13.11: Why It Matters- Animal Diversity - Biology

13.11: Why It Matters- Animal Diversity - Biology

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Why discuss the importance of animal diversity?

Animal evolution began in the ocean over 600 million years ago with tiny creatures that probably do not resemble any living organism today. The number of extant species is estimated to be between 3 and 30 million.

But what is an animal? While we can easily identify dogs, birds, fish, spiders, and worms as animals, other organisms, such as corals and sponges, are not as easy to classify. Animals vary in complexity—from sea sponges to crickets to chimpanzees—and scientists are faced with the difficult task of classifying them within a unified system. They must identify traits that are common to all animals as well as traits that can be used to distinguish among related groups of animals. The animal classification system characterizes animals based on their anatomy, morphology, evolutionary history, features of embryological development, and genetic makeup. This classification scheme is constantly developing as new information about species arises. Understanding and classifying the great variety of living species help us better understand how to conserve the diversity of life on earth.

Learning Outcomes

  • Discuss the evolutionary history of the animal kingdom
  • Chart animal phylogeny
  • Describe the various types of body plans that occur in animals
  • Discuss the tissue structures found in animals
  • Discuss methods and features of animal reproduction
  • Discuss the importance of homeostasis in animals

13.11: Why It Matters- Animal Diversity - Biology

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Assistant Professor

Dr. Kukekova studies genetics of social behaviors. She works with unconventional animal models that hold a significant potential for understanding genetic regulation of affiliation, aggression, anxiety, and fear, social behaviors that are consistently associated with human neurological disorders. The identification of genes and gene networks involved in regulation of these behaviors is also a subject of interest for animal breeding programs focused on selection for behavioral traits.

Why intergroup variation matters for understanding behaviour

Intergroup variation (IGV) refers to variation between different groups of the same species. While its existence in the behavioural realm has been expected and evidenced, the potential effects of IGV are rarely considered in studies that aim to shed light on the evolutionary origins of human socio-cognition, especially in our closest living relatives—the great apes. Here, by taking chimpanzees as a point of reference, we argue that (i) IGV could plausibly explain inconsistent research findings across numerous topics of inquiry (experimental/behavioural studies on chimpanzees), (ii) understanding the evolutionary origins of behaviour requires an accurate assessment of species' modes of behaving across different socio-ecological contexts, which necessitates a reliable estimation of variation across intraspecific groups, and (iii) IGV in the behavioural realm is increasingly likely to be expected owing to the progressive identification of non-human animal cultures. With these points, and by extrapolating from chimpanzees to generic guidelines, we aim to encourage researchers to explicitly consider IGV as an explanatory variable in future studies attempting to understand the socio-cognitive and evolutionary determinants of behaviour in group-living animals.

1. Introduction

Within the order of primates, humans are the species occupying the widest range of habitats, spanning from small-scale societies in subarctic climates to cities with millions of inhabitants in desert environments. Correspondingly, humans show a wide range of behavioural proclivities—stemming from the peculiarities of both their physical and social environments—which deems ‘flexibility’ a core characteristic of the human species [1]. In other words, disregarding the existence of behavioural variation on the individual and group levels would inevitably lead to an impoverished view of human nature.

The importance of considering cross-cultural variation for understanding universals and diversity in human cognition and behaviour has gained increasing traction over the past years (e.g. [2–4]). For instance, prosocial [5] and conformist [6] tendencies, as well as norms regarding what constitutes a ‘fair’ division of resources [7], differ markedly across societies. Several mechanisms have been identified underlying the cross-societal diversification of behavioural tendencies, e.g. genetics, environmental affordances, culture [1,8,9] and even gene–culture coevolution [10]. Intergroup variation (henceforth: ‘IGV’) is becoming an integrated level of explanation of behavioural diversity in the human species, especially with regard to social behaviour. The Proceedings of the National Academy of Sciences (PNAS) recently even published a special issue on the ‘pressing questions in the study of psychological and behavioral diversity’, emphasizing in their introductory article that: ‘A researcher who relies on just one of these [intraspecific] groups to develop and vet a theory of human psychology would have a challenge determining what is basic, fundamental, or universal and what is rather particular to the cultural and social context in which it is being studied’ [11, p. 11367].

