Shigella Mucilaginibacter Genus_Neochlamydia Kingdom_Bacteria 4.00 269.0 Enterobacteriaceae 4.00 347.0 Genus_Luteolibacter Genus_Escherichia−Shigella Genus_Hafnia−Obesumbacterium Bacteroides Order_Enterobacterales Candidatus_Xiphinematobacter Family_Enterobacteriaceae Family_Comamonadaceae KD4−96 Prevotellaceae Family_Comamonadaceae Genus_Mucilaginibacter Genus_Candidatus_Xiphinematobacter Pasteurellaceae Rikenellaceae Genus_KD4−96 Genus_Bacteroides Order_Enterobacterales Genus_uncultured Porphyromonas Family_Enterobacteriaceae 5.44 477.0 Family_Enterobacteriaceae 5.44 616.0 Pedosphaeraceae Prevotellaceae_Ga6A1_group Family_Comamonadaceae Genus_Porphyromonas Family_Pasteurellaceae Genus_Candidatus_Saccharimonas ASV count

Family_Enterobacteriaceae ASV count uncultured Alistipes Bibersteinia Genus_Prevotellaceae_Ga6A1_group 7.11 961.0 7.11 745.0 Genus_uncultured Family_Pasteurellaceae

Genus_Bibersteinia Genus_Alistipes Samples with reads Samples with reads 9.00 1380.0 9.00 1070.0

Figure 1. Heat tree of the abundance of bacterial taxa at different ranks present in (A) jejunum and (B) colon gut microbial communities. The size and color of nodes and edges are correlated with the abundance of each taxa. The central nodes are the total of all the other nodes in the tree for each phylum. Both the communities display similar phylogenetic branching and abundance of taxa.

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Figure 2 . Mean relative abundance of the major taxa found within the black bear colons from the Upper Peninsula of Michigan and North Carolina. "Minor" indicates the combined taxa with < 1% relative abundance of each site. (A) The major phylum present within the jejunum and colon and (B) major genus. (C) Distribution of black bears stable isotope values (•) in δ13C‰ and δ15N‰ isotopic space from the Upper Peninsula and North Carolina. Diet varied between the two states with black bears in the Upper Peninsula exposed to high levels of processed human foods and North Carolina black bears exposed to high levels of corn both during legal baiting periods. (D) Violin plots with boxplots inside of Shannon diversity for black bear from the two states. Bears in North Carolina had significantly higher diversity.

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Figure 3. Non-metric Multi-dimensional Scaling (NMDS) for (A) weighted UniFrac distance between microbial communities from two different black bear populations with environmental loadings, (B) unweighted UniFrac distance between microbial communities from two different black bear populations with environmental loadings, (C) weighted UniFrac distance between microbial communities from two different black bear populations with taxa that significantly influenced variation, and (D) unweighted UniFrac distance between microbial communities from two different black bear populations with taxa that significantly influenced variation.

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Discussion associated with carbohydrate metabolism, totaled over 96% of each community, By leveraging the evolutionary and highlighting the critical role diet plays in ecological uniqueness of black bears, we have wildlife-gut microbiome relationships. The showcased a comparative approach for presence of these major phyla could exploring host-microbiome relationship across demonstrate the role that the gut microbiome multiple scales of analysis. In case study I, we plays in host metabolism. The physiology of comparatively investigated microbiomes black bears results in limited potential for between gastrointestinal sites, sexes, and age- significant fiber fermentation directly. classes. We specifically showed, contrary to However, fiber fermentation still plays a role in other mammalian species (Greene and host gastrointestinal health and indirectly McKenney 2018), but similar to previous influences metabolic modulation via the gut studies on black bears (Gillman et al. 2020), microbiome. The gut microbiome contributes to black bears do not harbor compositionally energy harvest from the diet and adiposity by distinct microbial communities within (alpha) expanding nutrient sources due to a versatile gastrointestinal sites or sexes, nor between metabolome (Sommer and Bäckhed 2013). (beta). Microbial taxonomic heat trees of the While bear gastrointestinal tract indicate that jejunum and colon display similar branching retention time is limited, leading to reduced and community composition within both opportunity for microbial fermentation, in polar, gastrointestinal sites (Figure 1). Similar to the grizzly, and black bears, the presence of short findings in Gillman et al (2020), subadult black chain-fatty acids, which are produced by the bears were determined to have higher alpha microbial breakdown of fiber in the gut, have diversity compared to adults, although they been detected. The presence of short chained found a higher degree in phylogenetic diversity, fatty acids indicate metabolic activity and the whereas our current findings only found potential for intestinal microbiome to contribute significant differences in richness and evenness. to energy maintenance (Sommer et al. 2016). As in other mammals, our results could suggest For instance, Sommer et al., (2016) compared that as black bears mature and diets shift, their the fecal microbial communities of brown bears bodies are better able to filter or select for in the summer and winter and found during the specific microbes, i.e., via the immune system. summer months, that the microbial community As previously suggested, the similarity was dominated by Proteobacteria and between the two communities could be Firmicutes and overall higher microbial attributed to the simplicity of the black bear diversity. Further, Sommer et al. (2016) gastrointestinal tract, which leads to rapid demonstrated seasonal metabolic phenotypes of digestion time (13h for meat/hair and 7h for brown bears could be transferred to germ-free foliage; Pritchard and Robbins 1990). The lack mice and mice colonized with summer of digestive “pockets”, such as the appendix and microbial communities gained more fat; cecum, where microbes can be stored (Bollinger suggesting that seasonal differences in the bear et al. 2007, Greene and McKenney 2018), microbiome contribute to seasonal metabolic means microorganisms have limited time to changes, consequently the microbial community colonize the gastrointestinal tract and have no could be linked to the healthy obesity phenotype points of refuge. Black bears’ diet undoubtably in bears. has the strongest influence on the similarity In our second case study, we investigated between the two communities. As black bears’ whether the potential homogenization of diet diets can be upwards of 80% plant-based, from long-term consumption of human- microbial presence/absence will be modulated provisioned corn via baiting influenced by dietary sources present in the gastrointestinal differences in gut microbial composition tract, regardless of the evolutionary function of between black bears. Although there was a wide the gastrointestinal site. The known function of range in isotopic position/long-term corn major phyla in each community highlights this. consumptions in NC black bears, we did not For example, the two most dominant phyla in find significant differences in microbial both the jejunum and colon were Firmicutes, communities. As corn is high in carbohydrates, known fiber fermenters, and Proteobacteria, fiber, and sugars, it provides ample nutritional

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resources for microbes, and although increased disease (Shin et al. 2015, Moon et al. 2018). In corn consumption could lead to an overall MI bears, the phylum Campliobacterota made homogenization in diet across bears, the up 5% of the overall community, which was energetic needs of the dominant fiber attributed to the significantly enriched genus, fermenters and carbohydrate metabolizers found Helicobacter, a known pathogen. Conversely, within the colon microbial communities might NC bears were enriched in taxa associated with be met regardless of the plant types consumed. obesity/weight gain, genera associated with Conversely, as stable isotope can give insight plant polysaccharide degradation and increased into more long-term dietary composition, the fiber intake, and protein absorption and energy gut microbial community can experience rapid uptakes in dogs (Bermingham et al. 2017). turnover within hours or days following a While there has been a focus on how diet dietary shift, and therefore, stable isotopes of correlates with the increased occurrence of hair might not detect dietary changes on a time many diseases in humans and domestic pets, an scale necessary for analyzing microbial turnover altered microbiota resulting from diet-induced and diet (Ingala et al. 2019). dysbiosis, may also be a factor that contributes In our third case study, we expanded our to diseases in wildlife. The ANOSIM results analysis across geographic regions to further showed there was significant differences for probe the dietary mechanisms that potentially both relative abundance and the underlie baiting-induced microbial shifts. presence/absence of microbes between states Studying groups from differing geographic and NMDS plots illustrated separation of bears regions has been previously used to evaluate the between states. While there is an overlap in impact of habitat quality and captivity on the isotopic space between MI and NC bears gut microbiome (Borbón-García et al. 2017, (Figure 2C), the values in each population Ingala et al. 2019) with differences often imply different food sources. For instance, in attributed to dietary shifts. The use of stable MI bears that are provided processed human isotope analysis as a proxy for long-term diet foods, δ13C near -21 and above, indicate bears between two populations used in this study consuming long-term proportions of processed further demonstrates that diet is the human foods (Kirby et al. 2017). Whereas NC predominant driver in differences in microbial bears in similar isotopic positions are diversity within a species. Importantly, we consuming higher proportions of native found that the types of baits used in hunting can vegetation, as bears in NC are not offered significantly impact wildlife gut microbiomes, processed baits this is mostly likely due to corn, which supports previous links to processed being a C4 photosynthetic pathway plant, foods and dysbiosis in other mammalian having considerably enriched δ13C/δ15N species. Our results show that microbial values. Additionally, NMDS plots illustrated composition were distinct between MI and NC Escherichia-Shigella relative abundance black bears, with MI black bears harboring increased in bears with depleted δ13C, significantly reduce microbial diversity and indicating an association with native vegetation enriched levels of bacterial taxa associated with consumption, and based on weighted score disease. The taxa enriched in each community values, there were several genera which uncover the consequences of the types of baits significantly influenced variation between approved for use in hunting. For example, groups, which were differently enriched in although MI black bears had over double the either MI or NC bears (Table 3). number of significant enriched taxa present, We also observed greater heterogeneity in the closer examination of the identified Order and relative phylogenetic lineage in MI black bears, genera reveal a number of pathogenic bacteria, while NC bears were more localized to associated with Diphtheria, gut inflammation in centroids (Appendix Figure 1). The increase in dogs, Crohn’s disease, systemic infections in heterogeneity in the MI microbial community livestock, and ulcers (Table 3), and like humans could be a result from high variability in diet, that consume more Westernized diets and dogs with some black bears in MI consuming with gut inflammation, harbored an increased predominately natural vegetation and others level in Proteobacteria which is a potential eating high quantities of processed foods. The diagnostic signature of dysbiosis and risk of elevated level of heterogeneity in the MI black

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bears could be an indication of dysbiosis as 2015). In species such as black bears that individuals displaying consuming human- undergo extreme dietary shifts due to torpor, the provisioned foods shifting away from a reliance on their gut microbial community to balanced microbial community. In NC bears, all meet nutritional needs could be an integral part dietary sources were from plants and the effects to survival (Sommer et al. 2016). of the human-provisioned corn could limit disruption of dietary shifts. Therefore, wildlife agencies should consider the types of baits Conclusions allowed, as demonstrated here, not all baits could be detrimental to microbial diversity. Our multifaceted approach to investigate Of the 12 states that permit baiting to hunt wildlife gut-microbiome relationship black bears, only North Carolina limits the demonstrates not only the potential of types of baits to unprocessed plants. In other integrating harvested mammals into wildlife-gut states, many of the baits used by hunters are microbiome research but provides a framework food items composed of processed foods that to engage stakeholders such as hunters in the contain artificial sweeteners and other scientific process. As collecting fresh samples processed carbohydrates – all foods linked to from elusive wildlife species can be shifts in the gut microbiome of humans. From challenging, the use of harvested animals allows humans and model species, we know the typical for collection from parts of the body that are Western diet contains a low abundance and otherwise inaccessible. Further, as human variety of microbe-accessible carbohydrates provisioned foods are wildly distributed across (MACs), non-digestible dietary fibers that the United States, our research highlights the provide an important source of energy for need to consider the potential ramifications of fermentative gut bacteria. The abundance and the type of baits used on wildlife health. As diversity of MACs in the diet can influence gut wildlife managers are tasked with maintaining microbiome composition and functionality, healthy, harvestable wildlife, we encourage ultimately impacting host immunity and health them to reconsider bating policies given the (Daïen et al. 2017). Reduced gut microbial potential negative health implications of diversity could have several health-related provisioning wildlife with foods characteristic implications for black bears and other wildlife, of the “Western diet”. Besides black bears, as the composition of the gut microbiome can mammalian species known to visit and consume affect the efficacy of nutrition uptake from bear bait include wolves (Canis lupus), martens food. Moreover, while some gut microbiome (Martes americana), and fishers (Martes functions can be performed by several bacterial pennanti) (personal communication with taxa, other digestive functions involve unique hunters). The effects of human-provisioned interactions among specific bacteria. Much of foods on wildlife gut microbiomes could what we know of the functional shifts resulting therefore be widespread across ecosystems, with from microbial dysbiosis relies on human/model hitherto undetermined consequences for wildlife species, and in wildlife, inference is often health. These implications merit continued derived from captive studies; but, while consideration. findings from captive research move wildlife- To further quantify the effects of gut microbiome research forward, the well- baiting on mammalian gut microbiomes and the established differences between captive and subsequent consequences for host fitness, we wild specimen microbial membership limit our suggest that future studies investigate the long- full understanding for in situ populations term trends in gut microbiome composition (Cheng et al. 2015). Results from research on among individuals within a population exposed captive wildlife suggests decreased microbial to baiting. By monitoring microbial functional diversity in captivity leads to loss of loss along with other measures of host health, functionally important microbes and increase in such as body condition scores or parasite loads, potentially pathogenic taxa (Cheng et al. 2015). we could gain a more holistic understanding of In carnivores, such shifts in microbial how changes in gut microbial communities are membership in the gut microbiome, may result linked to changes in host health. As our results in major physiological changes (Cheng et al. showed, jejunum and colon microbiomes did not significantly differ within the communities,

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indicating that fecal samples may provide a Amato, K. R., C. J. Yeoman, A. Kent, N. sufficient proxy for monitoring gut microbiome Righini, F. Carbonero, A. Estrada, H. R. responses to diet perturbations in carnivores Gaskins, R. M. Stumpf, S. Yildirim, and M. (i.e., species with relatively simple Torralba. 2013. Habitat degradation impacts gastrointestinal tracts). As such, longitudinal black howler monkey (Alouatta pigra) studies of known individuals could be employed gastrointestinal microbiomes. The ISME to reveal long-term microbial responses to journal 7:1344. changes in diet. Additionally, although we Bäckhed, F., R. E. Ley, J. L. Sonnenburg, D. A. found MI bears to be enriched with known Peterson, and J. I. Gordon. 2005. Host- pathogens and bacteria linked to gastrointestinal bacterial mutualism in the human intestine. disease, here, we did not attempt to predict the Science 307:1915–1920. functional pathways associated with gut Beule, L., and P. Karlovsky. 2020. Improved microbial community, as the utility of normalization of species count data in metagenomic inference tools relies heavily on ecology by scaling with ranked subsampling the quality and abundance of reference genomes (SRS): application to microbial from highly studied species such as humans, communities. PeerJ 8:e9593. and is consequently limited for wildlife research Bollinger, R. R., A. S. Barbas, E. L. Bush, S. S. (Iwai et al. 2016). In the future, researchers Lin, and W. Parker. 2007. Biofilms in the could build upon this work by incorporating large bowel suggest an apparent function of multi-omics techniques that could provide the human vermiform appendix. Journal of greater insight to the functional consequences of theoretical biology 249:826–831. black bear and other non-targeted wildlife Borbón-García, A., A. Reyes, M. Vives-Flórez, exposures to human-provisioned foods. and S. Caballero. 2017. Captivity shapes the gut microbiota of Andean bears: insights Acknowledgements into health surveillance. Frontiers in Microbiology 8:13–16. The authors are grateful to J. Olden for support Cheng, Y., S. Fox, D. Pemberton, C. Hogg, A. in determining statistical methods and review of T. Papenfuss, and K. Belov. 2015. The the manuscript and E. Largent at MDNR for Tasmanian devil microbiome—implications assistance in aging bears. The authors are also for conservation and management. grateful to the guides/hunters that donated Microbiome 3:76. samples to the study. The authors would also Clarke, K. R. 1993. Non-parametric like to thank two anonymous reviewers for their multivariate analyses of changes in valuable input. community structure. Australian journal of ecology 18:117–143. This project was supported by Sigma Xi’s Daïen, C. I., G. V. Pinget, J. K. Tan, and L. Grants-in-Aid of Research Award Grant ID Macia. 2017. Detrimental Impact of G2018100198233997. S.J.G. was supported Microbiota-Accessible Carbohydrate- through the NSF Graduate Research Fellowship Deprived Diet on Gut and Immune Grant No. 1000263298. Homeostasis: An Overview. Frontiers in This material is based upon work supported by Immunology 8. the National Science Foundation Graduate DeNiro, M. J., and S. Epstein. 1977. Mechanism Research Fellowship Program under Grant No. of carbon isotope fractionation associated 1000263298. Any opinions, findings, and with lipid synthesis. Science 197:261–263. conclusions or recommendations expressed in Dominianni, C., R. Sinha, J. J. Goedert, Z. Pei, this material are those of the author(s) and do L. Yang, R. B. Hayes, and J. Ahn. 2015. not necessarily reflect the views of the National Sex, Body Mass Index, and Dietary Fiber Science Foundation. Intake Influence the Human Gut Microbiome. PLOS ONE 10:e0124599. References Faith, D. P. 1992. Conservation evaluation and Amato, K. R. 2013. Co-evolution in context: the phylogenetic diversity. Biological importance of studying gut microbiomes in Conservation 61:1–10. wild animals. Microbiome Science and Medicine:10–29.