Here, we wish to argue that it is reasonable to extend this position to non-human animals (henceforth: ‘animals’). Already in the same PNAS issue, there is one study highlighting that groups of chimpanzees differ from one another in their social dynamics, despite experiencing similar socio-ecological conditions [12]. More generally, in the light of the increasing evidence suggestive of the presence of group differences in the social behaviour of animals (e.g. [13–16]), we believe a similar cautioning—i.e. against the implicit assumption that individuals of the same species share a uniform psychology—is justified for the study of animal behaviour. By taking chimpanzees (Pan troglodytes) as an illustrative case, we argue that (i) IGV in animals is sufficiently documented to be taken seriously, (ii) its presence could plausibly resolve several scientific controversies, and (iii) without estimating the magnitude of IGV, we are premature in drawing species-typical conclusions. Lastly, we outline a pragmatic protocol for constructively incorporating the effects of IGV in animal studies.

2. What is intergroup variation and how does it emerge?

While animals of the same species have many traits in common, every single individual is also marked by distinct features with regard to both its genotype (with the exception of clones) and phenotype. This variation between individuals within the same species is referred to as intraspecific variation [17] and can be the result of both ultimate (genetic variation, developmental plasticity [8]) and proximate (ecology, learning) processes (e.g. [18,19]). However, variation within a species does not only occur on the level of individuals but is also possible on the level of populations and groups. IGV refers to variation between different communities of the same species (e.g. [14,20,21]. Thus, IGV is not observable in an isolated individual, but is a group-level phenomenon that comprises traits that show some stability within one group but can vary across other groups. Given its group-level nature, typically, IGV is spurred by a homogenizing force, for instance, a differential set of ecological affordances (e.g. availability/accessibility of food resources) and/or social determinants like group size and learning biases, most prominently within-group conformity [22–24]. Thus, variation between animal groups of the same species can arise through differences in ecological and/or demographic conditions [25], but also through socio-cognitive mechanisms [12,24].

Here, we will primarily focus on group-level variation in social behaviour and elucidate how its existence could possibly account for controversies across experimental studies on great ape behaviour and cognition (e.g. cooperation [26–29], prosociality [30–36] (van Leeuwen EJC, DeTroy SE, Kaufhold SP, Dubois C, Schütte S, Call J, Haun DBM 2016, unpublished manuscript) and inequity aversion [37–42]. For its increasingly recognized reach (e.g. [43–45]), the main focus of our piece is on cultural IGV, i.e. behavioural variation across groups owing to social learning within groups.