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Gillman, S. J., E. A. McKenney, and D. J. R. grizzly and black bears. canadian Journal of Lafferty. 2020. Wild black bears harbor Zoology 68:1645–1651. simple gut microbial communities with little Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. difference between the jejunum and colon. Schweer, P. Yarza, J. Peplies, and F. O. Scientific Reports 10:20779. Glöckner. 2012. The SILVA ribosomal RNA Greene, L. K., and E. A. McKenney. 2018. The gene database project: improved data inside tract: The appendicular, cecal, and processing and web-based tools. Nucleic colonic microbiome of captive aye-ayes. acids research 41:D590–D596. American Journal of Physical Anthropology Segata, N., J. Izard, L. Waldron, D. Gevers, L. 166:960–967. Miropolsky, W. S. Garrett, and C. Hopkins, J. B., P. L. Koch, C. C. Schwartz, J. Huttenhower. 2011. Metagenomic biomarker M. Ferguson, S. S. Greenleaf, and S. T. discovery and explanation. Genome biology Kalinowski. 2012. Stable isotopes to detect 12:R60. food-conditioned bears and to evaluate Shin, N.-R., T. W. Whon, and J.-W. Bae. 2015. human-bear management. The Journal of Proteobacteria: microbial signature of Wildlife Management 76:703–713. dysbiosis in gut microbiota. Trends in Ingala, M. R., D. J. Becker, J. B. Holm, K. Biotechnology 33:496–503. Kristiansen, and N. B. Simmons. 2019. Sommer, F., and F. Bäckhed. 2013. The gut Habitat fragmentation is associated with microbiota — masters of host development dietary shifts and microbiota variability in and physiology. Nature Reviews common vampire bats. Ecology and Microbiology 11:227–238. Evolution 9:6508–6523. Sommer, F., M. Ståhlman, O. Ilkayeva, J. M. Iwai, S., T. Weinmaier, B. L. Schmidt, D. G. Arnemo, J. Kindberg, J. Josefsson, C. B. Albertson, N. J. Poloso, K. Dabbagh, and T. Newgard, O. Fröbert, and F. Bäckhed. 2016. Z. DeSantis. 2016. Piphillin: Improved The gut microbiota modulates energy Prediction of Metagenomic Content by metabolism in the hibernating brown bear Direct Inference from Human Microbiomes. Ursus arctos. Cell Reports 14:1655–1661. PLOS ONE 11:e0166104. Trevelline, B. K., S. S. Fontaine, B. K. Hartup, Kirby, R., D. M. Macfarland, and J. N. Pauli. and K. Kohl. 2019. Conservation biology 2017. Consumption of intentional food needs a microbial renaissance: A call for the subsidies by a hunted carnivore. The Journal consideration of host-associated microbiota of Wildlife Management 81:1161–1169. in wildlife management practices. Lozupone, C., and R. Knight. 2005. UniFrac: a Proceedings of the Royal Society B: New Phylogenetic Method for Comparing Biological Sciences. Microbial Communities. Appl. Environ. Turnbaugh, P. J., R. E. Ley, M. A. Mahowald, Microbiol. 71:8228–8235. V. Magrini, E. R. Mardis, and J. I. Gordon. Mantel, N. 1967. The detection of disease 2006. An obesity-associated gut microbiome clustering and a generalized regression with increased capacity for energy harvest. approach. Cancer research 27:209–220. Nature; London 444:1027–31. Moon, C. D., W. Young, P. H. Maclean, A. L. Cookson, and E. N. Bermingham. 2018. Metagenomic insights into the roles of Proteobacteria in the gastrointestinal microbiomes of healthy dogs and cats. MicrobiologyOpen 7. Oro, D., M. Genovart, G. Tavecchia, M. S. Fowler, and A. Martínez-Abraín. 2013. Ecological and evolutionary implications of food subsidies from humans. Ecology letters 16:1501–1514. Pritchard, G. T., and C. T. Robbins. 1990. Digestive and metabolic efficiencies of

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Supplementary Figures

Appendix Figure 1. Box plot of Inter-group and Intra-group Beta distance (ANOSIM Analysis) for Michigan (MI) and North Carolina (NC) black bear weighted. The x-axis represents the grouping and the y- axis represents the distance calculated by weighted UniFrac. R-value: R-value range (-1, 1). The R-value > 0 shows that inter-group differences are greater than intra-group differences. Boxes represent the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively), and the horizontal line inside the box defines the median. Whiskers represent the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. MI black bears had significantly larger heterogeneous dispersion from the centroid compared to NC bears as determined by PERMDISP (F= 32, p= 0.001).

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Article

Effects of pile-driving noise on black sea bass (Centropristis striata) behavioral patterns in a small tank environment

Abigail Keller School of Marine and Environmental Affairs, University of Washington E-mail: [email protected]

Received December 2020; accepted in revised form January 2021; published May 2021

Abstract

Increased investment in U.S.’s offshore wind industry necessitates greater investigation into the industry’s environmental impacts. Regions of development interest overlap with the range of black sea bass (Centropristis striata), an ecologically, recreationally, and commercially important fish species in southern New England and the mid-Atlantic Bight. Offshore wind turbine construction creates acute pile-driving sounds, and recent investigations indicate that the hearing range of C. striata, and their most sensitive frequencies, directly overlap with high-amplitude anthropogenic noise pollution, including underwater construction. This study uses a typical behavioral response model, measuring the amount of time exhibiting seven behaviors before, during, and after pile-driving sound exposure in a controlled, small tank environment. Through multivariate analytical techniques, this investigation provides evidence for altered black sea bass behavioral patterns during exposure to pile-driving sounds. Altered patterns indicate reduced activity, including greater time spent sinking and pivoting or resting. Additionally, black sea bass behavioral changes diminish throughout sound exposure, although the mechanism of behavioral recovery is unclear. This study is the first to measure behavioral effects of anthropogenic noise on C. striata and supports further investigations into the impacts of long-term exposure to anthropogenic sounds.

Introduction same anthropogenic disturbances and altered ocean soundscapes. In salt water, sound waves Increasing anthropogenic noise in the world’s travel 4.5 times faster than air, detectable as oceans can have a range of physiological and particle motion and pressure waves. Acute, loud behavioral effects on marine animals (Popper sound sources and chronic noise can mask and Hastings, 2009). Although research and acoustic signals and decrease signal-to-noise regulatory efforts associated with anthropogenic ratios in fishes. These negative impacts can noise often focus on marine mammals and other interfere with a range of fish behavior, protected species, fishes are also exposed to the including feeding (Voellmy et al., 2014),

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predator avoidance (Simpson et al., 2016), anthropogenic noise pollution, including noise group cohesion (Bruintjes and Radford, 2013), from activities like shipping and the underwater settlement behavior (Holles et al., 2013), and construction required for offshore wind farms spawning success (Stanley et al. 2017). Altered (Stanley et al. 2020). behavior not only has ecological and Black sea bass show an attraction evolutionary consequences for fishes, but can towards structurally complex habitats, including also induce cascading ecosystem effects and rocky reefs, cobble and rock fields, stone coral incur negative economic impacts for patches, exposed stiff clay, and mussel beds commercially important fish species. (Steimle et al., 1999). The foundations that As renewable energy development support wind turbines in the ocean create expands globally to meet demand for electricity, structures that function as artificial reefs, the world’s oceans are becoming increasingly changing the local marine habitat, increasing urbanized. Particularly, development on the heterogeneity, and attracting high densities of eastern seaboard of North America has led to marine organisms, including black sea bass. the first major marine wind energy installations Previous research suggests that artificial reefs to be permitted within U.S. waters, with the support greater density and biomass of fish, and Block Island Wind Farm off the coast of Rhode compared to soft bottom areas, artificial reefs Island as the U.S.’s first offshore wind farm. provide higher catch rates (Langhamer, 2012). Development of these renewable energy regions Anecdotal evidence from recreational fishermen will lead to an increase in pile-driving during suggest that the habitat-forming nature of wind the construction process. Pile-driving produces turbine pilings produce higher catch rates at the highly intensive and impulsive strike sounds as Block Island Wind Farm in Rhode Island a metal pile is hammered into the ocean floor. (Stanley et al., 2020). Previous research suggests that marine pile- In 2016, the Department of Energy and driving can cause negative behavioral effects in Department of the Interior published a National fishes, including anti-predator behavior (Spiga Offshore Wind Strategy to facilitate the et al., 2017), disruption to schooling dynamics development of an offshore wind industry in the (Herbert-Read et al., 2017), avoidance of United States (Gilman and Golladay, 2016). As essential feeding and spawning habitats (Allison the industry grows, it is critical to determine the et al., 2019), and disruption of essential behavioral impacts on ecologically and intraspecific communication (Allison et al., economically important fish species in response 2019). Additionally, fishmen have expressed to wind farm development. This study used a concern that pile-driving sounds will have typical behavioral response experimental negative effects on the behavior and distribution design, intended to measure black sea bass of target species (Thomsen et al., 2006). behavioral responses to pile-driving sound The effects of pile-driving sounds have patterns and frequencies. In a controlled small not yet been studied on black sea bass, tank environment, black sea bass were exposed Centropristis striata, a species of commercial to pile-driving recordings, and the time spent and recreational importance along the eastern exhibiting seven behaviors was recorded before coast of the United States. Black sea bass occur exposure, during exposure, and after exposure along the entire eastern seaboard of North for both control and experimental sound America, and the northern-most population treatments. Since fish behavior is complex, undergoes a seasonal cross-shelf migration, multivariate statistical approaches were used to moving north and inshore from southern and reveal behavioral patterns and determine how deeper waters (Steimle et al., 1999). Black sea these behaviors change in complementary or bass are typically found singly, except during non-complementary ways. Through principal spawning season when they tend to aggregate component analyses and tests of discrimination (Mercer, 1989). Cape Cod is the northern among groups, this study aims to meet two population’s northern-most endpoint, therefore objectives: (a) Do black sea bass exposed to overlapping regions of interest for wind energy pile-driving sounds exhibit changes in development. Physiological examinations behavioral patterns? (b) Do changes in black sea indicate that the frequencies at which black sea bass behavioral patterns diminish during bass are most sensitive to sound directly overlap exposure to sound? By understanding black sea with frequencies of high-amplitude bass behavioral responses to anthropogenic

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sounds and assessing their ability to habituate to three sessions of pile driving playback sound exposure, we can gain a more (Supplemental Figure 2). comprehensive picture of offshore wind energy Each 15-minute time segment was cut development’s environmental impact. into one-minute intervals (00:00-01:00, 04:00- 05:00, 09:00-10:00, and 14:00-15:00). Each 1- minute interval was then analyzed using Methods Behavioral Observation Research Interactive

Software (BORIS version 6.3.7), and behaviors

were manually recorded. The number of seconds Experimental Design and Data Collection spent exhibiting each of seven behaviors Adult black sea bass were wild-caught via long- (hereafter referred to as ‘behavioral time line fishing off the New Jersey coast during July budget’) was recorded for each fish and each of 2017 and July 2018. They were held in 8-ft the four 1-minute intervals (Table 1). Behaviors diameter fiberglass tanks at average include resting, swimming, bobbing, pivoting, temperatures of 16.4 ± 2.1 °C, with a hovering, back-finning, and sinking. Recorded fluctuating range between 12.4-20.9 °C, which behaviors did not co-occur, and the dominant is within the normal temperature range for C. behavior was favored. striata. Black sea bass remained in holding Statistical Analysis conditions for as few as 7 weeks and up to 14 All data manipulation and analysis were months prior to running experiments. Pile- conducted in R version 3.6.3 using the packages driving sound exposure experiments took place ‘vegan’ (Oksanen et al. 2011) and ‘Biostats’ from May-September 2018. (McGarigal, 2009). A total of 2,007 black sea In groups of three, black sea bass were bass behavioral time budgets were included in transferred to a 1.8-meter diameter fiberglass the analysis. Twenty-six data points were test tank and allowed to acclimate overnight missing due to video camera malfunction or (Supplemental Figure 1). The test tanks lack of visibility, reducing the total number of contained an underwater speaker and an observations to 1,981. Since the missing data underwater camera for sound exposure and were randomly distributed throughout the behavior video recording. The following day, dataset, the missing observations were simply the test group of fish experienced three separate removed. The behavioral time budgets were sound treatments at 9:00am, 11:30am, and averaged across the three black sea bass in each 2:00pm EST (hereafter referred to as “session”). group. Each session included filming 15 minutes of baseline pre-sound behavior, followed by 15 minutes of exposure to no sound (control) or a Do black sea bass exposed to pile-driving sounds pile-driving sound-track (experimental), and exhibit changes in behavioral patterns? then 15 minutes of recovery or post-sound behavior (Supplemental Figure 2). The pile- Within each 15-minute video segment, the driving sound-track included repeated striking behavioral time budgets were converted into a sounds within the frequency range associated proportion of the total recording time (4 with pile-driving (168-197 dB re 1 uPa). minutes of behavior recording), resulting in 167 Following the 2pm sound treatment observations. The behavioral time budget session, black sea bass were measured for total proportions underwent an arcsine square-root body length, weighed, and moved to a separate transformation, suitable for proportional data, holding tank until they could be released. Black and the data were evaluated using a Mardia sea bass ranged in size from 24.5-47.0 cm (219- Kurtosis test of multivariate normality (Mardia, 1314 g). Temperatures in the test tank ranged 1974). from 14.3-17.3 °C with dissolved oxygen The proportional behavioral time budgets ranging from 3.07-8.15 mg/L. In total, the were considered to be a member of one of six experiment contained 8 control and 13 groups: Control: Pre-Exposure, Control: experimental groups with three black sea bass in Exposure, Control: Post-Exposure, Treatment: each group. Within the 13 experimental groups, Pre-Exposure, Treatment: Exposure, Treatment: 7 groups were exposed to two sessions of pile Post-Exposure (Supplemental Figure 2). A driving playback and 6 groups were exposed to permutational multivariate analysis of variance (perMANOVA) was used to determine if the

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black sea bass behavioral patterns in underwent an arcsine square-root multivariate space were significantly different transformation. between the six groups. perMANOVA partitions The four one-minute subsets were analyzed the within- and among-group sums of squares of individually and were considered to be a the Euclidean distance matrix and is permuted member of one of six groups: Control: Pre- 1000 times to test for significance (Anderson Exposure, Control: Exposure, Control: Post- 2001). A series of pair-wise perMANOVA tests Exposure, Treatment: Pre-Exposure, Treatment: were also conducted to compare each of the six Exposure, Treatment: Post-Exposure. A groups directly against each other. Since permutational multivariate analysis of variance perMANOVA tests assume independence in (perMANOVA) was used to determine if the observations, strata for sessions (9 am, 11:30 black sea bass behavioral patterns in am, and 2 pm treatments) were used to retain multivariate space were significantly different the data structure during permutation and to between the six groups for each one-minute satisfy the assumption of time independence subset. A series of pair-wise perMANOVA tests between observations. A test of multivariate were also conducted to compare each of the six homogeneity of group dispersions was groups directly against each other for each one- conducted both globally and pair-wise to minute subset. Since perMANOVA tests assume determine if the dispersions of one or more independence in observations, strata for groups were significantly different (Anderson et sessions (9 am, 11:30 am, and 2 pm treatments) al., 2006). were used to retain the data structure during A principal component analysis (PCA) was permutation and to satisfy the assumption of conducted to search for and summarize time independence between observations. A test behavioral patterns associated with group of multivariate homogeneity of group differences. The PCA was performed using a dispersions was conducted both globally and variance-covariance approach and a Euclidean pair-wise to determine if the dispersions of one distance matrix. Statistical significance of each or more groups were significantly different for principal component (PC) axis was tested using each one-minute subset. a Monte Carlo randomization test, using 1000 A PCA was conducted on each one-minute permutations to compare the observed subset separately to search for and summarize eigenvalues to the distribution of eigenvalues behavioral patterns associated with group under the null hypothesis of no real correlation differences in each minute. The PCA was structure. Structure correlations between the performed using a variance-covariance approach original behavior variables and the principal and a Euclidean distance matrix. Statistical component scores were calculated, and the significance of each PC axis was tested using a significance of the variable loadings (behavior Monte Carlo randomization test, using 1000 significance) was determined through permutations to compare the observed permutation. On each PC axis, behavior eigenvalues to the distribution of eigenvalues loadings were included only if significantly under the null hypothesis of no real correlation correlated to the axes (p < 0.01 and r2 > 0.1). structure. Structure correlations between the The observations in multivariate space were original behavior variables and the principal visualized by separating observations by the six component scores were calculated, and the groups and by adding 95% confidence ellipses. significance of the variable loadings (behavior significance) was determined through Do changes in black sea bass behavioral patterns permutation. On each PC axis, behavior diminish during exposure to sound? loadings were included only if significantly correlated to the axes (p < 0.01 and r2 > 0.1). Within each 15-minute video segment, the The observations in multivariate space were behavioral time budgets were subsetted by visualized by separating observations by the six st th th minute (1 minute, 5 minute, 10 minute, and groups and by adding 95% confidence ellipses. th 15 minute). The behavioral time budgets were A Procrustean superimposition approach converted into a proportion of the total was used to test the overall degree of recording time (one minute), resulting in 167 association between the four one-minute observations for each of the four one-minute segments (Gower, 1971). To determine how subsets. The behavioral time budget proportions behavioral patterns change during the 15-minute