3. Cultural intergroup variation

Cultural IGV develops proximately as a response to ecological factors through the mechanism of social learning and can arise both between and within generations [43]. Ecological factors can be coarsely defined as all aspects of the environment that affect an organism's reproductive success. If ecological factors differ between groups of the same species, different group-specific behaviours (i.e. cultural IGV) can emerge. For instance, the absence of appropriate stone tools could prompt groups of chimpanzees to create and maintain a tradition of nut-cracking with wooden hammers, while other groups, with an abundance of stone tools in their territory, may resort to stone instead of wood technologies (cf. [16]). Conspecifics constitute a particularly influential ecological factor in social species, which comprises the complex (polyadic) interaction patterns among individuals, both within- and between groups. Owing to a constant tension between the overlapping needs of conspecifics, in conjunction with the benefits that all individuals may reap from group-living (e.g. [46,47]), behavioural phenotypes, especially in gregarious species, are opportunistically transient and as such prone to induce individual- and group-level variation. In particular, the capacity to learn socially—i.e. learning that is influenced by observation of, or interaction with, a conspecific, or its products [48]—has been identified as a source of both intraspecific variation and IGV in behavioural tendencies, not only for humans, but also for many animal species across a wide range of taxa, for instance, in birds [49], cetaceans [50], ungulates [51], insects [52] and primates [53]. Typically, when social learning is involved, IGV emerges owing to an original innovation within one particular group leading to a group-specific behavioural variant by means of within-group copying (e.g. [54]) and possibly a mechanism mitigating the eroding effects of dispersal and random drift, like conformity (e.g. [55]). Cultural IGV in behaviour can express itself both qualitatively, in terms of novel behaviours, but also quantitatively, in terms of the frequency of common behaviours. Studies of animal culture have initially focused on novel behaviours (e.g. tool-use) because the presence or absence of behaviours across groups with similar ecologies can be a salient indicator of cultural behaviour (e.g. [53]). However, groups can also differ with regard to the frequency of commonly performed behaviours (e.g. grooming, aggression) owing to culture (e.g. [12,56–58]). Observing quantitative culture requires more extensive data collection in terms of behaviour sampling within and between groups. Yet, in our view, such extended investments are worthwhile because of the potent impact quantitative culture may have on local adaptive landscapes. Culturally sustained interaction patterns can become part of individuals' selective environment [56,59], which opens up the possibility of gene–culture coevolution in animals [43,50]. For example, different killer whale ecotypes have developed distinct genetic adaptations for digesting proteins of either mammals or fish, depending on the specific cultural food preferences displayed by the respective groups over multiple generations [43,60]. This example of gene–culture coevolution in a non-human species is reminiscent of the evolution of lactase persistence in certain human populations [10] and emphasizes the importance of studying not just isolated cultural traditions in animals (e.g. nut-cracking in chimpanzees), but rather long-term patterns of social interactions in relation to local customs, and their potential genetic signatures [43,45,59].

4. Intergroup variation in chimpanzees: a synopsis

Chimpanzees show a wide variety of IGV for which several mechanisms have been identified. For instance, Western female chimpanzees (Pan troglodytes verus) have been reported to be more gregarious than their Eastern (Pan troglodytes schweinfurthii) counterparts [61], which is suggestive of the workings of genetic predispositions, although ecological factors (e.g. differing densities/probabilities of food abundances) might similarly, or even simultaneously, exert effects on local sociality [62]. Ecology has played an essential role in explaining social relationships among (especially female) primates in general [63,64]. The respective theoretical framework was coined the ‘socio-ecological model’ [63] and purported to explain social group structures by integrating ecological factors (e.g. predation risk and food abundance) with additional determinants like the risk of infanticide and habitat saturation (see e.g. [63–68]). More recently, social learning has been identified as a substantial driver of IGV in chimpanzees (i.e. cultural IGV), causing not only group-specific behavioural variants like spear hunting [69], nut-cracking [16] and handclasp-grooming [70], but possibly also substantial variation in the very fabric of within-group sociality, for instance, in terms of spatial closeness of group members (in the presence of valuable resources [71]) and grooming patterns [12] (for similar findings in other species, see: olive baboons (Papio anubis) [56], vervet monkeys (Chlorocebus pygerythrus) [14,58], sperm whales (Physeter macrocephalus) [72]). The perceived importance of social learning in shaping non-human primate behaviour has even increased to the extent that some scholars have proposed to integrate the capacity to learn from others into the ‘null-model’ aimed at understanding primate behaviour [73].

5. Reconciling scientific inconsistencies

While IGV has been acknowledged and studied by scholars working with wild chimpanzee populations (e.g. [62,74,75]), experimental studies with captive chimpanzees rarely include the possibility of IGV in their study designs and discussions. Given that experimental studies typically involve only one chimpanzee group, the tendency to avoid speculations about the influence of IGV is understandable for each single experiment. However, a systematic neglect of IGV across many studies can lead to a distorted view of what constitutes typical chimpanzee social behaviour. For instance, there is a long-standing and unresolved debate about whether chimpanzees are inequity averse or not (cf. [37–42]). Despite unavoidable differences in applied methodologies across studies (although see [38] and [42] for reporting contradictory findings with the exact same procedure), it is conceivable that chimpanzee groups may differ in their expression of inequity aversion. A hint at the possible effect of group-specific dynamics on inequity aversion was already implicit in the original study, wherein two subgroups were found to respond differently to inequitable conditions [38]. The circumstances and extent to which chimpanzees use cooperative strategies have also been debated and studies yielded mixed results [26–29]. Considering the degree of IGV with regard to social dynamics might help explain how the propensity for cooperation varies depending on certain group traits such as social tolerance [76] or steepness of hierarchies [77,78]. Similarly, the inconsistent results with respect to chimpanzees' ‘prosocial behaviour’—all acts that alleviate conspecifics' needs or improve their welfare [79]—may be an artefact of single-group studies and thus ultimately, at least partly, attributable to IGV (e.g. [30–32]). In short, the conclusions from experimental studies on chimpanzees’ prosociality range from ‘indifferent to the welfare of unrelated group members' [33] to ‘spontaneously occurring prosocial choices without solicitation’ (paraphrased from [34]). It has been shown that task-designs can influence chimpanzees' prosocial behaviour [35], but similar to the rationale of the ‘individual-differences’ approach (e.g. [36]), and in the light of the evidenced IGV in chimpanzees so far, we conjecture that chimpanzee groups may differ from one another in their expression of prosocial behaviour as well (cf. van Leeuwen EJC, DeTroy SE, Kaufhold SP, Dubois C, Schütte S, Call J, Haun DBM 2016, unpublished manuscript). In addition to adopting a multi-group approach, one way of testing this conjecture would be to focus on migrating individuals and assess their behavioural changes accordingly (e.g. [55,80]). Overall, we note two important considerations: (i) IGV may be more likely for expressions of propensity (e.g. prosociality) than for capacity (e.g. theory of mind) 1 and (ii) inferences from single-group studies about species-typical behaviour need to be evaluated with caution (also see [25]). The latter consideration pertains especially to species for which substantial IGV could be envisaged. In §6, we address this issue in more detail.

6. How to go from here?

The perils of neglecting IGV encompass inadequate scientific scrutiny leading to premature and possibly biased ‘species-typical’ generalizations, especially in behavioural experiments that use a small sample size of subjects from the same group. In turn, such inaccurate accounts can cause artefactual inconsistencies in research findings and generate erroneous phylogenetic approximations. Beyond highlighting the need to account for cultural IGV, when a multi-group approach is not readily possible, we propose the following incremental protocol towards scientific improvement: (i) assessment of the potential for IGV in the species under study by means of literature review, (ii) interpretation of outcomes of single-group studies as representative of a specific group rather than the entire species, (iii) application of a methodologically simple assay across multiple groups within the study species, and (iv) incorporation of at least one ‘replicate’ group to validate the findings of the test-group.

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Environmental and Geographic Studies

Environmental Science and Geographic Information Systems are two fields that are becoming increasingly important in today’s society. These projects and presentations focus on different aspects of biology, environmental science, and geographic studies such as mapping, zoning, and spatial modeling. Specific scholarship and research address an assortment of issues including historical matters, animal and marine conservation and protection, global warming and climate change, water quality, rising sea levels, and sustainable land redevelopment.

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Time: 12:30 – 1:30 p.m.