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exposure, three Procrustean comparisons of Exposure and Control: Post-Exposure groups matrices were conducted: minute 1 behaviors were marginally significant. No significant were compared to minute 5 behaviors, minute 5 difference in behavior was detected between the behaviors were compared to minute 10 Treatment: Exposure and Treatment: Post- behaviors, and minute 10 behaviors were Exposure groups (Table 2). compared to minute 15 behaviors. The The multivariate test of dispersion for the Procrustes analysis compares the previously global comparison between all six groups conducted PCA ordination solutions by rotating indicated that there was no significant the ordination to find an optimal difference in dispersion between groups (5 df, superimposition that maximizes their fit, and 0.66 F-statistic, 0.665 p-value). The pair-wise the sum of the squared residuals between multivariate tests of dispersion directly configurations in their optimal superimposition comparing each group indicated that there were can be used as a metric of association. A no significant differences in dispersion between permutation procedure (PROTEST) was used to groups. assess the statistical significance of the The principal component analysis ordinated Procrustean fit (Jackson, 1995). During the 167 black sea bass behavioral time budgets, permutation, strata for sessions (9 am, 11:30 and the first three PC axes explained significant am, and 2 pm treatments) were used to retain variation in the behavioral time budgets. The the data structure and to satisfy the assumption first two PC axes explained 59.7% of the of time independence between observations. variation in the behavioral time budgets (PC1: The residuals for each comparison of matrices 39.2%, PC2: 20.5%). All seven behavior were extracted, and a one-way analysis of variable loadings were statistically significant variance (ANOVA) was conducted on each of (p-value < 0.001). Behaviors including hovering the three sets of residuals to determine if there (r = 0.755), back-finning (r = 0.298), bobbing (r are significant differences in the residuals = 0.696), pivoting (r = 0.482), sinking (r = between the six groups (Control: Pre-Exposure, 0.327), and swimming (r = 0.627) were Control: Exposure, Control: Post-Exposure, positively correlated with PC1, while resting (r Treatment: Pre-Exposure, Treatment: Exposure, = -0.931) was negatively correlated with PC1 Treatment: Post-Exposure) and between the (Figure 1a). Behaviors bobbing and resting are three time sessions (9 am, 11:30 am, and 2 pm negatively correlated with each other. Behaviors treatments). sinking and pivoting are positively correlated, and behaviors swimming and hovering are Results positively correlated. There is no correlation between sinking/pivoting and resting, and Do black sea bass exposed to pile-driving sounds sinking/pivoting and bobbing behaviors. exhibit changes in behavioral patterns? Differences between the six groups were Based on the Mardia Kurtosis test of visualized on the PC axes (Figure 1b). The multivariate normality, the data can be ordination of observations in the Treatment: considered as coming from a normal Exposure group was consistent with the pair- distribution (1.68 Z-statistic, 0.093 p-value). wise perMANOVA test and displayed marginally different behavioral patterns than the The global perMANOVA test comparing all six groups (Control: Pre-Exposure, Control: other five groups based on visual inspection. Exposure, Control: Post-Exposure, Treatment: The Treatment-Exposure group is strongly Pre-Exposure, Treatment: Exposure, Treatment: associated with sinking, pivoting, and resting Post-Exposure) indicated that there are no behaviors, and is not associated with bobbing significant differences in fish behavior between behavior (Figure 1b). The other five groups are groups (5 df, 1.33 F-statistic, 0.219 p-value). more strongly associated with bobbing However, the series of pair-wise perMANOVA behavior. tests between the six groups showed significant (p-value < 0.05) or marginally significant (p- Do changes in black sea bass behavioral patterns value < 0.10) differences in behavior between diminish during exposure to sound? the Treatment: Exposure group and all three The global perMANOVA test comparing all control groups and the Treatment: Pre-Exposure six groups (Control: Pre-Exposure, Control: group. The difference between Treatment: Post- Exposure, Control: Post-Exposure, Treatment:

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Pre-Exposure, Treatment: Exposure, Treatment: behavior recording. In minute 1, the first two Post-Exposure) indicated marginally significant PC axes explained significant variation (58.6% differences in fish behavior between groups of total variation) in the behavioral time during minute 1 and 5 (Minute 1: 5 df, 1.57 F- budgets. All seven behavior variable loadings statistic, 0.095 p-value; Minute 5: 5 df, 1.61 F- were statistically significant (p-value < 0.001). statistic, 0.098 p-value). The global In minute 5, the first two PC axes explained perMANOVA test comparing all six groups significant variation (54.9% of total variation) indicated that there were no significant in the behavioral time budgets. All seven differences in fish behavior between groups behavior variable loadings were statistically during minute 10 and minute 15 (Minute 10: 5 significant. In minute 10, the first two PC axes df, 1.09 F-statistic, 0.365 p-value; Minute 15: 5 explained significant variation (53.5% of total df, 1.17 F-statistic, 0.298 p-value). variation) in the behavioral time budgets. The The minute 1 pair-wise series of back-finning behavior variable loading was no perMANOVA tests indicated significant longer statistically significant (p-value = 0.431), differences (p-value < 0.05) in fish behavior but the remaining six behavior variable loadings between the Treatment: Exposure group and all were statistically significant (p-value < 0.001). three control groups and the Treatment: Pre- In minute 15, the first two PC axes explained Exposure group. The minute 5 pair-wise series significant variation (53.0% of total variation) of perMANOVA tests indicated significant (p- in the behavioral time budgets. The back- value < 0.05) or marginally significant (p-value finning behavior variable loading was no longer < 0.10) differences between the Treatment: statistically significant (p-value = 0.508), but Exposure group and the Control: Exposure, the remaining six behavior variable loadings Control: Post-Exposure, and Treatment: Pre- were statistically significant (p-value < 0.001) Exposure groups. The Treatment: Post-Exposure (Supplemental Figure 3). group was significantly (p-value < 0.05) or During minutes 1 and 5, the variable marginally significantly (p-value < 0.10) loadings indicated that pivoting, back-finning, different than the Control: Exposure and and sinking behaviors were positively Control: Post-Exposure groups. In minutes 10 correlated; swimming and hovering behaviors and 15, there were also significant (p-value < were positively correlated; and resting and 0.05) and marginally significant (p-value < bobbing behaviors were negatively correlated. 0.10) differences between the Treatment: The variable loadings for minutes 10 and 15 Exposure group and other groups (Supplemental were highly similar and indicated that sinking, Table 1). swimming, and pivoting behaviors are The multivariate test of dispersion for the positively correlated; resting and hovering global comparison between all six groups behaviors were negatively correlated; and indicated that there was no significant resting and bobbing behaviors were negatively difference in dispersion between groups in all correlated (Supplemental Figure 3). minutes (Minute 1: 5 df, 1.33 F-statistic, 0.253 Differences in behavioral time budgets for p-value; Minute 5: 5 df, 1.11 F-statistic, 0.350 the six groups during each one-minute segment p-value; Minute 10: 5 df, 1.77 F-statistic, 0.120 were visualized on the PC axes. During minute p-value; Minute 15: 5 df, 0.08 F-statistic, 0.990 1, the ordination of observations in the p-value). In the minute 1 pair-wise tests of Treatment: Exposure group displayed different dispersion, the Treatment: Exposure group was behavioral patterns than the other five groups significantly (p-value < 0.05) or marginally based on visual inspection. The Treatment- significantly (p-value < 0.10) different than all Exposure group was strongly associated with other groups (Supplemental Table 1). In the sinking, pivoting, back-finning, and resting minute 10 pair-wise tests of dispersion, the behaviors and was not associated with bobbing Control: Exposure group was significantly (p- behavior. The other five groups were more value < 0.05) or marginally significantly (p- strongly associated with bobbing behavior. value < 0.10) different than all other groups Minute 5 exhibited similar behavioral patterns (Supplemental Table 1). to minute 1, although the Treatment: Exposure Separate principal component analyses group displayed more variance than in minute 1. ordinated 167 black sea bass behavioral time During minutes 10 and 15, the behavioral budgets during minute 1, 5, 10, and 15 of

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patterns in the six groups were indistinguishable statistic, 0.076 p-value) (Figure 2). The (Supplemental Figure 3). ANOVA indicated no significant differences in Procrustes analysis was used to determine residuals between groups in comparison 2 (5 df, overall degree of concordance throughout the 0.36 F-statistic, 0.876 p-value) and comparison 15-minute segments. For all three comparisons, 3 (5 df, 0.42 F-statistic, 0.838 p-value) (Figure the randomization test (PROTEST) indicated 2). The ANOVA indicated no significant statistically significant association between differences in residuals between trials in all matrices (comparison 1: 0.67 m2, 0.001 p-value; three comparisons (comparison 1: 2.62 F- comparison 2: 0.68 m2, 0.001 p-value; statistic, 0.107 p-value; comparison 2: 0.613 F- comparison 3: 0.65 m2, 0.001 p-value). The one- statistic, 0.435 p-value; comparison 3: 0.22 F- way test of variance (ANOVA) indicated statistic, 0.636 p-value). marginally significant differences in residuals between groups in comparison 1 (5 df, 2.04 F-

Table 1: Glossary of seven behaviors recorded for each fish.

Response Behavior Glossary Behavior Definition Resting maintains position on tank floor Swimming movement along a place in forward direction Bobbing movement across the air/water interface Pivoting change in body orientation around central point Hovering holding position while elevated in the water column Back Finning fish motions fins in backward circles lowering in the water column; pectoral fins are not Sinking being used to move body in forward direction

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Table 2: Results of pair-wise perMANOVA tests and pair-wise multivariate dispersion tests between six groups for all combined recorded minutes in each 15-minute recording segment. Bottom diagonals show the results of pair-wise perMANOVA tests between the six groups. Each cell contains the F-statistic and the associated p-value in parenthesis. Upper diagonals show the results of pair-wise multivariate tests of dispersion between the six groups. Each cell contains the t-statistic and associated p-value in parenthesis. Significant and marginally significant test statistics are bolded. (p-value < 0.05**; p-value < 0.10*)

Control: Contr Treatme Control: Treatment: Treatment: Pre- ol: nt: Post-Exposure Pre-Exposure Post-Exposure Exposure Exposure Exposure Control : 0.98 0.32 -0.24 0.75 -0.35

Pre- (0.345) (0.759) (0.810) (0.457) (0.721) Exposure Control 0.04 0.65 0.77 1.68 : 0.63 (0.527) (0.973) (0.526) (0.452) (0.108) Exposure Control : 0.09 0.16 0.09 1.05 -0.03

Post- (0.929) (0.855) (0.921) (0.299) (0.979) Exposure Treatme nt: 0.45 0.25 0.83 -1.02 0.12 (0.891) Pre- (0.633) (0.786) (0.409) (0.305) Exposure Treatme 2.76 2.40 3.59 2.39 -1.11 nt: (0.079)* (0.094)* (0.046)** (0.063)* (0.272) Exposure Treatme nt: 2.02 1.62 2.81 1.22 0.54 Post- (0.149) (0.196) (0.072)* (0.229) (0.479) Exposure

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Figure 1: Behavioral time budget ordination based on principal component analysis for all combined recorded minutes in each 15-minute recording segment. Ordination of the 167 behavioral time budgets in multivariate space for all combined recorded minutes. The triangles represent each group centroid. The observations are color-coded by group, and 95% confidence ellipses are included. A. Ordination of three control groups. B. Ordination of Treatment: Pre-Exposure group. C. Ordination of Treatment: Exposure group. D. Ordination of Treatment: Post-Exposure group. E. Behavioral variable loadings on PC axes 1 and 2. The length of the vectors indicates the strength of the associated variable for describing the PCs. The direction of the vectors indicates the direction of the associated variable gradients in ordination space.

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Figure 2: Dot plot of the residuals from the Procrustes comparison of matrices. The residuals are separated by group, and the black diamond indicates the mean of the residuals in each group. A. Residuals from the comparison of minute 1 and minute 5 behavioral data. B. Residuals from the comparison of minute 5 and minute 10 behavioral data. C. Residuals from the comparison of minute 10 and minute 15 behavioral data.

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Historically, multivariate statistical Discussion approaches assessing animal behavior have been used in behavioral genetics to link rat This study uses a behavioral response behavioral patterns to genetic underpinnings experimental design to examine the effect of (Berton et al. 1997). Similar approaches can be pile-driving playback on black sea bass used to reveal behavioral patterns and behavior in a controlled tank environment. A relationships among behaviors in fish. Through multivariate statistical approach was used to the ordination procedures used in this study, a search for and summarize behavioral patterns few behavioral patterns emerge. Time spent and to examine interrelationships among sinking and pivoting are positively correlated, behavioral variables. This study seeks to and the time spent sinking-pivoting has no understand both black sea bass behavioral relationship to time spent resting (Figure 1A). changes in response to pile-driving sounds, as Swimming and hovering are positively well as black sea bass’s ability to habituate to correlated, while bobbing and resting are pile-driving sounds during exposure. The results negatively correlated (Figure 1A). Analyzing of this investigation provide support for a single behaviors concurrently in multivariate measurable behavioral response to pile-driving space can reveal aggregate behavioral patterns. sounds for black sea bass and that these changes Black sea bass’s sinking-pivoting or resting in behavioral patterns are strongest at the onset response to pile-driving sound exposure creates of sound exposure. a more detailed, mechanistic understanding of Changes in black sea bass behavioral “reduced activity,” which can inform more patterns in response to pile-driving sounds complex behaviors. For example, the sinking- Through pair-wise tests of pivoting and resting behaviors in response to discrimination between groups, this study finds pile-driving sounds could have evolutionary and significant to marginally significant differences ecological implications for black sea bass, in black sea bass behavioral patterns during including altered predator avoidance behavior, exposure to pile-driving sounds, compared to feeding behavior, and aggregation behavior behavioral patterns in control groups (no sound) necessary for spawning (Mercer, 1989). and before pile-driving sound exposure (Table Additionally, altered behavior could impact 2). Compared to black sea bass in control black sea bass fisheries directly by causing groups and before sound exposure, black sea changes in distribution in the water column, as bass tend to sink and pivot or rest during pile- well as indirectly through the evolutionary driving sound exposure (Figure 1). During consequences that could affect population sound exposure, black sea bass spend less time stability. bobbing than control and pre-exposure groups Evidence for decreased black sea bass (Figure 1). This sinking and pivoting behavior behavioral response during sound exposure is consistent with behavioral responses in other Black sea bass exhibit the strongest fish to anthropogenic noises. For example, behavioral response to pile-driving sounds exposure to moderate sound levels affect during the first minutes of sound exposure, and swimming behavior of zebrafish by changing these behavioral changes diminish throughout swimming speed and height (Neo et al. 2015). the 15-minute sound exposure segment Wild-caught Atlantic salmon swim down in (Supplemental Figure 3). During minute 1, response to pure tones (Knudsen et al., 1992), black sea bass exhibit a significantly distinct and European seabass swim faster, deeper, and sinking-pivoting or resting behavioral response away from pile-driving playback (Neo et al., to sound exposure (Supplemental Figure 3, 2016). However, these behavioral patterns Supplemental Table 1A). Additionally, their exhibit an opposite trend to rockfish behaviors. behavior becomes significantly less variable When exposed to louder sounds, rockfish spend during sound exposure during minute 1, more time in the upper two-thirds of enclosure compared to control and pre-exposure groups in controlled tank environments (Pearson, (Supplemental Table 1A). These same 1992). The sinking and pivoting or resting behavioral patterns are apparent during minute response to pile-driving sounds provide species- 5 but diminish by the end of the exposure by specific evidence for a behavioral trend of minutes 10 and 15 (Supplemental Figure 3). By reduced activity level. minutes 10 and 15, the behavioral patterns of

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black sea bass during sound exposure are no through habituation. Future black sea bass longer significantly distinguishable from other behavioral analyses should assess these control groups (Supplemental Table 1C-D). potential long-term behavioral responses. Although black sea bass exhibit high degrees of Study limitations overall behavioral concordance during the 15- This study has a few key limitations minute measurements in the pre-exposure, that constrain extrapolation to black sea bass exposure, and post-exposure treatments (Figure behavioral impacts in response to pile-driving in 2), black sea bass exhibit marginally the environment. The controlled experimental significantly greater overall changes in design by nature does not consider potential concordance between minute 1 and minute 5 interactive or antagonistic effects on behavior during sound exposure than in other groups that would be present in the environment. For (Figure 2). example, a multivariate analysis of humpback The diminished differences in whale behavioral response to anthropogenic behavioral patterns throughout sound exposure sound stimuli used a field experimental suggest a degree of black sea bass habituation to approach and considered the interactive effects pile-driving sounds. These findings are similar of environmental and whale social variables, to previous studies showing evidence for intra- finding multifaceted interactions and complex trial habituation. For example, after responding responses to acoustic stimuli (Dunlop et al., to intermittent sounds with altered behavior 2013). These multifaceted interactions will be including increased swimming speed, swimming critical for understanding the impact of pile- depth, and group cohesion, European seabass driving on black sea bass in the environment displayed reduced behavioral changes and cannot be fully understood in a controlled throughout exposure (Neo et al. 2015; Neo et al. setting. Additionally, black sea bass behavior is 2016). However, a decrease in response does generally variable, therefore complicating not necessarily denote habituation, where inferences of behavior changes in response to animals hear selectively while filtering out sound. For example, in minute 10 of controlled repeated or irrelevant sound signals (Rankin et experiments, the control black sea bass group al. 2009). The observed decrease in behavioral exhibited statistically significant increases in response can also be attributed to sensory behavioral variability in response to the no- adaptation, or reduced sensitivity of the hearing sound treatment (Supplemental Figure 3, organs, as well as motor fatigue, or Supplemental Table 1). Also, in the Procrustes unresponsiveness due to exhaustion (Domjan, analysis, all treatment groups contained large 2010). Future work should determine the residuals between minutes, indicating that black mechanism of behavioral recovery, since sea bass exhibit behavioral changes even in a different mechanisms vary in ecological controlled setting that cannot be attributed to implications. sound exposure (Figure 2). These changes in Due to a lack of sufficient replication behavior variability without acoustic stimulus suitable for a multivariate analysis, it is unclear underscore the challenges associated with fish if the black sea bass behavioral patterns behavioral analysis. Finally, although a changed throughout repeated daily exposures multivariate analytical approach can reveal (the 9 am, 11:30 am, and 2 pm exposure behavioral patterns that might underly more sessions). Most studies investigating behavioral complex behaviors, linking altered behavioral responses of fish to anthropogenic sound stimuli patterns to reduced evolutionary fitness remains investigate intra-exposure habituation, but it is elusive. uncertain how repeated and extended exposure will affect fish behavior. Some behavioral investigations have identified decreased Conclusions behavioral changes in fish after weeks of repeated sound exposures (Nedelec et al. 2016). As the industry for offshore wind However, there is broadly little evidence as to energy grows in the United States, it is critical whether repeated exposure sessions over long to investigate the full scope of offshore energy’s time periods cause behavioral responses to environmental impact. Development along the accumulate, leading to either stronger responses eastern seaboard will overlap with the range of through sensitization or diminished responses black sea bass, a fish species of ecological and commercial importance. This study investigates