The Urban Coast Institute’s Heidi Lynn Sculthorpe Scholars Seminar
Moderated by: Tom Herrington, Associate Director, Urban Coast Institute

  • Aidan Bodeo-Lomicky, Junior, Marine Environmental Biology Policy
  • Breana DiRenzi, Senior, Marine Environmental Biology Policy
  • Avery Jackson, Graduate Student, Computer Science
  • Nicole Owenburg, Graduate Student, Clinical Mental Health
  • Johanna Vonderhorst, Senior, Chemistry

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Tracking Movements and Migrations of Sandbar Sharks (Carcharinus plumbius)
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Environmental Control on Harmful Algal Bloom (HAB) Toxicity in Coastal Lakes

Intermolecular interactions of liquefied petroleum gas-alcohol mixtures with phyllosilicates

Asynchronous Poster Presentations

Counting Diamondback Terrapin in Aerial Drone Image Using Supervised Image Classification

Rebecca Berzins, Senior, Marine & Environmental Biology & Policy

Mapping War Memorials and Monuments Relative to Population and Demographic Attributes in New Jersey

Mark Cianciosi, Graduate Student, Anthropology

Ranking Migratory Areas Around Vernal Pools for Spotted Salamander Based on Spatial Metrics

Hannah Craft, Senior, Marine & Environmental Biology & Policy

Aerial Drone Survey and High-Resolution Terrain Examination of a Revolutionary War Era Shipwreck Site

Breana DiRenzi, Senior, Marine & Environmental Biology & Policy

Mapping the Migratory Range of Striped Bass (Morone saxatilis) Based on Catch-and-Release Data from New Jersey

Sarah Gillogly, Graduate Student, Geographic Infosystems

Spatially Explicit Wave Modeling of the Navesink River, New Jersey

Richard Kane, Junior, Marine & Environmental Biology & Policy

Mapping Water Quality Parameters Related to Red Tide Events in the Gulf of Mexico

Logan Murphy, Senior, Marine & Environmental Biology & Policy

Assessing Relation Between Historic Sea Surface Temperatures and Damage to Coral Reefs in Caribbean

Nicholas Occhiogrosso, Junior, Marine & Environmental Biology Policy


Sampling sites

The two sampling sites used in this study are part of a long-term experiment by the Noble Research Institute aiming to understand the factors that regulate switchgrass establishment in marginal soils. Each plot measures 22 m by 27 m. The sandy loam site (SL) is located in Burneyville, Oklahoma (33.882083, − 97.275233), and the clay loam site (CL) is located in Ardmore, Oklahoma (34.172100, − 97.07953). Soil pH, soil organic matter, water content, and plant available N and P were determined from soil samples collected from each site prior to the start of the experiment following common analytical procedures. Briefly, 10 g of soil were used for determination of gravimetric moisture, pH measurement in water, organic matter content using combustion, and plant available P using the Mehlich III extraction method [78]. Same amount of soil was used for KCl extraction and the extract used to measure NH4 + and NO3 − content using colorimetric assays. Soil properties included in this analysis are presented in the Supplementary Dataset 1 (Tab 1 and Tab 2).

Soil sampling

Five-hundred SG seedling plants (Panicum virgatum) were planted in each cultivated site in May 2016, and 30 were randomly selected from each site for continuous rhizosphere and bulk soil sampling using a 5 cm diameter by 20 cm deep soil corer. These selected plants were sampled at five sampling points corresponding to different phenological stages of the switchgrass plants: T1—vegetative growth (June), T2—late vegetative growth (July), T3—reproductive growth (August/September), T4—maximal growth (October), and T5—senescence (November). Roots were separated from each core and transferred to a 50 ml centrifuge tube, while leftover soil was labeled as bulk soil and stored at − 80 °C until further processing, yielding a total of six-hundred samples. Separated roots were processed immediately to wash the 1–2 mm of attached soil (rhizosphere soil) for DNA extraction as follows: Tubes containing the roots received 50 ml of 1X phosphate buffer supplemented with 0.35% tween 20, inverted 3–4 times, vortexed for 10 s, and sonicated at a frequency of 100 (1/s). Samples were then centrifuged at 2500×g for 5 min. Roots were removed with sterile tweezers, and the leftover material was filtered through a sterile funnel made of a polypropylene mesh with 1 mm pores. The flow-through liquid was collected in a 50 ml centrifuge tube and centrifuged at 2500×g for 5 min, the supernatant removed, and the residual soil stored at − 80 °C. For uniformity purposes, aliquots of bulk soil were also washed and concentrated with the same procedures used for the rhizosphere soil, prior to DNA extraction.

DNA extraction

Aliquots of 0.2 g of washed soil (rhizosphere or bulk soil) were transferred to a 2-ml Lysing Matrix E tube (MP Biomedicals, Solon, OH, USA), which received 500 μl of extraction buffer (5% CTAB, 0.5 M NaCl, 240 mM K2HPO4, pH 8.0) and 500 μl of 25:24:1 phenol:chloroform:isoamyl alcohol. The samples were then bead beaten in a Fast Prep instrument (MP Biomedicals, Solon, OH, USA) at 4 m/s for 30 s, and centrifuged at 16,000 g for 5 min. The supernatant was transferred to a MaXtract high density tube (Qiagen, Germantown, MD, USA) containing 500 μl of chloroform:isoamyl alcohol (24:1), and the lysis procedure repeated and supernatants collected in their corresponding tubes. The samples were centrifuged at 10,000×g for 1 min at 4 °C, and the supernatants transferred to a microcentrifuge tube containing 1 ml of isopropanol and 1 μl of linear acrylamide (Ambion, Grand Island, NY, USA). The DNA/RNA mixture was precipitated by incubating for 10 min at room temperature and centrifuged at 10,000×g for 5 min at 4 °C, and the isopropanol removed. The obtained pellet was washed with 70% ethanol and centrifuged at 10,000×g for 1 min at 4 °C. The ethanol was completely removed, and the pellet dissolved in DEPC-treated water. The crude extracts were transferred to a 96-well plate and purified using magnetic beads as follows: Each sample received 1.2× volume of 2% magnetic beads (Speedbeads, GE Healthcare, Chicago, IL) in 18% polyethylene glycol 8000, 1 M NaCl, 10 mM Tris-HCl, pH 8, 1 mM EDTA pH 8, 0.05% Tween 20. The plate was then incubated in a shaking incubator at 100 rpm for 10 min. The plate was placed on a magnetic stand and allowed to settle for 5 min, and the supernatant removed. Each well was washed twice with 200 μl of 80% ethanol and incubated for 1 min. Then, the ethanol was removed, the samples left to dry for 5 min, and the beads were eluted with 30 μl of elution buffer. The samples were transferred to a shaking incubator and incubated at 500 rpm for 5 min, and transferred back to the magnetic stand for 5 min. The resulting supernatant containing the DNA was transferred to a clean plate and the DNA concentration determined with the use of a Qubit fluorometer (Thermofisher Scientific, Waltham, MA). Out of the 600 samples, 582 yielded DNA that was of sufficient quality for amplicon library preparation. From the 582 samples, 293 belonged to the CL site (145 from the rhizosphere and 148 from bulk soil), and 289 from the SL site (142 from rhizosphere and 147 from bulk soil). Detailed information can be found in the Supplementary Dataset 1, Tab 4.

Amplicon library preparation and sequencing

The amplicon libraries were prepared with a two-step barcoding approach as described by Herbold et al. [79] with some modifications. First, the target markers were PCR-amplified with diagnostic primers synthesized with a 16 bp head sequence (5′-GCTATGCGCGAGCTGC-3′, modified from Rudi et al. [80]) at the 5′ end for 25 cycles. After the 25 cycles, the PCR was paused, and a second set of primers consisting of the 16 bp head sequence and a library-specific 8 bp barcode [81] was added and amplified for 5 more cycles. Each PCR reaction (45 μl in the first step, and 50 μl in second step) consisted of 10 ng of DNA template, 1 unit of Titanium Taq DNA Polymerase (Takara Mirus Bio Inc., WI, USA), 100 ng of bovine serum albumin, 1× Titanium Taq PCR buffer, 0.2 mM dNTP mix, 0.2 μM of forward and reverse diagnostic primers, and 5 μM of the library-specific barcodes (added during the last 5 PCR cycles). Thermocycler conditions were 95 °C for 3 min 95 °C for 30 s, 60 °C (for diagnostic primers), or 52 °C (barcodes) for 30 s 73 °C for 5 min. Obtained PCR products were inspected by gel electrophoresis, purified using magnetic beads following the protocol for magnetic purification described in the DNA extraction section, and quantified using a Qubit fluorometer. Products were equimolarly combined and concentrated by bead purification to create sequencing libraries, which consisted of 200 samples at a time (200 out the 600 samples), yielding a total of 3 libraries. One microgram of each pooled library was used for the ligation of adapters for Illumina sequencing using the NEBNext Ultra II DNA Library Prep kit for Illumina (New Englands Biolabs). Each adapter-ligated library was quantified by qPCR using the NEBNext Library Quantification Kit. Each library was spiked with 10% phiX and sequenced on an Illumina Miseq using the Miseq Reagent kit v3.