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the behavioral response of black sea bass to Marine Science Laboratory, pile-driving sound patterns and frequencies, NOAA/NMFS/NEFSC Passive Acoustics finding altered behavioral patterns during sound Group, and Woods Hole Oceanographic treatment. Multivariate analyses indicate these Institution for providing the resources and changes in behavioral patterns underly reduced facilities for data collection. Finally, I would activity, including greater time spent sinking- like to thank Julian Olden for advice on pivoting or resting. Additionally, black sea bass statistical methods. display diminished behavioral response throughout exposure, although it is unclear if this reduction indicates habituation. Future References work should investigate how repeated and Allison, T. D., Diffendorfer, J. E., Baerwald, E. extended exposure will affect fish, and how F., Beston, J. A., Drake, D., Hale, A. M., behavioral response patterns are associated with Hein, C. D., Huso, M. M., Loss, S. R., changes in more complex behaviors, including Lovich, J. E. et al. (2019). Impacts to changes in migration, feeding and breeding Wildlife of Wind Energy Siting and grounds, or stress-induced reduction in Operation in the United States. Issues in reproductive output. The altered behavioral Ecology 21, 23. patterns evidenced by this investigation lay the Anderson, M.J. (2001). A new method for groundwork for future black sea bass research, nonparametric multivariate analysis of since behavioral responses to short- and long- variance. Aust. Ecol. 26, 32–46. term exposures to anthropogenic sound could Anderson, M.J., Ellingsen, K.E. and McArdle, have significant impacts on individual fish and B.H. Multivariate dispersion as a measure of entire populations, as well as the fisheries that beta diversity. (2006). Ecology letters 9(6), rely on them. 683-693. Spending estimates represent only plant Berton, O., Ramos, A., Chaouloff F., Mormède, material prices. The calculated retail costs of P. (1997). Behavioral Reactivity to Social the planted perennials may be overestimates. and Nonsocial Stimulations: A Multivariate Developers are probably more likely to get bulk Analysis of Six Inbred Rat Strains. Behavior discounts. I may not have been able to detect Genetics. 27, 155-166. some of the plants that homeowners removed. I Bruintjes, R. and Radford, A. N. (2013). originally planned to include mulch, gravel, Context-dependent impacts of anthropogenic cobble, or rock in my estimates of patch prices. noise on individual and social behaviour in a Quoted prices from landscaping firms and rock cooperatively breeding fish. Animal sources were too variable to reliably estimate Behaviour 85, 1343-1349. price. Factors influencing quoted prices Domjan, M. (2010). The Principles of Learning included parent material for rock walls and and Behaviour (sixth ed.), Wadsworth, boulders, finer size and shape criteria than I had Cengage Learning, Belmont, CA recorded for gravel and cobble, minimum Gillman, P. and Golladay, J. (2016). National quantity purchase requirements, and transport Offshore Wind Strategy: Facilitating the costs. Estimated prices may be higher than Development of the Offshore Wind Industry when the landscapes were installed in 2014 or in the United States. U.S. DOE & U.S. DOI. 2015. Pg. 1-84. Gower, J.C. (1971). Statistical methods of Acknowledgements comparing different multivariate analyses of the same data. In: Hodson FR, Kendall DG, I would like to thank Katharine Shelledy for Tautu P (eds) Mathematics in the access to the data used in the analysis. K. archaeological and historical sciences. Shelledy conducted all data collection and Edinburgh University Press, Edinburgh, pp contributed to the information provided in the 138–149. introduction, methods, and discussion of this Herbert-Read, J. E., Kremer, L., Bruintjes, R., paper. I would also like to thank Dr. Beth Radford, A. N. and Ioannou, C. C. (2017). Phelan and Dr. Jenni Stanley for Anthropogenic noise pollution from pile- conceptualizing the research plan and study driving disrupts the structure and dynamics design, as well as NOAA/NMFS/NEFSC of fish shoals. Proceedings of the Royal Fisheries Ecology Branch at James J. Howard Society B: Biological Sciences 284.

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Holles, S., Simpson, S. D., Radford, A. N., European seabass. Biological Conservation. Berten, L. and Lecchini, D. (2013). Boat 178, 65-73. noise disrupts orientation behaviour in a Neo, Y.Y., Ufkes, E., Kastelein, R.A., Winter, coral reef fish. Marine Ecology Progress H.V., ten Cate, C., Slabbekoorn, H. (2015). Series 485, 295-300. Impulsive sounds change European seabass Jackson, D.A. (1995). PROTEST: a Procrustean swimming patterns: Influence of pulse randomization test of community repetition interval. Marine Pollution environment concordance. Écoscience 2, Bulletin. 97(1-2), 111-117. 297–303. Oksanen, Jari. (2018). Vegan: Ecological Knudsen, F.R., Enger, P.S., Sand, O. (1992). Diversity. R Package Version 2.5-2. Awareness reactions and avoidance https://cran.rproject.org/web/packages/vegan responses to sound in juvenile Atlantic /index.html. salmon, Salmo salar. Journal of Fish Pearson, W.H., Skalski, J.R., Malme, C.I. Biology. 40(4), 523-534. (1992). Effects of Sounds from a Langhamer, O. (2012). Artificial reef effect in Geophysical Survey Device on Behavior of relation to offshore renewable energy Captive Rockfish (Sebastes spp.). Canadian conversion: state of the art. The Scientific Journal of Fisheries and Aquatic Sciences. World Journal. 386713- 386713. 49, 7. Mardia, K.V. (1974). Applications of some Popper, A. N. and Hastings, M. C. (2009). The measures of multivariate skewness and effects of anthropogenic sources of sound on kurtosis in testing normality and robustness fishes. Journal of Fish Biology 75, 455 – studies. Sankya: The Indian Journal of 489. Statistics. 36(2): 115-128. Rankin, C.H., Abrams, T., Barry, R.J., McGarigal, K. (2009). R. package. Chatnagar, S., Clayton, D.F., Colombo, J., https://www.umass.edu/landeco/teaching/eco Coppola, G., Geyer, M.A., Glanzman, D.L., data/labs/biostats.pdf Marsland, S., McSweeney, F.K., Wilson, Mercer, L. P. (1989). Species profiles: life D.A., Wu, C-F., Thomson, R.F. (2009). histories and environmental requirements of Habituation revisited: an updated and coastal fishes and invertebrates (South revised description of the behavioral Atlantic): black sea bass. Coastal Ecology characteristics of habituation. Neurobiol. Group, U.S. Army Corps of Engineers, Learn. Mem. 92, 135-138. Waterways Experiment Station. Simpson, S. D., Radford, A. N., Nedelec, S. L., Nedelec, S.L., Mills, S.C., Lecchini, D., Ferrari, M. C. O., Chivers, D. P., Nedelec, B., Simpson, S.D., Radford, A.N. McCormick, M. I. and Meekan, M. G. (2016). Repeated exposure to noise increases (2016). Anthropogenic noise increases fish tolerance in a coral reef fish. Environmental mortality by predation. Nat Commun 7. Pollution. 216, 428-436. Spiga, I., Aldred, N. and Caldwell, G. S. (2017). Neo, Y.Y., Hubert, J., Bolle, L., Winter, H.V., Anthropogenic noise compromises the anti- ten Cate, C., Slabbekoorn, H. (2016). Sound predator behaviour of the European seabass, exposure changes European seabass Dicentrarchus labrax (L.). Marine Pollution behaviour in a large outdoor floating pen: Bulletin 122, 297-305. Effects of temporal structure and a ramp-up Stanley, J. A., Van Parijs, S. M. and Hatch, L. procedure. Environmental Pollution. 214, T. (2017). Underwater sound from vessel 26-34. traffic reduces the effective communication Neo, Y.Y., Parie, L., Bakker, F., Snelderwaard, range in Atlantic cod and haddock. Scientific P., Tudorache, C., Schaaf, M., Slabbekoorn, Reports 7, 14633. H. (2015). Behavioral changes in response to Stanley, J.A., Caiger, P.E., Phelan, B., Shelledy, sound exposure and no spatial avoidance of K., Mooney, T.A., Van Parijs, S.M.V. noisy conditions in captive zebrafish. Front. (2020). Ontogenetic variation in the hearing Behav. Neurosci. 9, 28. sensitivity of black sea bass (Centropristis Neo, Y.Y., Seitz, J., Kastelein, R.A., Winter, striata) and the implications of H.V., ten Cate, C., Slabbekoorn, H. (2016). anthropogenic sound on behavior and Temporal structure of sound affects communication. Journal of Experimental behavioural recovery from noise impact in Biology. 223.

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Steimle, F. W., Zetlin, C. A., Berrien, P. L. and Chang, S. (1999). Essential Fish Habitat Source Document: Black Sea Bass, Centropristis striata, Life History and Habitat Characteristics. In NOAA Technical Memorandum NMFS-NE-143, pp. 50: National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, Woods Hole, Massachusetts. Thomsen, F., Ludemann, K., Kafemann, R. and Piper, W. (2006). Effects of offshore wind farm noise on marine mammals and fish, biola, Hamberg, Germany on behalf of COWRIE Ltd. Voellmy, I. K., Purser, J., Flynn, D., Kennedy, P., Simpson, S. D. and Radford, A. N. (2014). Acoustic noise reduces foraging success in two sympatric fish species via different mechanisms. Animal Behaviour 89, 191-198. .

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Supplementary Figures

Supplemental Figure 1: Diagram of tank experimental set up. Each control and experimental treatments were performed in a small tank with three black sea bass. Figure courtesy of K. Shelledy.

Supplemental Figure 2: Schematic displaying the experimental set-up. Groups of three black sea bass individuals were separated into 21 small tanks. Thirteen tanks received sound treatment, with 6 receiving 3 treatments and 7 receiving 2 treatments. Within each session, the fish were recorded for 15 minutes before sound exposure, during sound exposure, and after sound exposure. During each 15-minute segment, fish behavior was recorded for the three fish at four one-minute time intervals. In each minute, the number of seconds spent exhibiting the above seven behaviors was recorded.

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Supplemental Figure 3: Behavioral time budget ordination based on principal component analysis for each one-minute segment within the 15-minute recording segment. A. Ordination of the 167 behavioral time budgets in multivariate space for minute 1. The observations are color-coded by group, and 95% confidence ellipses are included. B. Behavioral variable loadings on PC axes 1 and 2 for minute 1. The length of the vectors indicates the strength of the associated variable for describing the PCs. The direction of the vectors indicates the direction of the associated variable gradients in ordination space. C. Ordination of the 167 behavioral time budgets in multivariate space for minute 5. D. Behavioral variable loadings on PC axes 1 and 2 for minute 5. E. Ordination of the 167 behavioral time budgets in multivariate space for minute 10. F. Behavioral variable loadings on PC axes 1 and 2 for minute 10. G. Ordination of the 167 behavioral time budgets in multivariate space for minute 15. H. Behavioral variable loadings on PC axes 1 and 2 for minute 15.

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Supplemental Table 1: Results of pair-wise perMANOVA tests and pair-wise multivariate dispersion tests between six groups for each minute within the 15-minute recording segment. Bottom diagonals in A-D show the results of pair-wise perMANOVA tests between the six groups. Each cell contains the F-statistic and the associated p-value in parenthesis. Upper diagonals in A-D show the results of pair-wise multivariate tests of dispersion between the six groups. Each cell contains the t-statistic and the associated p-value in parenthesis. Significant and marginally significant test statistics are bolded (p-value < 0.05**; p-value < 0.10*). A. Minute 1. B. Minute 5. C. Minute 10. D. Minute 15

A. Minute 1 Treatment Control: Contro Control: Treatme Treatment: : Pre- l: Post- nt: Post- Pre- Exposure Exposure Exposure Exposure Exposure Exposure Control: -0.16 0.74 0.23 1.89 0.03

Pre-Exposure (0.891) (0.526) (0.807) (0.075)* (0.976) Control: 0.23 -0.79 0.06 1.69 -0.14

Exposure (0.827) (0.418) (0.943) (0.095)* (0.887) Control: 0.03 0.26 0.89 2.65 0.74 Post- (0.993) (0.787) (.350) (0.012)** (0.470) Exposure Treatment: 0.36 0.85 0.23 -1.68 0.22

Pre-Exposure (0.709) (0.420) (0.831) (0.094)* (0.824) Treatment: 3.67 3.60 3.12 2.74 -2.04

Exposure (0.026)** (0.046)** (0.047)** (0.039)** (0.053)* Treatment: 2.11 2.81 1.81 1.15 1.02 Post- (0.134) (0.081)* (0.161) (0.257) (0.296) Exposure

B. Minute 5 Treatment Control: Contro Control: Treatme Treatment: : Pre- l: Post- nt: Post- Pre- Exposure Exposure Exposure Exposure Exposure Exposure Control: 1.08 0.11 -0.32 1.21 0.22

Pre-Exposure (0.263) (0.900) (0.742) (0.220) (0.819) Control: 0.15 0.97 0.75 2.34 1.623

Exposure (0.890) (0.337) (0.425) (0.021)** (0.218) Control: 0.34 0.25 -0.21 1.32 -0.54 Post- (0.719) (0.769) (0.832) (0.184) (0.768) Exposure Treatment: 0.58 0.35 1.05 -1.60 -0.57

Pre-Exposure (0.542) (0.715) (0.364) (0.092)* (0.582) Treatment: 1.95 2.58 3.59 2.21 -0.96

Exposure (0.171) (0.080)* (0.035)** (0.094)* (0.339) Treatment: 2.14 2.49 4.12 2.16 0.84 Post- (0.131) (0.071)* (0.032)** (0.103) (0.381) Exposure

C. Minute 10 Control: Contro Control: Treatment Treatme Treatment: Pre- l: Post- : nt: Post- Exposure Exposure Exposure Pre- Exposure Exposure

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Exposure

Control: 1.83 -0.28 0.37 0.65 -0.05

Pre-Exposure (0.067)* (0.778) (0.698) (0.534) (0.959) Control: 0.14 2.10 2.18 2.23 2.05

Exposure (0.887) (0.038)* (0.031)* (0.027)* (0.048)* Control: 0.33 0.61 0.11 0.43 -0.38 Post- (0.724) (0.521) (0.910) (0.696) (0.696) Exposure Treatment: 0.48 0.29 1.66 -0.34 0.48

Pre-Exposure (0.623) (0.755) (0.185) (0.724) (0.611) Treatment: 1.79 0.96 2.87 2.22 -0.80

Exposure (0.158) (0.387) (0.073)* (0.073)* (0.452) Treatment: 1.37 0.72 2.63 1.12 0.46 Post- (0.251) (0.457) (0.079)* (0.249) (0.592) Exposure

D. Minute 15 Treatment Control: Contro Control: Treatme Treatment: : Pre- l: Post- nt: Post- Pre- Exposure Exposure Exposure Exposure Exposure Exposure Control: 0.32 0.39 -0.21 0.09 -0.38

Pre-Exposure (0.748) (0.850) (0.830) (0.919) (0.720) Control: 0.48 0.18 0.16 0.43 -0.04

Exposure (0.623) (0.863) (0.863) (0.668) (0.959) Control: 0.09 0.39 -0.02 0.29 -0.22 Post- (0.936) (0.684) (0.979) (0.763) (0.821) Exposure Treatment: 0.66 0.21 0.70 -0.32 0.22

Pre-Exposure (0.505) (0.850) (0.450) (0.732) (0.827) Treatment: 3.54 2.15 3.99 2.08 -0.50

Exposure (0.053)* (0.126) (0.035)** (0.083)* (0.599) Treatment: 1.77 0.79 1.95 0.49 0.65 Post- (0.167) (0.444) (0.143) (0.597) (0.430) Exposure

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Article

Assessing the impact of stand thinning on restoration of old-growth forest characteristics

Kavya Pradhan Department of Biology, University of Washington Email: [email protected]

Received December 2020; accepted in revised form January 2021; published May 2021

Abstract

Late-successional coniferous forests are in decline and expected to be vulnerable to climate change. Forest restoration practices to accelerate the growth of late-successional forests are often utilized in restoration and management. Variable density thinning is one such method that has been shown to improve tree growth and understory development. To understand the efficacy of this method in restoring coastal mesic forests, The Nature Conservancy established a watershed scale experiment in young managed forests in the Ellsworth Creek Preserve. We examined the presence-absence of plant species, understory cover, and basal area increment ~10 years after first application of thinning. Using canonical correspondence analysis, we found that environmental variables explain 32.39 %, 33.13 %, and 56.55 % variation in species presence-absence, understory cover and basal area increment respectively. We also found that species presence-absence and understory cover are significantly correlated with one another. Additionally, stand age stood out as an important contributor to the constrained ordination. Finally, there was a significant difference in the species presence-absence between thinned and control (unthinned) stands. Overall, our study suggests that forest restoration using stand-thinning leads to changes in plant community composition and that forest characteristics vary by stand age.

Introduction The structural and compositional complexity of these late-successional forests contributes Late-successional forests, including old- heavily towards their functions which includes growth forests, are incredibly important for providing habitat for increased biodiversity their capacity in microclimatic buffering (Frey (Franklin and Van Pelt 2004). However, et al. 2016) and carbon storage (Luyssaert et al. increased vulnerability of coniferous forests to 2008), including in the soil (Zhou et al. 2006). climate change (Seidl et al. 2017) combined

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with declines in old growth (Laurance et al. three things in different areas of the forest: 2012) and late-successional forests (Davis et al. removing a certain percentage of trees, creating 2015) have increased concern for their gaps, or skipped over in thinning. A further step resilience. As such, forest restoration practices can be taken by varying the proportion of trees are commonly used by managers in response to removed based on characteristics of the stand declining forest resilience (Spies et al. 2010). prior to thinning (eg, greater percentage of trees removed from denser stands). VDT enhances Restoration efforts in the mesic temperate spatial and structural heterogeneity and has forests in the Pacific Northwest (PNW) have been shown in increase basal area growth focused on speeding up succession to promote (Roberts and Harrington 2008). the development of late-successional forest characteristics in previously logged secondary Despite support for thinning as a method to forests. Young managed forests are hasten late-successional characteristics, regional characterized by closed canopy, homogenous and local management agencies need to tree age and structure, and low understory carefully consider how local environmental and species diversity. In contrast, late successional historical conditions might alter the ultimate forests are characterized by spatially and outcomes of any management intervention. The vertically heterogenous forests (Franklin and impacts of management through stand thinning Van Pelt 2004) and higher understory species have been mostly addressed in drier, interior diversity and abundances (Halpern and Spies forests (for example the ICO method; Churchill 1995). et al. 2013). Experimental studies in coastal coniferous forests, have revealed valuable One commonly used method aimed at insight (Roberts and Harrington 2008, Roberts conservation of iconic temperate rainforests and Harrington 2010), but due to the small involve managing stand density by thinning, number of such experiments, determining the i.e., removing a certain proportion of trees to most effective application of VDT is still up for alter the density of the forested stand. Although debate. This is particularly true for our study there are multiple methods used for region in the Willapa Hills of Washington state, accomplishing thinning, in general it is thought USA, where the wind regimes (strong coastal to improve carbon storage capacity (Luyssaert et winds) can lead to potential negative impacts of al. 2008) and ensure the persistence of late VDT due to wind damage (though see Roberts successional habitat and biodiversity (Spies et et al. 2007). al. 2010). Experimental studies have shown that thinning can lead to increased growth of adult To assess the efficacy of VDT on forest and young trees (Sullivan et al. 2006) and restoration in coastal mesic forests, The Nature seedling regeneration (Ares et al. 2010). In Conservancy has conducted a series of addition to changes to the overstory, studies vegetation and biodiversity pretreatment have also shown an increase in understory cover surveys and applied restoration treatments in (Ares et al. 2010). Beyond impacts on the plant 2008-2012 to young managed forests in the community, thinning has been linked to impacts Ellsworth Creek Preserve. A subset of these on other organisms; this includes a short-term sites was re-surveyed in summer 2020 providing increase in chantarelle productivity (Pilz et al. us with a timely opportunity to assess their 2006), increased habitat use by rodents effect on forest development and biodiversity. (Ransome et al. 2004) and ungulates (Sullivan In this study, we assessed et al. 2007), and increased diversity of breeding 1) the association between plant community songbirds (Hagar et al. 2004). Although these diversity, understory cover, and basal area studies and others indicate that thinning has increment and environmental conditions, and positive effects on forest structure and habitat 2) the impact of VDT on the same availability/use, variable density thinning characteristics. (Carey 2003) has emerged as a common Our analysis indicates that while both abiotic operationalization of the thinning technique. In and human-influenced environmental conditions contrast to other thinning methods, variable impact forest characteristics, stand age is an density thinning (VDT) is used to create non- important determinant. More importantly for the uniform forests by doing one of the following aims of this experiment, differences between

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control and thinned stands was evident for To assess the impact of management species presence-absence. intervention on forest characteristics, permanent monitoring plots (17.8 m radius circular plots; Fig 1b) were established and surveyed (2006 Methods and 2007) within each sub-basin prior to the

implementation of the experiment. A subset of Study site and experimental treatments these sites were resurveyed in 2020 (Fig 1).