Before amplifying all our samples, we tested two-sets of primers, targeting the V1V2 and V9, 18S rRNA regions, for the characterization of soil protist communities in our soils. The V1V2 primers were those published by Fonseca et al. [82], FO4 (5′-GCTTGTCTCAAAGATTAAGCC-3′) and R22 (5′-CCTGCTGCCTTCCTTRGA-3′). The V9 primers were previously published by Amaral et al. [83], 1380F (5′-CCCTGCCHTTTGTACACAC-3′), and 1510R (5′-CCTTCYGCAGGTTCACCTAC-3′). The results showed that, when used in our soil samples, the V1V2 amplified more non-target sequences than the V9 primers (Wilcoxon test p = 4.9 × 10 −7 ), with 46.6% and 26.4% of the total belonging to fungi (for the V1V2 and V9 markers, respectively Supplementary Fig 9). Our analysis also showed that the V9 primers amplified significantly more sequences (Wilcoxon test p = 7.3 × 10 −9 ) belonging to the protist division Alveolata (27.8% for V9 vs. 4% for V1V2), and also detected sequences belonging to the division Apusozoa, Hacrobia, Protalveolata, and the phyla Mesomycetozoa and Rhodophyta which were not amplified by the V1–V3 primers. Since the V9 primers outperformed the V1V2 pair in representing protists and discriminating fungal sequences (Supplementary Fig. 9), our subsequent analyses were conducted with the V9-18S rRNA primers.

Sequence analyses

Libraries were demultiplexed based on their unique barcodes using custom scripts and trimmed to the same length. Sequences were dereplicated and sorted by decreasing abundance using USEARCH v11 [84]. The dereplicated sequences were denoised, de-novo chimera filtered, and zero-radius OTUs (ZOTU) generated using unoise3 from USEARCH v11. Resulting ZOTUs, which are a form of amplicon sequence variants (ASVs), were screened against the NCBI nucleotide database using Blastn with an e-value of 1e-5 and keeping 100 hits. The Blast file was imported into MEGAN Community edition v.6 [85] software for taxonomic parsing to identify ZOTUs of protist origin. Filtered ZOTUs were taxonomically characterized against the PR2 database v.4.12.0 [34] using Sintax (USEARCH v11) with a cutoff of 0.8, and genus as the maximum taxonomic level. Total sequences were mapped against protist ZOTUs at a 97% identity and an abundance table was generated that was subsequently transformed into a biom table. Protists ZOTUs were then aligned using Clustalw, and the alignment was used to generate a phylogenetic tree with IQ-TREE 2 [86] using the model GTR+F+R10 (identified using model finder) and ultrafast bootstrap approximation (UFBoot) with 1000 replicates. The abundance table, mapping file, and phylogenetic tree were imported to the R software using the Phyloseq package [87].