Within each plot, diameter at breast height The Ellsworth Creek Preserve (ECP) is a 33 (DBH) for all trees with DBH > 5.7” was km2 coastal forest owned by The Nature measured. We then converted DHB to basal area Conservancy in southwestern Washington, (basal area = 0.005454 x (DBH)2 where DBH is USA. Although there are patches of old-growth measured in inches) for both pre- and post- stands containing western red cedar (Thuja treatment surveys for each species present at the plicata), Sitka spruce (Picea sitchensis), site and used these values to compute basal area Douglas fir (Pseudotsuga menzesii) and western increment (BAI). Subplots (2.5 m radius at 9 m hemlock (Tsuga heterophylla), a large part of from plot center) were also established in each the preserve consists of younger and denser cardinal direction where seedling tally (height forests that were, until recently, managed between 5.9” and 4.5’), sapling tally (trees with primarily for timber production. By 2001, only height >= 4.5’ and < 5.7” DBH), and percent 7% of the old-growth stands in the ECP basin cover for each species in the understory remains standing while greater than 16% of the (including seedlings that are < 4.5’) were basin has been logged twice (Churchill et al. measured using visual estimates within the 2007; Powell et al. 2003). The regional climate subplot. Plot level plant community consists of generally cool wet winters (6 ºC, composition (presence-absence of species was 1202.9 mm) and warm dry summers (14.3 ºC, derived by combining tree, seedling, sapling, 153.4 mm) with mean annual temperature of and understory species occurrence information. 10.1 ºC and average annual precipitation of

2845.0 mm according to 1981-2010 climate Human influence variables normal based on data from the nearby Long

Beach Experiment Station (NOAA). To understand the impact of management As part of as part of the Ellsworth (i.e., experimental treatment) and stand history Creek Adaptive Management Study, TNC has on forest characteristics, we included human implemented landscape scale experimental influence variables in our analysis. Stand age is treatment to 2,040 ha within the broader related to a variety of forest characteristics and preserve (Churchill et al. 2007). The treatments function (Jonsson et al. 2020). Since land units were implemented in an unbalanced randomized within ECP were previously logged and block design. For this analysis, we will be replanted, age of the forest stands may influence focusing on four of the eight experimental sub- responses to treatment. As such, we collected basins in the northern and central portions of information of stand ages in addition to the experimental region (Fig 1a). These sub- information on whether the sites were actively basins contain sites that have been surveyed managed (Thinned) or left without management pre- and post-treatment allowing us to intervention (Control). We note here that effectively assess the impacts of the treatment was spread across stand ages, such management intervention. Two (one northern that and one central) of the four stands were actively managed. These stands were subjected to VDT Environmental data based on stand density (on average 30% thinning) and treatments were applied from To explore the relationship between plant 2008 to 2012. The other two were control stands communities and environmental conditions we where no management intervention was used a combination of climatic, topographic, implemented. and edaphic variables. Climate and edaphic

variables were obtained from data assembled for Vegetation data use in the BloomFinder project

(http://bloomfinder.org/). Temperature and

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precipitation associated climate variables were were interested in the joint influence of stand- obtained from ClimateNA with 1km Delta- thinning and stand age, we also conducted a downscaling using GWR (Wang et al. 2016). two-way perMANOVA followed by a This includes accumulated annual degree days multivariate homogeneity of group dispersions above 5°C (DD5), temperature seasonality, (Anderson 2006). We used chi-squared climate moisture deficit (CMD), and mean distances as the association coefficient for both annual precipitation (MAP). Annual potential of these analyses for species presence-absence, solar radiation (RAD) was computed using understory cover, and basal area increment. We GRASS GIS (Scharmer et al. 2000). Edaphic opted to use chi-squared distances since this is variables included in the analysis were soil the resemblance metric used in CCA and we probability of bedrock (BDR), and soil organic wanted the analyses to be comparable. carbon, surface soil PH in H2O (pH), USDA soil class (TUS) and percent sand in soil from SoilGrids (Hengl et al. 2017). Topographic Results variables (SLOPE, ASPECT, and topographic position index or TPI) from LIDAR data Correlation amongst forest characteristics collected in 2007 for ECP were computed in R. Pairwise Mantel test between the chi-squared Analyses distances of species presence-absence and understory abundance indicates significant All analyses were done in R (v3.6.1, R Core correlation (r = 0.352, p = 0.001). However, the Team 2020) using the vegan package (Okansen correlation between BAI and species presence- et al. 2019). First we assessed the absence (r = -0.1385, p = 0.906), and BAI and correspondence amongst the three response understory abundance (r = -0.1235, p = 0.884) matrices (species presence-absence, understory are not significant. cover, and BAI). To do this, we conducted pairwise mantel tests using chi-squared Environmental variables explain forest distances to maintain consistency with the characteristics above analyses. We then conducted constrained All environmental variables explained ordinations to determine the correspondence 32.39% variation in the species occurrences between the vegetation within these plots, and data (p < 0.001). Of the variables included in environmental and human influence variables. the model CMD (p < 0.01), DD5 (p < 0.01), TPI We used Canonical Correspondence Analysis (p < 0.05), ASPECT (p < 0.001), AGE (p < (CCA) as this method is appropriate when we 0.001) and THINNING (p < 0.01) contributed have unimodal objects and descriptors. significantly. Of these variables, TPI, AGE, and However, CCA can produce biased results when THINNING were negatively loaded on axis1 this assumption is violated; to ensure this is not while CMD, DD5, and ASPECT were positively the case, we conducted a gradient analysis loaded (Fig2.a). All of the variables loaded which supported our use of CCA (see positively on axis2 except TPI which loaded APPENDIX AI). The three forest characteristics slightly negatively on axis 2. AGE and of interest, we conducted CCAs separately and THINNING were positively associated with one combined the abiotic and human variables another and with ACCI (Acer circinatum), together to create a single matrix for the HYRA3 (Hypochaeris radicata), RILA3 (Ribes predictor variables. Due to low species richness laxiflorum), ERMI6 (Erechtites minima). at these sites we retained all species regardless With regard to understory cover, of rarity. 31.33% (p < 0.01) of the variation was To understand the short-term impact of explained by environmental conditions with stand-thinning on forest characteristics, we DD5 (p < 0.01), TPI (p < 0.05), ASPECT (p < employed perMANOVA (Anderson 2001) to 0.05), and AGE (p < 0.01) as significantly assess if there are indeed multivariate contributing variables. In addition, SLOPE (p = differences in species occurrences, understory 0.075) and THINNED (p = 0.065) were species cover, and basal area increment with marginally significant. Aspect and DD5 are treatment as the grouping variable. Since we positively associated with one another and positively associated with axis2. Both AGE and

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TPI are positively associated with axis1 but not An examination of the thinned and control strongly with one another. sites in multivariate space revealed that there Finally, the constraining environmental was a difference (p < 0.05, pseudo F = 1.3537) variables explain 56.55% (p < 0.001) of the between the two based on perMANOVA. A variation in the Basal Area Increment across further examination testing the multivariate years. MRA (p < 0.05) and PH (p < 0.01) were homogeneity of group dispersions revealed that positively associated with one another and this difference this was not due to variation in PSME (Pseudotsuga Menzeisii) and SALIX dispersions between the two groups (Table 1). (Salix sp.). While BDR (p < 0.01) was In contrast, there are no significant differences associated with PISI (Picea sitchensis). between either the centroids or the dispersions between thinned and control sites with reference Thinning impacts on forest characteristics to understory cover and BAI.

Table 1. F statistic for permutational multivariate analysis of variance (upper diagonal) and test of multivariate homogeneity in dispersion (lower diagonal) for three forest characteristic variable with respect to whether the sites were thinned or control sites.

Thinned Control Species presence- Thinned 1.3537* absence Control 0.054 Understory Thinned 1.3854 cover Control 0.749 Basal Area Thinned 1.2513 Increment Control 0.6265 * p <0.05

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Figure 1. Vegetation survey sites withing Ellsworth creek watershed. A) Sites that have been surveyed twice within basins that have been actively managed with VDT (Thinned) and basins that were left as control (Control). b) The set-up for permanent monitoring plots where vegetation data was collected. The grey circle indicates plot area where tree species presence-absence and diameter at breast height were collected. White circles designate 4 subplots in each cardinal direction where understory percent cover, and seedling and sapling tallygetValues(x) were recorded.

425000 49

48

47

420000 46

-125 -123 -121 -119 -117

415000

410000

405000 Thinn ed a Contr b . ol . 400000 5 km

770000 775000 780000 785000 790000

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Figure 2. Ordination plots for the canonical correspondence analysis of species occurrences with environmental conditions as the constraining variables. Sites are represented either by grey dots (Control sites) or black triangles (Thinned sites). Predictor variables (a) are represented by arrows with blue 3arrows highlighting the variables that significantly3 contribute to species occurrences. Tree species (b) and understory speciesEQUIS (c) are shown with red text. Note that c) understoryEQUIS species are denoted by 2species codes and their full scientific and common2 names are presented in Appendix AII

ACCI MIDE3 ACCI MIDE3 HYRA3 OXALI GATR3 HYRA3 OXALI GATR3 1 3 RIBR 1 3 RIBR RILA3 ERMI6SARA2 TROV2 RILA3 ERMI6SARA2 TROV2 a TRT TRT RUAR9 SEJA RUPA VACCI RUBUS RUAR9SEJAb RUPA VACCI RUBUS PACKECAREX)RULA VIOLAMEFE RUSP DREX2 CON PACKECAREX) RULAVIOLAMEFE RUSP DREX2 CON 0 0 AGROS2 DIPU BLSP CLSI2 THIN AGROS2 DIPU BLSP CLSI2 THIN 2 AGE ATFIGASH 2 ATFIGASH BOOC2 VAOV2 POMU BOOC2 VAOV2 POMU CCA 2 (4.1 %) (4.1 2 CCA TITR CMD %) (4.1 2 CCA TITR MOUN2 VAOV slope MOUN2 VAOV VAPA VAPA -1 MADI PROSA -1 MADI PROSA THINNEDPTAQ PTAQ ILAQ801 RUDI2 DD5 1ILAQ80 RUDI2 -2 -2 TRT Picea sitchensis TRT ASPECT Thuja plicata CON Tsuga heterophylla CON 0 PSL_TUS 0 -2 -1 0 12 -2THIN -1Frangula 0 purshiana12 THIN CCA 1 (5.8TPI %) PSL_PHO CCA 1 (5.8 %) PSL_SNDPCL_MRA CCA 2 (4.1 %) (4.1 2 CCA %) (4.1 2 CCA Alnus rubra PSL_BDR Pseudotsuga menzesii -1 -1 ELEV PCM_TD PCM_MAP

3 Salix -2 PSL_CAR -2 EQUIS 2 -2 -1 0 12 -2 -1 0 12 ACCI MIDE3CCA 1 (5.8 %) CCA 1 (5.8 %) HYRA3 OXALI GATR3 1 3 RIBR RILA3 ERMI6SARA2 TROV2 RUAR9SEJAc RUPA VACCI TRT RUBUSEQUIS PACKECAREX)RULA VIOLAMEFE RUSP DREX2 CON 0 2 AGROS2 DIPU BLSP CLSI2 THIN ATFIGASH BOOC2 VAOV2 POMU CCA 2 (4.1 %) (4.1 2 CCA TITR MOUN2 VAOV ACCI MIDE3 -1 MADI VAPA PROSAHYRA3 OXALI GATR3 1 PTAQ RIBR ILAQ80 RILA3RUDI2 ERMI6SARA2 TROV2 RUPA TRT -2 RUAR9SEJA VACCI RUBUS PACKECAREXRULAVIOLAMEFE RUSP DREX2 CON 0 -2AGROS2 -1 0 DIPU12BLSP CLSI2 THIN ATFIGASH BOOC2CCAVAOV2 1 (5.8 %) POMU CCA 2 (4.1 %) (4.1 2 CCA TITR MOUN2 VAOV VAPA -1 MADI PROSA PTAQ ILAQ80 RUDI2

-2

-2 -1 0 12 CCA 1 (5.8 %)

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Figure 3. Canonical correspondence analysis ordination plots for understory cover (a and b) and basal area increment (c and d). For all plots, sites are represented by the shapes (grey circles for control and 5.0 black triangles for thinned sites). Constraining5.0 variables are shown with arrows (a and c) where blue arrows represent variables that significantly contribute to the CCA. Species are shown in red text (b and d). Note that in b) understory species are denoted by codes (see AII for scientific names).

2.5 4 2.5 4 a b TRT TRT ) PSL_CAR ) Frangula purshiana CON Frangula purshiana CON Salix Thinned THIN Salix THIN Thuja plicata ThujaILAQ80 plicata 2 Alnus rubra slope 2 Alnus rubra CCA 2 (8.8 %) (8.8 2 CCA CCA 2 (8.8 %) (8.8 2 CCA 0.0 0.0 Pseudotsuga menzesiiASPECTPSL_PHO TPI Pseudotsuga menzesii EQUIS Tsuga heterophylla ACCITsugaFRPU7 heterophyllaRULA Picea sitchensis TRT TROV2 PiceaVAOV sitchensisGASH RUPA HYRA3 TRT PCM_CMDDD5 CONBOOC2 RUBUSRUDI2 RUAR9 RILA3 CON PCM_TD PACKE 0 0THIN ATFI ERMI6 SEJA CAREX THIN PSL_BDR -2.5 PCM_MAP -2.5 POMU RUSP VIOLAAGROS2OXALI MEFE

CCA 2 (5.0 %) (5.0 2 CCA %) (5.0 2 CCA BLSP PCL_MRA VAPA TITR VAOV2 SARA2 -2 0 ELEV 2 -2 0 MOUN2 2 CCA 1 (38.6 %) AGE MIDE3CCA 1CLSI2 (38.6 %) VACCI RIBR PROSA GATR3 PTAQ MADI -2 -2 DIPU 5.0 5.0 DREX2 PSL_SND

-2 0 24 -2 0 24 CCA 1 (7.2 %) CCA 1 (7.2 %) 2.5 2.5 5.0 5.0 c TRT d TRT ) ) Frangula purshiana CON Frangula purshiana CON Salix Thuja plicata THIN Salix Thuja plicata THIN Alnus rubra Alnus rubra CCA 2 (8.8 %) (8.8 2 CCA 0.0 %) (8.8 2 CCA 0.0 Pseudotsuga menzesii Pseudotsuga menzesii 2.5 Tsuga heterophylla 2.5 Tsuga heterophylla Picea sitchensis TRT Picea sitchensis TRT PSL_CAR PCM_DD5 CON Frangula purshiana CON slope RAD PCM_CMD THIN Salix Thuja plicata THIN -2.5 aspect -2.5 Alnus rubra CCA 2 (8.8 %) (8.8 2 CCA 0.0 AGE %) (8.8 2 CCA 0.0 -2 0 pH 2 tpi_30 BDR -2Pseudotsuga 0 menzesii 2 Thinned Tsuga heterophylla CCA 1 (38.6 %) PCM_TD CCA 1 (38.6 %) Picea sitchensis ELEVMAP

PSL_SND -2.5 -2.5 -2 0 2 -2 0 2 CCA 1 (38.6 %) CCA 1 (38.6 %)

explained by abiotic and human-influence Discussion variables. Of all the environmental factors we included in our analysis, stand age emerged as Our study examined three aspects of forest an important factor in explaining variation in all characteristics in a coastal forest and revealed three characteristic we assessed. Interestingly that variation in forest characteristics can be

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BAI was not correlated with either species this, studies on basal area increment have also presence-absence or understory cover shown that older versus younger stands respond suggesting that different forest characterizes can to thinning differently; in general, thinning vary independently of one another. Finally, applied to young stands show more increase in thinned stands were different from control growth than older stands though there is a lack stands with regard to species composition. In of consensus (Roberts and Harrington 2008). total, these results indicate that management practices have altered forest characteristics. Thinning impacts species presence-absence, but not other forest characterisitcs Impacts of abiotic conditions on forest VDT, as applied to ECP, was a significant characteristics factor in determining the variation in species Temperature and moisture associated presence-absence and understory cover (to a variables were important in explaining the smaller extent). Thinned sites tended to be more variation in these forests, although the variables associated with Thuja plicata for tree species. differed amongst the different characteristics. Thinning was also positively associated with Species presence-absence was associated higher cover of horsetail (EQUIS; Equisetum significantly with both types of climatic spp.), Salal (GASH; Gaultheria shallon), and variable (climatic moisture deficit and degree holly (ILAQ80; Ilex aquifolium). However, days above 5 ºC) as well as topographic presence of some introduced species position. For understory cover, while degree (Hypochaeris radicat and Erechtites minima) days above 5 ºC was important, aspect and were also associated positively with thinning. topographical position was more so. Radiation Similar observations of higher introduced and mean annual precipitation was important for species richness in thinned stands have been basal area increment indicating that energy and made previously and is a potential negative moisture availability impact how fast a tree can impact of thinning that can be accelerated (Ares grow. In addition to climatic variables, aspect et al. 2009). was also important in explaining variation in When we compared thinned sites to forest composition and growth such. Although control sites, they were only different when abiotic variables were important for describing viewed through the lens of species presence- the forest characteristics, there was still absence, we did not observe multivariate variation unaccounted for. This could be differences in either understory cover or basal attributed to variable we did not include in the area increment. An important factor for this analysis. In particular, since these forests have might be the time since the thinning treatments been impacted by human influence as they were that have been applied. For instance, previous used primarily for timber production, other studies have shown differences in basal area human-influence variables in addition to stand- responses with time since thinning such that age and thinning would be important to use in basal area increase differences may not be future analyses. quantifiable until more time since thinning (Roberts and Harrington 2008). As such, some Stand age influences forest characteristics forest characteristics changes may take longer Stand age was an important variable for all than others to realize with thinning treatments. three forest characteristics and contributed the Another factor that potentially affected our most to the first two canonical correspondence results is the differences in thinning intensity axes. Stand age was consistently positively that was applied to each stand. Thinning associated with Thuja plicata with regard to treatments at ECP were applied based on stand both presences and basal area increment. Stand density index and varied throughout the age is often related to forest characteristics Ellsworth Creek Watershed. We did not where younger forests are more productive and consider the variation within the thinning homogenous while older forests have higher treatments in this analysis, but further species diversity and spatial heterogeneity. This exploration of this dataset that includes explicit variation is shown by studies that indicate that information regarding the manner of thinning species richness and understory abundances has has potential to reveal more nuances regarding been shown to vary with stand age (Jules et al. the impact of thinning on the development of 2008, Halpern and Spies 1995). In addition to late-successional characteristics within these

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forests. In addition to this, while the results of in coniferous stands. Applied Vegetation our study indicate that thinning impacts species Science, 12(4), 472-487. presence-absence at a site, this does not by Ares, A., Neill, A. R., & Puettmann, K. J. itself indicate the utility of this method in old- (2010). Understory abundance, species growth restoration. Further exploration into the diversity and functional attribute response (dis)similarity of these forest characteristics to thinning in coniferous stands. Forest with those of old-growth forests is necessary to Ecology and Management, 260(7), 1104- provide further support for the use of VDT in 1113. forest management at ECP. Carey, A. B. (2003). Biocomplexity and restoration of biodiversity in temperate Conclusions coniferous forest: inducing spatial In our study, we assessed how forest heterogeneity with variable‐density communities have responded ~ 10 years from thinning. Forestry, 76(2), 127-136. the implementation of variable density stand Churchill, D. J., & Larson, A. J. (2007). COS thinning in the coastal mesic forests of 149-1: A Landscape Restoration Plan for Ellsworth Creek Preserve (ECP). By examining the South Willapa Bay Conservation Area. species presence-absence, understory cover, and Churchill, D. J., Larson, A. J., Dahlgreen, M. basal area increment we demonstrated the C., Franklin, J. F., Hessburg, P. F., & Lutz, importance of stand age and thinning J. A. (2013). Restoring forest resilience: application. As a first step in the assessment of from reference spatial patterns to the viability of using VDT at ECP, our analysis silvicultural prescriptions and shows encouraging responses of species monitoring. Forest Ecology and presence-absence to thinning. Further analyses Management, 291, 442-457. of the forest structural characteristics as well as Davis, R. J., Ohmann, J. L., Kennedy, R. E., more detailed representation of the manner in Cohen, W. B., Gregory, M. J., Yang, Z., ... which VDT was applied will provide more & Spies, T. A. (2015). Northwest Forest insight into best management practices for Plan–the first 20 years (1994-2013): status forest restoration at ECP. and trends of late-successional and old- growth forests. Gen. Tech. Rep. PNW-GTR- Acknowledgements 911. Portland, OR: US Department of We thank Julian Olden for providing valuable Agriculture, Forest Service, Pacific guidance on the multivariate statistical Northwest Research Station. 112 p., 911. techniques used in this study. We acknowledge Franklin, J. F., & Van Pelt, R. (2004). Spatial Janneke Hille Ris Lambers for guidance aspects of structural complexity in old- regarding the direction of this analysis. We also growth forests. Journal of Forestry, 102(3), thank The Nature Conservancy, in particular 22-28. Ailene Ettinger and Michael Case for mentoring Frey, S. J., Hadley, A. S., Johnson, S. L., and feedback and access to their inventory and Schulze, M., Jones, J. A., & Betts, M. G. monitoring data. We also thank the Northwest (2016). Spatial models reveal the Climate Action Science Center fellowship for microclimatic buffering capacity of old- funding the graduate fellowship that made this growth forests. Science advances, 2(4), collaboration possible. e1501392. References Hagar, J., Howlin, S., & Ganio, L. (2004). Anderson, M. J. (2001). A new method for Short-term response of songbirds to non‐parametric multivariate analysis of experimental thinning of young Douglas-fir variance. Austral ecology, 26(1), 32-46. forests in the Oregon Cascades. Forest ecology and management, 199(2-3), 333- Anderson, M. J. (2006). Distance‐based tests for 347. homogeneity of multivariate Halpern, C. B., & Spies, T. A. (1995). Plant dispersions. Biometrics, 62(1), 245-253 species diversity in natural and managed Ares, A., Berryman, S. D., & Puettmann, K. J. forests of the Pacific Northwest. Ecological (2009). Understory vegetation response to Applications, 5(4), 913-934. thinning disturbance of varying complexity Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M.,

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Blagotić, A., ... & Guevara, M. A. (2017). flying squirrels and red squirrels. Forest SoilGrids250m: Global gridded soil Ecology and Management, 202(1-3), 355- information based on machine 367. learning. PLoS one, 12(2), e0169748. Roberts, S. D., & Harrington, C. A. (2008). Jari Oksanen, F. Guillaume Blanchet, Michael Individual tree growth response to variable- Friendly, Roeland Kindt, Pierre Legendre, Dan density thinning in coastal Pacific McGlinn, Peter R. Minchin, R. B. O'Hara, Northwest forests. Forest Ecology and Gavin L. Simpson, Peter Solymos, M. Henry Management, 255(7), 2771-2781. H. Stevens, Eduard Szoecs and Helene Wagner Roberts, S. D., Harrington, C. A., & Buermeyer, (2019). vegan: Community Ecology Package. K. R. (2007). Does variable-density R package version 2.5-6. https://CRAN.R- thinning increase wind damage in conifer project.org/package=vegan stands on the Olympic Peninsula?. Western Jonsson, M., Bengtsson, J., Moen, J., Gamfeldt, Journal of Applied Forestry, 22(4), 285- L., & Snäll, T. (2020). Stand age and 296. climate influence forest ecosystem service Scharmer, K., Greif, J., eds., (2000). The European delivery and solar radiation atlas, Vol. 2: Database and multifunctionality. Environmental Research exploitation software. Paris (Les Presses de Letters, 15(9), 0940a8. l'École des Mines) Jules, M. J., Sawyer, J. O., & Jules, E. S. Seidl, R., Thom, D., Kautz, M., Martin-Benito, (2008). Assessing the relationships between D., Peltoniemi, M., Vacchiano, G., ... & stand development and understory Lexer, M. J. (2017). Forest disturbances vegetation using a 420-year under climate change. Nature climate chronosequence. Forest Ecology and change, 7(6), 395-402. Management, 255(7), 2384-2393. Spies, T. A., Giesen, T. W., Swanson, F. J., Laurance, W. F., Franklin, J. F., & Franklin, J. F., Lach, D., & Johnson, K. N. Lindenmayer, D. B. (2012). Global Decline (2010). Climate change adaptation in Large Old Trees. American Association strategies for federal forests of the Pacific for the Advancement of Science. Northwest, USA: ecological, policy, and Luyssaert, S., Schulze, E. D., Börner, A., socio-economic perspectives. Landscape Knohl, A., Hessenmöller, D., Law, B. E., ... ecology, 25(8), 1185-1199. & Grace, J. (2008). Old-growth forests as Sullivan, T. P., Sullivan, D. S., Lindgren, P. M., global carbon sinks. Nature, 455(7210), & Ransome, D. B. (2006). Long-term 213-215. responses of ecosystem components to Pilz, D., Molina, R., & Mayo, J. (2006). Effects stand thinning in young lodgepole pine of thinning young forests on chanterelle forest: III. Growth of crop trees and mushroom production. Journal of coniferous stand structure. Forest ecology Forestry, 104(1), 9-14. and management, 228(1-3), 69-81. Powell J, Lebovitz AD, Rudolph J, Penttila BA. Sullivan, T. P., Sullivan, D. S., Lindgren, P. M., 2003. A Chronology and Historical Analysis of & Ransome, D. B. (2007). Long-term Forest Harvest and Regeneration, Logging responses of ecosystem components to stand thinning in young lodgepole pine Road Construction, and Landslide Activity in the Ellsworth Creek Watershed. Bone River, forest: IV. Relative habitat use by Willapa Bay: CWC Coastal Watersheds mammalian herbivores. Forest Ecology and Consulting Management, 240(1-3), 32-41. Wang, T., Hamann, A. Spittlehouse, D.L. and R Core Team (2020). R: A language and Carroll, C. 2016. Locally downscaled and environment for statistical computing. R spatially customizable climate data for Foundation for Statistical Computing, Vienna, historical and future periods for North Austria. URL https://www.R-project.org/. America. PLoS One 11: e0156720 Zhou, G., Liu, S., Li, Z., Zhang, D., Tang, X., Ransome, D. B., Lindgren, P. M., Sullivan, D. Zhou, C., ... & Mo, J. (2006). Old-growth S., & Sullivan, T. P. (2004). Long-term forests can accumulate carbon in responses of ecosystem components to soils. science, 314(5804), 1417-1417. stand thinning in young lodgepole pine forest. I. Population dynamics of northern

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Supplementary Figures

AI. Results from analysis of gradient length to determine whether a redundancy analysis (RDA) or a canonical correspondence analysis (CCA) was a better option for our data. As a general rule of thumb, values greater than 3 indicate a unimodal response whereas those less than 1.5 indicate a linear response. Basal Area Increment is clearly unimodal. However, species presence-absence and understory cover fall between 1.5 and 3. As such we made the choice to use a CCA as it is often used for species presence- absence. For understory cover, we also choose to use CCA.

Gradient Length (DCA axis1) Species presence-absence 1.9277 Understory cover 2.6073 Basal Area Increment 3.2551

AII. Species included in this analysis. The table contains both species scientific and common names as well as the species code used in the figures.

Code Common name Scientific name TSHE Western hemlock Thuga heterophylla ALRU2 Red alder Alnus rubra THPL Western red cedar Thuja plicata PISI Sitka spruce Picea sitchensis PSME Douglas fir Pseudotsuga menzesii SALIX Salix species Salix sp. FRPU7 Cascara Frangula purshiana ACCI Vine maple Acer circinatum AGROS2 Bentgrass Agrostis spp. ATFI Lady fern Athyrium filix-femina BLSP Deer fern Blechnum spicant BOOC2 Coastal brookfoam Boykinia occidentalis CAREX Sedges Carex spp. CLSI2 Pink puslane Claytonia sibirica DIPU Foxglove Digitalis purpurea DREX2 Wood ferm Dryopteris expansa EQUIS Horsetail Equisetum spp. ERMI6 Coastal burnweed Erechtites minima GASH Salal Gaultheria shallon GATR3 Sweet scented bedstraw Galium triflorum HYRA3 Cat's ear Hypochaeris radicata ILAQ80 Common holly Ilex aquifolium MADI False lily of the valley Maianthemum dilatatum MEFE False huckleberry Menziesia ferruginea MIDE3 Coastal monkeyflower Mimulus dentatus

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MOUN2 One flowered wintergreen Moneses uniflora OXALI Wood sorrel Oxalis spp. PACKE Ragworts Packera spp. POMU Sword fern Polystichum munitum PROSA Fairybells Prosartes spp. PTAQ Eagle fern Pteridium aquilinum RIBR Stink currant Ribes bracteosum RILA3 Trailing black currant Ribes laxiflorum RUAR9 Himalayan blackberry Rubus armenacius RUBUS Blackberries Rubus spp. RUDI2 Himalayan blackberry Rubus discolor RULA Evergreen blackberry Rubus laciniatus RUPA Thimbleberry Rubus parviflorus RUSP Salmonberry Rubus spectabilis SARA2 Red elderberry Sambucus racemosa SEJA Stinking willie Senecio jacobaea TITR Foamflower Tiarella trifoliata TROV2 Pacific trilium Trillium ovatum VACCI Blueberry/hucklebery Vaccinium spp. VAOV Oval lead huckleberry Vaccinium ovalifolium VAOV2 Evergreen huckleberry Vaccinium ovatum VAPA Red huckleberry Vaccinium parvifolium VIOLA Violets Viola spp.

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Article

Vegetation Changes Across the Cretaceous-Paleogene Boundary in Montana

Paige K. Wilson Department of Earth and Space Sciences, University of Washington Email address: [email protected]

Received December 2020; accepted in revised form January 2021; published May 2021

Abstract

The Cretaceous-Paleogene (K-Pg) mass extinction marks a pivotal event in Earth history, one of five mass extinctions which devastated marine and terrestrial life. While much research has focused on the demise of abundant vertebrate groups (e.g., non-avian dinosaurs), relatively little is known about the fate of plants across this mass extinction event. This study investigates a suite of ten localities spanning the K-Pg boundary in northeastern Montana to evaluate the impact of the K-Pg mass extinction on plant community composition, and the alternative hypothesis that depositional environment overprints plant community structure. My results indicate that diversity remained high from the Cretaceous to Paleogene even while plant community composition changed dramatically. There are significant differences in plant species abundance at Cretaceous versus Paleogene localities explained by the age of the floras. However, lithology of the localities was not able to explain a significant amount of variance or difference in the species abundance data. This study indicates that the K-Pg mass extinction caused a major turnover in plant species, even as communities were evidently quick to recover in terms of diversity

Introduction (e.g., Alvarez et al. 1980, Schulte et al. 2010), Deccan volcanism (e.g., Keller et al. 2009), or The Cretaceous-Paleogene boundary some combination thereof (e.g., Petersen et al. (KPB) marks one of the most pivotal events in 2016, Renne et al. 2013). These causal Earth history (Raup & Sepkoski 1982). Studies mechanisms have been linked to widespread of both marine and terrestrial settings indicate environmental changes leading up to and across that the Cretaceous-Paleogene (K-Pg) mass the KPB, resulting in ecosystem disruption and extinction was global and affected groups at all biotic turnover on a global scale (see e.g., levels of the phylogenetic tree. Proposed causes Macleod et al. 1997). of the mass extinction include bolide impact

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Patterns of floral diversity across the K- across both formations (summarized in Figure Pg mass extinction are variable from region to 1; Hicks et al. 2002, LeCain et al. 2014, Moore region and depending on the plant fossils being et al. 2014, Sprain et al. 2015, 2018). The HCF investigated (i.e., palynoflora or pollen versus and FUF record terrestrial deposition in megaflora or leaf compression fossils). Studies floodplain environments to the west of the of palynoflora are more abundant and cover a receding Western Interior Seaway (Fastovsky greater geographic extent (e.g., western North and Bercovici 2016). The HCF mostly preserves America, New Zealand, the Netherlands, Japan; backwater environments (small channels, Vajda and Bercovici 2014). These studies crevasse-splays, lakes, and ponds) (Scholz and generally indicate extinction on the order of 15– Hartman 2007). The FUF is interpreted to 30% of local palynoflora. Megafloral studies of represent a rise in base level (leading to the K-Pg mass extinction are less abundant, widespread flood/pond horizons) (Johnson et al. restricted to New Zealand, Argentina, and the 2002). Depositional environments in the HCF Western Interior of North America. Argentinian and FUF can largely be divided into channel megafloras were apparently devastated by the and overbank settings. Channel deposits are mass extinction, with ca. 90% extinction (Stiles typified by coarser grain size, often with et al. 2020). In contrast, studies of megafloral inclined strata, and likely higher oxidation; records from New Mexico to North Dakota overbank deposits (i.e., pond or splay/levee show consistent extinction on the order of 50– settings) are typified by finer grain size, 75% of morphospecies across the KPB (e.g., horizontal massive or tabular strata, and lower Barclay et al. 2003, Johnson 2002, Wolfe and oxidation (Johnson 2002). The rise in base level Upchurch 1986). Furthermore, studies of plant coincident with the deposition of the FUF is communities across the KPB in North Dakota thought to be responsible for the increase in indicate that certain groups (e.g., ginkgoes) channel size and frequency observed in these were particularly hard hit and locally extirpated Paleogene deposits (Fastovsky and Bercovici while other taxa (particularly mire adapted taxa) 2016). In this study, these definitions are the were more likely to survive (Johnson 2002). basis for assignment of my ten localities to Paleogene floras from North Dakota remained either channel or overbank lithologies and depauperate and homogenous for ca. 1 Ma depositional environments. following the K/Pg boundary, even as vertebrate The plant fossil record of northeastern communities may have been recovering (Smith Montana presents an opportunity to further et al. 2018). This variability in regional signal assess vegetational dynamics across the KPB as may result from proximity to the causal well as spatial variation of these patterns in the mechanism, underlying ecosystem vulnerability, northern Western Interior. Several studies have or biases inherent in the fossil record. This reported on palynofloras from this area (e.g., underscores the importance of collecting high- Hotton 2002, Arens et al. 2014), but mainly resolution records and developing additional with the goal of understanding biostratigraphy regions to add to our picture of how this mass immediately across the KPB. There have also extinction progressed. Furthermore, no studies been some studies of the northeastern Montana have investigated compositional changes from megaflora (e.g., Shoemaker 1966), however the Late Cretaceous to Paleogene, focusing on these mainly focused on taxonomic collections. taxonomic rather than ecologic effects of the There is only one published detailed report on mass extinction event. Therefore, it is unclear plant megafossils from the study area (Arens how dramatic the local plant community change and Allen 2014), and it is focused on a single was from the Cretaceous to the Paleogene. plant locality (PDM) from the lowest Hell The Hell Creek Formation (HCF) and Creek Formation. Tullock Member of the Fort Union Formation In this study, we investigate the (FUF) outcrop in the northern Great Plains ecologic implications of the mass extinction at a region and preserve a rich fossil assemblage suite of ten sites spanning ca. 2.5 Ma around the dated to the latest Cretaceous and earliest KPB in northeastern Montana (Figure 1). We Paleogene. Recent paleomagnetic data, test the hypothesis that plant community 40Ar/39Ar radioisotopic age determination, and composition was significantly altered by the K- identification of the KPB impact layer have led Pg mass extinction event. We further drill down to a precise chronostratigraphic framework on potential differences between Cretaceous and

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Paleogene vegetation by testing the hypothesis At each quarry, all identifiable specimens that Paleogene floras are more homogeneous were collected and, in some cases, photographs and and less diverse whereas Cretaceous floras are notes of exceptional specimens were taken in the more disparate and more diverse. We also field. Fossils include vegetative and reproductive investigate potential ecological impacts of the plant structures, primarily preserved as mass extinction by testing the hypothesis that compression and impression fossils. Care was taken plant groups (i.e., angiosperms versus conifers) to excavate fossils at a given quarry using hand were similarly affected by the mass extinction. tools (i.e., pick axe, chisel, and rock hammer). In order to investigate the potential effect of Fossil specimens were examined in the field, differing depositional environments, we also carefully packed, and reposited at the UWBM. The test the alternative hypothesis that differences in localities herein described include relocated sites plant community composition are largely previously visited by other researchers as well attributable to lithology. The results presented novel sites discovered by our UW team. herein are a first look at the syn-ecology of K- A given fossil locality may include Pg aged floras and vegetational changes multiple quarries, where plant fossils were associated with the K-Pg mass extinction. distributed at multiple fossiliferous horizons or where a fossiliferous horizon outcropped at multiple points along a small section of outcrop. Methods Each quarry was given an individual UWBM locality number in order to maintain distinction of Fossil Collection fossils coming from each individual quarry. Care Fossils were collected in Garfield and McCone was taken to separate fossils collected at each Counties, Montana over 2015-2019 by a team of quarry to retain specific locality information for volunteers and researchers from the University of each specimen. For purposes of these analyses, Washington (UW). For purposes of this study, a these quarries have been aggregated into the ten subset of 10 localities (3278 specimens from 40 localities I am comparing. quarries) are described and analyzed. These localities represent, in order of importance, a) Sedimentological Description and Stratigraphy unique stratigraphic intervals, b) significant At each locality we used hand levels and collections (sample size >150) and c) relatively Jacob’s Staff to record detailed similar lithologies. This sampling strategy is sedimentological descriptions of the intended to minimize taphonomic bias, assemble a fossiliferous horizon and surrounding series of localities spanning the time period of approximately 4 m of stratigraphy. interest (ca. 2.5 Ma around the K/Pg boundary), and Additionally, we took GPS coordinates at each minimize time gaps in this record. These were locality on each visit to locate and verify the voucher collections, with all identifiable fossils locality. We took images of each locality for collected to be brought to the University of future reference. Finally, we located the nearest Washington Burke Museum of Natural History and marker bed (i.e., dated coal bed, formation Culture (UWBM). contact) and measured the stratigraphic distance Specimens were collected from Bureau of to our locality using both hand level and Jacob’s Land Management lands (permits MTM 108226, staff as well as high precision Trimble GPS MTM108766, MTM 109605, MTM 110439; unit. These positions are the basis for our UWBM localities P7112-7114, P8091-8095, estimation of the relative age of each locality. P8527, P7135, P85442, P8086, P8087, P7137, The stratigraphic sections were used to evaluate P7136, P8079, P7144, P7145, P8084, P8536, the lithology and interpret the depositional P8537), the Charles M. Russel Wildlife Refuge environment at each locality (summarizing administered by U.S. Fish and Wildlife and the these as channel versus overbank lithofacies), Army Corps of Engineers (permits 15-2, DACW45- following Johnson (2002). 3-16-6023, 17-007, 18-008; UWBM localities P8526, P8080, P6909, P6910, P8543, B8198, Classification of the Flora P8544, P8077, P8078, P7152, P8088, P8545), as Each taxon is assigned to a morphotype based well as private lands (UWBM localities B8202, on organ type, gross morphology, and leaf P7125, P7126, P8075, P8076, B8201, P6911, architecture. Angiosperm leaves were grouped P7142, P8098). and described based on leaf architecture

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according to Ellis et al. (2009). Major venation account for the wide variation in sample size at patterns are generally stable within each locality. morphotypes whereas size and shape may be Initially, I tabulated the sample size and variable (Ellis et al. 2009). Therefore, particular raw richness of each locality and also calculated importance was given to primary, secondary, rarefied species richness (rarefied to smallest and tertiary venation and secondarily to features sample size of n=169 or n=139, depending on such as laminar size and shape. These whether the entire data set or only vegetative morphotypes are assigned a unique morphotypes were considered) in order to alphanumeric code (e.g., HC-001). After summarize diversity and size of each locality. I morphotypes were described, we assessed the measured richness of both all morphotypes and affinity of each morphotype to taxa that have vegetative morphotypes only, and then used a been previously described in the published two sample t-test to evaluate whether raw literature. Species epithets were applied or richness or rarefied richness was different compared with (cf.) where appropriate. For a between floras of different age or lithology. summary of morphotypes, species names, and In order to evaluate whether there were material, please see Supplementary Material. differences in community structure between localities of differing ages or lithologies, I first Data Analyses performed an unconstrained ordination and Species abundance from each locality was correlated this analysis with my environmental tabulated. While many species in this study are variables. I chose a nonmetric multidimensional very rare (singletons), several of these are scaling (NMDS) ordination based on Bray- ecologically important taxa (e.g., there is only a Curtis distances, reducing to k = 2 and running single species of cycad, known only from a the analysis 100 times to find the minimum single locality). I ran analyses using both a stress solution. This method is well suited to more conservative list (excluding singletons) species abundance data, avoids any assumption and a more liberal list (including singletons) of linearity among variables, and is relatively and found the effects on my results were robust to the arch effect. I examined a scree plot negligible. I also performed a Mantel test (based of stress versus dimensionality to determine that on pearson’s correlation) on Bray-Curtis values of k higher than 2 did not appreciably dissimilarity measured on species abundance of minimize stress further. I also ran a Monte vegetative morphotypes, comparing the Carlo simulation (n = 100) to examine the conservative and liberal datasets to determine significance of my observed stress value. whether the inclusion of these rare taxa Finally, I examined the resultant sressplot to significantly affects species distribution determine whether this solution is likely a local patterns. The results indicated that the inclusion minima of stress or truly a well fit solution. of rare taxa does not significantly alter patterns I then calculated the correlation of my of species distribution (statistic r = 0.9716, p = environmental variables (age and lithology of 0.001). Given the taxonomic and ecological localities) to this analysis to evaluate the importance of some of these singletons, I strength, direction, and significance of this therefore used the more liberal approach correlation, which may help to explain patterns (including all taxa) in the results reported in my ordination (using n = 1000 permutations below. While including rare taxa may affect my to evaluate significance). I also calculated the results, the ecological value of these taxa correlation of my species abundance data to this justifies this decision. Similarly, I ran analyses ordination (loadings or variable weights) to including all morphotypes (a more liberal evaluate which species were correlated approach) and including only vegetative significantly, and the direction of such morphotypes (a more conservative approach) correlations (again using n = 1000 permutations and determined that including only vegetative to evaluate significance). Subsequently, I morphotypes resulted in clearer results and investigated the species most influential in avoided the potential double counting of taxa, defining locality groupings (based on age and so I below present analyses on abundance of lithology) using the simper function. While the vegetative morphotypes only (except where simper function can be difficult to interpret and noted). This abundance data was then row may confound mean between group and within transformed to normalize localities and to group variation, in this instance it is an

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appropriate analysis to apply as it also utilizes ecology package vegan, version 2.5–6 (Okansen Bray-Curtis dissimilarity which is designed to et al. 2019). summarize differences in species abundance data. The simper function summarizes important Results taxa (variables) in differentiating different groups of localities (objects); these variables Of the ten localities in this study, six are contribute at least 70% to the differences Paleogene in age and four are Cretaceous in between groups. It complements the NMDS age. Based on sedimentology at each locality, ordination in highlighting important taxa to the eight of the localities represent channel deposits groupings of interest, beyond those which are and three represent overbank deposits (one significantly correlated with the ordination. Cretaceous and two Paleogene in age) (Figure Secondly, in order to drill down on the 1). The ten localities in this study range from distribution of various taxonomic groups across 169 to 588 specimens (Table 1). Specimens localities, I performed a Mantel test (based on were identified to 110 morphotypes (see pearson’s correlation) comparing abundance Supplementary Material for detail); 84 are non- data of vegetative morphotypes of angiosperm monocot angiosperm morphotypes, eight are affinity versus conifer affinity at my localities. conifer morphotypes, seven are monocot This test compares the distribution of taxa in angiosperm morphotypes, six are pteridophyte these two groupings to evaluate how similar or morphotypes, two are of indeterminate affinity, dissimilar the distribution is. I measured Bray- one is a bryophyte morphotype, one is a cycad Curtis distance for use in this test. morphotype, and one is a ginkgo morphotype. Thirdly, to quantitatively evaluate the Of these, most are leaf fossils (93), some are differences between localities based on age or reproductive structures (11), some are other lithology, I performed a series of statistical vegetative structures (4), and some are of analyses on my abundance data of vegetative indeterminate origin (2) (Figure 2). morphotypes. I performed a Permutational Species richness varied with the sample Multivariate Analysis of Variance on distances size at each locality (from 14 to 34; Table 1). (perMANOVA), testing for differences between There is no significant difference between the the age groups, lithology groups, and interaction richness of Cretaceous vs. Paleogene localities between lithology and age (using n = 1000 (t-test p = 0.6451) or between the richness of permutations in each perMANOVA). I used channel vs. overbank localities (t-test p = Bray-Curtis distances to measure dissimilarity 0.6243). To account for differences in sample between my localities. Note that I also ran an size I also computed rarefied richness (ranging analysis of similarity (ANOSIM) on this data, from 12.0 to 23.1; Table 1). Once again, there is which utilizes rank dissimilarity and is therefore no significant difference in rarefied richness well suited to be paired with an NMDS between localities of different age (t-test p = ordination. The results of this ANOSIM were 0.4669) or different lithology (t-test p = very similar to the results of my perMANOVA, 0.7093). Restricting to only vegetative but the perMANOVA allows for analysis of morphotypes, richness varied similarly (from 29 interaction of variable groupings; therefore, I to 12; Table 1) and is not significantly different chose to only report perMANOVA results here. between Cretaceous vs. Paleogene (t-test p = Finally, I investigated whether the dispersion 0.8446) or between channel vs. overbank (t-test within groups (age or lithology) was p = 0.7732) localities. Rarefied richness of statistically significant using a permutation test vegetative morphotypes (Table 1) is also not of multivariate dispersion (disper), again significantly different between Cretaceous vs. applying the Bray-Curtis dissimilarity metric. I Paleogene (t-test p = 0.6091) or between tested whether or not this dispersion was channel vs. overbank (t-test p = 0.5624) significantly different between localities using localities. an ANOVA. NMDS results indicate the ordination is All analyses described above were a relatively good fit to the underlying variance conducted in R version 4.0.2 (R Core Team, and dissimilarity of my species data; stress is 2020; http://www.r -project.org) using low (0.0815) and significant based on Monte appropriate functions from the community Carlo simulation (p = 0.020), and (non-metric) R2 shows a strong correlation between ordination distance and observed dissimilarity

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(0.993). Finally, examination of the resultant which also tend to distinguish localities based stress plot indicates that this solution is a good on lithology, one is another conifer morphotype. representation of the underlying data, and not Ten of these are non-monocot angiosperm likely to be a local minima. morphotypes, some of which (e.g., HC-050 In the resultant ordination biplot, Paranymphaea crassifolia) are also influential Cretaceous localities are clustered separately in distinguishing channel from overbank from the Paleogene localities along the first localities. While some of these influential taxa NMDS axis. Lithology is very weakly, and not are restricted to Cretaceous localities (e.g., HC- significantly, correlated with the ordination 080 cf. “Dryophyllum” subfalcatum, HC-025 results (R2 = 0.0934, p = 0.490), and no clear Leepierceia preartocarpoides), other species pattern is evident among localities by lithology. which are locally extirpated at the KPB (e.g., Age is significantly correlated with the Ginkgo adiantoides) are not considered ordination results (R2 = 0.4743, p = 0.016) influential by this analysis. Comparing these loaded high on axis 1. Rare taxa tend to co- influential taxa to those found significantly occur; there are several clusters of taxa which correlated with the ordination analysis, represent these locality-specific associations. Paranymphaea crassifolia (HC-050) and cf. Non-monocot angiosperms are by far the most Zizyphoides flabella (HC-008) are both common morphotype and are relatively evenly influential in defining age and lithology distributed. Conifers tend to plot between the groupings and are significantly correlated with Cretaceous and Paleogene clusters, perhaps the ordination. Paranymphaea crassifolia (HC- slightly weighted towards the Cretaceous 050) plots near one of the Paleogene overbank localities. The single ginkgo taxon (Ginkgo deposits; cf. Zizyphoides flabella (HC-008) adiantoides) and single cycad taxon (Nilssonia plots between areas of ordination space comtula) recovered in this study are both known occupied by Cretaceous or Paleogene localities. only from Cretaceous localities and both plot A selection of these influential species is near these Cretaceous floras. There are several depicted in Figure 2. monocot taxa which plot close to the Paleogene Taxonomic groups (angiosperm versus localities; these represent common weedy taxa conifer morphotypes) are weakly, negatively such as Limnobiophyllum scutatum (HC-111) correlated in their distribution across the ten and a probable Typhaceae species (HC-054). localities (Mantel statistic r = -0.1359, p = Correlation of species abundance data to this 0.769). While this correlation is weak, it is not ordination shows that only a handful of these significant, indicating that there are substantial taxa are significantly correlated. The probable differences between the abundance distribution Typhaceae species (HC-045), Paranymphaea of angiosperms and conifers at these localities. crassifolia (HC-050), cf. Zizyphoides flabella This supports the above observation that (HC-008), and three unnamed angiosperms angiosperms and conifers differ in their (HC-054, HC-084, HC-023) are significantly distributions at these localities; angiosperms are correlated with this ordination (p < 0.1). more evenly distributed in the ordination, while Simper analysis also highlights the most conifers often plot between the Cretaceous and influential species contributing to the variance Paleogene locality clusters. between groups. In this study, overbank Quantitative comparisons of variance in lithologies tend to be distinguished from species composition reveals that Cretaceous and channel lithologies by the presence of a suite of Paleogene localities differed significantly in 12 morphotypes. Seven of these are non- community composition, while differing monocot angiosperm, two are monocot, two are lithologies did not result in significantly conifer, and one is a fern morphotype. While different species composition. Using groupings some (e.g., HC-017 Metasequoia occidentalis) by age, between-group variance in species are found at all localities, but tend to be more composition is significantly greater than within- common at channel localities, others (e.g., HC- group variance (Table 2; perMANOVA p = 050 Paranymphaea crassifolia) are only found 0.004). However, by lithology, overbank and at overbank localities. Cretaceous localities tend channel localities are not significantly different to be distinguished from Paleogene localities by (Table 2; perMANOVA p = 0.694). Finally, the presence of a suite of 13 morphotypes. Two looking at the interaction between age and of these are the same conifer morphotypes lithology, groupings by age are significantly

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different (Table 2; perMANOVA p = 0.006) but Dispersion among overbank versus channel groupings by lithology or by the combination of localities is also not significantly different. age and lithology are not significantly different Average distance to median among channel (Table 2; perMANOVA p = 0.572 and p = localities (Table 3; 0.568) is similar to that 0.719, respectively). among overbank localities (Table 3; 0.542), and However, dispersion among Cretaceous average dispersion is not significantly different and Paleogene localities is not significantly between these two lithologic groups (Table 3; different. Average distance to median among ANOVA p = 0.661). Cretaceous localities (Table 3; 0.479) is similar to that among Paleogene localities (Table 3; . 0.549), and average dispersion is not significantly different between these two age groups (Table 3; ANOVA p = 0.5109).

Table 1: Summary of size and richness of localities. Raw and rarefied richness were calculated for data sets including all morphotypes and including only vegetative morphotypes for comparison.

All Morphotypes Vegetative Morphotypes Litholo Rarefied Rarefied Locality Sample Raw Raw gy Richness Richness Size Richness Richness (n=169) (n=139) Jane’s channel 354 22 18.2 20 15.7 overban 18 13.4

Biscuit Springs 376 23 16.6 k Tharp’s Market channel 348 25 21.3 21 17.4 Yabba Dabba channel 18 15.0 336 19 16.4

Paleogene Do New York channel 254 23 19.9 20 16.4 overban 12 9.2 The Swamp 299 16 12.5 k

Bruce Leaf channel 227 24 22.0 19 17.3 Smurphy’s channel 11 10.1 327 14 12.0 Guess II Fisk I channel 169 22 22.0 17 17.0 Cretaceous overban 29 18.5 Seafood Salad 588 34 23.1 k

Table 2: perMANOVA results modeling groupings based on age, lithology, and the interaction of age and lithology. Model pseudo p- df R2 -F value 0.2030 Age 1 2.0383 0.004 5 0.0926 Lithology 1 0.8172 0.694 9 Age 0.2030 Interaction 1 1.7114 0.006 5 Lithology 0.0946 1 0.9191 0.572 8 Age- 0.0842 Lithology 1 0.8174 0.719 0 Interaction

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Table 3: Multivariate dispersion within age and lithology groupings, evaluated by ANOVA. Analyses were conducted on abundance data of vegetative morphotypes using Bray-Curtis distances. ANOVA Average distance to median Pseudo F-statistic p-value Cretaceous 0.475 0.4232 0.5336 Paleogene 0.551 Channel 0.578 0.4026 0.5435 Overbank 0.542

Figure 1: Location and position of localities. Localities are marked in their position within Garfield and McCone Counties, Montana (B, inset of A) and in their relative stratigraphic position (C). Synthetic stratigraphic column shows stratigraphic height, formation, magnetochron, and age (Ma) based on magneto and radioisotopic dating techniques (LeCain et al. 2014; Sprain et al. 2015, 2018). Localities (in chronologic order from oldest) are Seafood Salad (S), Fisk I (F), Smurphy’s Guess II (SG), Bruce Leaf (BL), The Swamp (TS), New York (NY), Yabba Dabba Do (YDD), Tharp’s Market (TM), Biscuit Springs (BS), and Jane’s (J). Locality symbols correspond to epoch (Cretaceous = blue, Paleogene = yellow) and lithology (channel = square, overbank = circle).

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Figure 2: Influential or common species/morphotypes from this study. Angiosperm morphotypes: (A) morphotype HC-025 (Leepierceia preartocarpoides) [UWBM PB 96532], (B) morphotype HC-050 (Paranymphaea crassifolia) [UWBM PB 105249], (C) morphotype HC-008 (cf. Zizyphoides flabella?) [UWBM PB 103640], (D) morphotype HC-020 (“Dryophyllum” subfalcatum) [UWBM PB 103630], (E) morphotype HC-065 (cf. Archaempelos nebrascensis) [UWBM PB 104858], (F) morphotype HC-028 [UWBM PB 105684], (H) morphotype HC-080 (cf. “Dryophyllum” tenneseensis) [UWBM PB 103945], (I) morphotype HC-042 (cf. Zizyphoides flabella) [UWBM PB 97344]. Pteridophyte (fern) morphotype: morphotype HC-027 [UWBM PB 105256]. Conifer morphotypes: (G) morphotype HC-030 [UWBM PB 104709], (J) morphotype HC-017 (Metasequoia occidentalis) [UWBM PB 103535], (K) morphotype HC- 033 (Metsequoia occidentalis) [UWBM PB 96535], (M) morphotype HC-018 (Glypotostrobus europaeus) [UWBM PB 96554]. Scale bar is 1 cm.

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Figure 3: NMDS biplot of analysis on vegetative morphotype abundance at the ten localities. Distance measured using Bray-Curtis dissimilarity. Stress (0.0815) is relatively low and correlation between ordination distance and observed dissimilarity is high (R2 = 0.993), indicating a good representation of the underlying variance captured by these two dimensions. Monte Carlo permutation test indicates that this stress is statistically significant (p = 0.020). Closed symbols are localities; symbols correspond to age (K = Cretaceous, Pg = Paleogene) and lithology (ob = overbank, ch = channel) of locality. Open symbols are species (color corresponds to species affinity; PTE = pteridophyte, DIC = non-monocot angiosperm, MON = monocot angiosperm, CON = conifer, BRY = bryophyte, GIN = ginkgo, UID = indeterminate, CYC = cycad). Environmental variables (age and lithology) were correlated with analysis, and the centroids of these groupings are plotted as vectors in black. Age is significantly correlated (p = 0.016) while lithology is not significantly correlated (p = 0.490) with this ordination.

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Figure 4: Boxplots of dispersion within groupings by age or lithology. Dispersion calculated on Bray- Curtis distances based on vegetative species abundance data. Within-group dispersion by age (K = Cretaceous; Pg = Paleogene) or by lithology (channel or overbank depositional environment) is not statistically significant (see Table 3).

overbank localities do not differ significantly in Discussion their richness. This is significant, as it indicates that environmental changes or biases in the Despite the documented extinction and local megafloral fossil record are not likely to extirpation at the KPB, there is apparently no significantly alter the signal of diversity we significant loss of diversity from the Late recover from these floras. Beyond strictly Cretaceous to the early Paleogene in univariate measures of diversity, researchers in northeastern Montana (Table 1). While a North Dakota also noticed a homogeneity to significant proportion of taxa disappear at the Paleogene-age floras. Among these North KPB, these are evidently quickly replaced by Dakota floras, Cretaceous floras demonstrate a new taxa in the earliest Paleogene, and marked turnover from lower to upper HCF megafloras do not document any period of whereas floras recovered throughout the FUF sustained low diversity. This stands in contrast are relatively similar to each other. In contrast, to the record in North Dakota, where my results indicate that Paleogene and researchers documented at least 1 Ma of low Cretaceous floras are not significantly different diversity, homogeneous floras following the in their dispersion (Table 3). Therefore, we find KPB (Johnson, 2002). Despite these differences no evidence to support the hypothesis that floras in diversity, the same characteristic taxa noted remained depauperate and homogeneous during in North Dakota (e.g., Paranymphaea the early Paleogene. crassifolia) come in as characteristic Paleogene While there was no dramatic change to taxa shortly after the KPB in Montana. the diversity or disparity of floras from the Examining these same diversity measures by Cretaceous to the Paleogene, it is evident that lithology, it is evident that channel and

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the community composition changed markedly (Johnson 2002). My results may support these across this boundary (Figure 3; Table 2). This findings; Paleogene communities have more pattern is robust, statistically significant, and mire-adapted taxa, and perhaps these flooding- implies that there is a clear difference in species type environments (i.e., overbank localities) are composition between Cretaceous and Paleogene more common in the Paleogene. However, localities. The ecological underpinnings of this further investigation is needed to find statistical difference are less obvious. Both Cretaceous significance to this trend. and Paleogene localities are dominated by non- Overall, there appear to be a suite of monocot angiosperms, with a minor component important taxa (e.g., Paranymphaea crassifolia, on conifers and some few other taxa (i.e., ferns, Metasequoia occidentalis, Leepierceia cycads, and ginkgoes). The non-monocot preartocarpoides) distinguishing Cretaceous angiosperms appear to be evenly distributed; from Paleogene and channel from overbank some are aligned with Paleogene localities, lithologies. While some of these influential taxa some agnostic, and some aligned with are restricted to the Cretaceous or Paleogene Cretaceous localities. Conifer taxa appear less exclusively, many are common to both time likely to align with either Cretaceous or bins. It appears that most of these important Paleogene localities, implying that these taxa and/or significant taxa are also abundant. are more evenly distributed. Overall, the Ecologically, this raises an interesting line of angiosperm taxa appear to be more endemic inquiry: was the K-Pg mass extinction more while conifer taxa appear to be more impactful on abundant or rare taxa, and do these cosmopolitan and long-lasting through this trends help to explain why local community record. Further analyses are needed to parse out composition changed even though major the drivers of this relationship. Taxa of other lineages persisted? Alternatively, the simper affinities show a stronger association with one analysis may simply be picking up on abundant time bin or the other. For instance, the only taxa as important because of their abundance. ginkgo and cycad taxa from this study (Ginkgo Further investigation into the significance of adiantoides and Nilssonia comtula) are both rare versus abundant taxa in surviving the K-Pg only known from Cretaceous localities and mass extinction is needed to explore these cluster with those localities (Figure 3). questions. By contrast, lithology does not appear My results also provide insight into the to have a significant effect on species broader ecology of terrestrial communities composition. We can feel more confident in across the KPB. This boundary marked a comparing floras from differing lithologies, as transition from diverse riparian forest the taphonomic overprinting appears to be vegetation to equally diverse mire or forest minimal. However, while the impact of vegetation. Paleogene floras were also equally lithology may not be significant, there are still heterogeneous to their Cretaceous predecessors, interesting patterns at the intersection of contradicting some of the conclusions of lithology and age in my analyses. Monocot workers in North Dakota. Therefore, there is no angiosperms and bryophytes tend to be more evidence of widespread diversity loss or aligned with Paleogene localities, but in depauperate floras following the KPB. particular with overbank deposits. The However, there is a massive turnover in monocots in this grouping include vegetation composition at the KPB. This can Limnobiophyllum scutatum, an aquatic taxon perhaps be explained by vegetation die off at found which grows helically arranged, ovate the KPB (due to environmental devastation leaves attached in rosettes. These more weedy stemming from bolide impact and/or volcanism) and aquatic plants tend to grow in wet, swampy followed by re-colonization by a diverse array environments such as the floodplain or pond of plant taxa. This rapid re-diversification has environments encompassed by overbank important implications for ecosystem recovery. lithology. Previous research in North Dakota Vertebrate groups (e.g., mammals) experienced indicates that the Paleogene ushered in a period a moderate local extinction at the KPB but had of increased ponding or flooding regionally begun to recover in the early Paleogene; (Fastovsky and Bercovici 2014). Investigation expansion of plant communities following the of North Dakota megafloras found an increase mass die-off at the KPB may have laid the path in mire-adapted taxa following the KPB for, and directed, this diversification. Future

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work will focus on connecting patterns of extinction and recovery in plants with those in mammals, non-avian dinosaurs, and other Acknowledgements vertebrates. Funding for the fieldwork of this project was These results indicate that extinction provided by the Colorado Scientific Society, rate may not tell the entire picture on the impact Quaternary Research Center, American of the K-Pg mass extinction. While previous Philosophical Society’s Lewis and Clark Grant, studies have emphasized high species loss in the University of Washington’s Earth and Space Western Interior and Argentina, the results Sciences (ESS) Department (Jody Bourgeois presented here indicate that while species Graduate Student Support Fund), the Evolving turnover and therefore floral composition Earth Foundation, Geologic Society of America changed, diversity and heterogeneity remained Student Grant, and Paleo Society in conjunction high. Furthermore, there may be environmental with the Bearded Lady Project. This research or ecological reasons for some of the turnover was also supported by the Hell Creek Project (e.g., a transition to more mire-adapted taxa, from the Myhrvold and Havranek Charitable endemic angiosperms more susceptible to Family Fund. The Burke Museum of Natural extirpation). Future work to investigate History and Culture also provided collections ecologically important traits of common space and curational support to inventory and Cretaceous and Paleogene taxa (e.g., level of deposit this collection. dissection, weedy habit) may lend additional Thanks also go to a long list of field assistants support to this conclusion. (Paul Kester, Susan Kester, Mara Page, Matt In addition, this work pioneers the Butrim, Tran Do, Sarah Reza, Aida Rusman, study of plant fossils from a novel section in Anton Resing, Gregg Wilson, Moon Draper, Montana, adding additional local data to our Robert Spencer, Ben LeFebvre, and Mary Alice picture of how this mass extinction proceeded. Benson) and curational assistants (Teresa Di Further investigation is needed to understand Leonardo, Emily Gallagher, Sherman Chen, why and how the mass extinction may have Allison Phillips, and Ellen Ng) who contributed affected plant communities differently in to this body of work. Also, I would like to different areas of the world. Next steps are to express my gratitude to Dr. Gregory P. Wilson quantitatively compare these floras with Mantilla and Dr. Caroline A. E. Strӧmberg for contemporaneous floras from elsewhere in the their advice, mentorship, and guidance in this Western Interior to better understand the spatial project as well as to the many members of the as well as temporal distribution of species at Wilson Mantilla and Strӧmberg labs who this critical time interval. This study is limited provided advice and feedback during this course by the small number of localities and the of this project. abundance of rare taxa (singletons) at these Permitting and land access was localities. While the rare taxa do not appear to provided by the Charles M. Russell Wildlife significantly alter species distribution at any Refuge (administered by the U.S. Army Corps given site, their inclusion in these analyses may of Engineers and U.S. Fish and Wildlife), have impacted my results. Therefore, future Bureau of Land Management, and the individual work is also aimed at increasing sampling at landowners Bob & Jane Engdahl, Bob & Cindy localities and increasing the number of sampled Stroh, Les & Jeri Thomas, and Dale & Jane localities. Tharp. Thanks also to Greg Liggett, Doug Overall, this work provides a new Melton, Paula Gouse, and Michele Fromdahl for window into the K-Pg mass extinction and a assistance with this permitting. new record of plant communities to better Thank you to Dr. Julian Olden and the understand how the mass extinction affected FISH 560 2020 community for advice on terrestrial ecosystems. These results indicate analysis and writing. that megafloral collections are robust to I would also acknowledge that the overprinting by depositional environment, and fossils in this paper were collected on lands that largely capture the biological response through are the traditional territory of the Fort Belknap time. While plant diversity remained high Assiniboine & Gros Ventre Tribes and Fort during this interval, plant communities changed Peck Assiniboine & Sioux Tribes. Future field dramatically at the KPB. trips will be respectful to the original people and sovereignty.

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Supplementary Figures

Table S1. Species list including morphotype name, species name (if known), plant affinity, organ, and number of specimens in study. Affinity abbreviations are: CON – conifer, DIC – non-monocot angiosperm, MON – monocot, PTE – pteridophyte, BRY – bryophyte, CYC – cycad, GIN – ginkgo. Morphotypes are arranged in order of abundance. Morphoty Affin Number of Species Name (if known) Organ pe Name ity Specimens HC-017 Metasequoia occidentalis CON Leaf 419 HC-041 cf. Zizyphoides flabella DIC Leaf 319 HC-018 Glyptostrobus europaeus CON Leaf 312 HC-080 cf. “Dryophyllum” tenneseensis DIC Leaf 223 HC-050 Paranymphaea crassifolia DIC Leaf 219 HC-065 cf. Archaempelos nebrascensis DIC Leaf 217 HC-028 DIC Leaf 111 HC-094 cf. Archaempelos nebrascensis DIC Leaf 106 HC-008 cf. Zizyphoides flabella? DIC Leaf 90 HC-033 Metasequoia occidentalis CON Reproductive 78 HC-025 Leepierceia preartocarpoides DIC Leaf 67 HC-030 CON Leaf 64 HC-013 DIC Leaf 59 HC-027 PTE Leaf 57 HC-055 DIC Reproductive 53 HC-020 "Dryophyllum" subfalcatum DIC Leaf 46 HC-029 Taxodium olriki CON Leaf 45 HC-054 Typhaceae sp. MON Leaf 44 HC-095 DIC Leaf 44 HC-092 DIC Leaf 43 HC-042 cf. Zizyphoides flabella DIC Leaf 34 HC-111 Limnobiophyllum scutatum MON Leaf 31 HC-023 DIC Leaf 30 HC-113 Limnobiophyllum scutatum MON Axis 23 HC-115 Platanites sp. DIC Leaf 23 HC-035 DIC Leaf 22 HC-102 DIC Leaf 22 HC-003 DIC Leaf 21 HC-052 cf. Paranymphaea crassifolia DIC Leaf 21 HC-004 DIC Leaf 20 HC-015 Carpites ulmiformis DIC Reproductive 19 HC-061 Ginkgo adiantoides GIN Leaf 19 HC-082 DIC Leaf 19 HC-011 Quereuxia angulata DIC Leaf 17 HC-084 DIC Leaf 17 HC-005 DIC Leaf 16 HC-038 DIC Leaf 14 HC-091 DIC Leaf 13 HC-105 DIC Leaf 13 HC-066 Platanites sp. DIC Leaf 13 HC-014 Harmisia hydrocotyloidea DIC Leaf 12 HC-044 BRY Leaf 12 HC-073 DIC Reproductive 12

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HC-045 Equisetum sp. PTE Leaf 11 HC-104 DIC Leaf 11 HC-039 DIC Leaf 10 HC-031 DIC Leaf 10 HC-099 DIC Leaf 10 HC-022 DIC Leaf 8 HC-007 cf. Zizyphoides flabella DIC Leaf 7 HC-032 DIC Leaf 7 HC-109 DIC Leaf 7 HC-024 cf. “Dryophyllum” tenneseensis DIC Leaf 6 HC-087 DIC Leaf 6 HC-106 DIC Leaf 6 HC-112 Typhaceae sp. DIC Leaf 6 HC-001 Erlingdorfia montana DIC Leaf 5 HC-012 cf. “Dryophyllum” tenneseensis DIC Leaf 5 HC-026 DIC Leaf 5 HC-046 DIC Leaf 5 HC-093 DIC Leaf 5 HC-064 DIC Leaf 5 HC-009 DIC Leaf 4 HC-016 Ditaxocladus catenulatus CON Leaf 4 HC-051 PTE Leaf 4 HC-086 DIC Leaf 4 HC-047 DIC Leaf 3 HC-019 DIC Leaf 3 HC-034 DIC Leaf 3 HC-060 MON Leaf 3 HC-075 DIC Leaf 3 HC-076 DIC Leaf 3 HC-059 DIC Leaf 2 HC-037 DIC Leaf 2 HC-085 DIC Leaf 2 HC-089 DIC Reproductive 2 HC-090 DIC Leaf 2 HC-096 DIC Leaf 2 HC-098 DIC Leaf 2 HC-103 DIC Leaf 2 HC-114 DIC Axis 2 HC-122 DIC Leaf 2 HC-062 CON Leaf 2 HC-063 DIC Leaf 2 HC-048 DIC Reproductive 1 HC-049 DIC Reproductive 1 HC-053 DIC Leaf 1 HC-056 DIC Reproductive 1 HC-057 MON Leaf 1 HC-058 MON Leaf 1 HC-002 DIC Leaf 1 HC-006 Nelumbo sp. #2 DIC Leaf 1 HC-010 DIC Indet. 1 HC-021 DIC Leaf 1 HC-036 DIC Leaf 1

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HC-040 CON Reproductive 1 HC-043 DIC Leaf 1 HC-069 Cobbania corrugata MON Leaf 1 HC-072 DIC Leaf 1 HC-077 PTE Leaf 1 HC-079 DIC Reproductive 1 HC-081 Hydropteris pinnata PTE Leaf 1 HC-083 DIC Reproductive 1 HC-088 Indet. Indet. 1 HC-097 PTE Axis 1 HC-101 DIC Leaf 1 HC-107 DIC Leaf 1 HC-108 Nilssonia comtula CYC Leaf 1 HC-110 Indet. Axis 1 HC-116 DIC Leaf 1

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