Data analyses

Once imported into R, 43 underperforming libraries (with less than 1000 protist sequences) were removed from the dataset. For alpha diversity, measured as Observed Richness and the Shannon Index, the libraries were subsampled to the minimum number of sequences 100 times using a seed number of 3. A rarefaction analysis was performed to show that this level of subsampling represented the communities for the chosen diversity indices (Supplementary Fig. 1). Temporal variability of within-sample diversity was assessed by calculating the coefficient of variation (CV) for observed richness and the Shannon index for each plant and its corresponding rhizosphere and bulk samples in both field sites through time [88]. Individual values were used to determine the per sample median and mean values across environments (bulk/rhizosphere) per sampling site, with higher values indicating more variables communities [88]. Statistical significance of the differences in alpha diversity and temporal variation was assessed using the Kruskal-Wallis test and pairwise comparisons with the Wilcoxon test and the Benjamini-Hochberg method for p value adjustment. For community composition analyses (beta-diversity), data was normalized using the variance stabilization approach in DESeq2 [89], and a weighted Unifrac distance matrix was generated using the vegan package. The obtained distance matrix was ordinated using multidimensional scaling in Phyloseq. The samples were categorized based on sampling site (SL, CL), environment (bulk, rhizosphere), sampling time (T1 to T5) the effect of these categories on data variation was tested with Adonis (nonparametric permutation multivariate analysis of variance), performed with 1000 permutations. Temporal dispersion of community composition was assessed using betadisper and permutest (R package: vegan). Correlations between environmental data and community composition were tested using envfit (R package: vegan). Environmental data was fitted onto the ordination space with a gam model using ordisurf (R package: vegan). Significant differential abundance of protists groups in the rhizosphere relative to the bulk was determined using DESeq2 (p < 0.01), which adjusts p values using false discovery rate (FDR) for multiple comparisons. For the differential abundance analysis, the data was agglomerated to the maximum identified taxonomic level for each ZOTU, and the data are discussed as differential abundance of protists populations rather than for exact sequence variants. Preferential feeding or nutrition strategies for the protists populations was described only for those protists identified at the genus level based on published reports [17, 35, 38, 90–96].

Network construction and analysis

To investigate the dynamics of protist community patterns over time in both marginal soils, we used random matrix theory (RMT)-based co-occurrence association network analysis. Networks were constructed for rhizosphere and bulk soil at each time point based on center log ratio transformed abundance data, which was normalized using the Microbiome R package. Prior to normalization, the data was subsetted for each sampling site (SL/CL), environment (bulk/rhizosphere), and time point (T1 to T5), and underperforming samples (with less than 1000 sequences) removed while keeping a minimum of 10 replicates per dataset. Only ZOTUs detected in at least 70% of each subset of replicated samples were used for network reconstruction. Network reconstruction was conducted with the Molecular Ecological Network Analyses pipeline (MENAP, with the following settings: for missing data fill blanks with 0.01 if data have paired values do not take logarithm as the data was already CLR normalized use Spearman Correlation similarity matrix calculate by decreasing cutoff from the top and for speed selection, regress Poisson distribution only. RMT was used to automatically identify the appropriate similarity threshold for network reconstruction [97, 98]. Modules were detected using the greedy modularity optimization method and network topological properties, including number of nodes, links, and modules, were calculated according to Deng et al. [43]. Proportion of negative over positive correlations was also calculated from the outputs of MENAP. The connectivity of each node was determined based on its within-module connectivity (Zi) and among module connectivity (Pi) [99], and used to classify the nodes based on their topological roles that they play in the network (Table 1 and Supplementary figure 2). The general classification was based on categories defined in Deng et al. [43] that considers four categories: module hubs (highly connected nodes within modules, Zi > 2.5), network hubs (highly connected nodes within modules, Zi > 2.5, Pi > 0.62), connectors (nodes that connect modules, Pi > 0.62), and peripherals (nodes connected in modules with few outside connections, Zi < 2.5 and Pi < 0.62) [43, 64, 97].

Inferring community assembly mechanisms

The relative influences of community assembly processes were assessed by a phylogenetic bin-based null model framework, iCAMP, which was recently reported with substantially improved performance [44]. Briefly, iCAMP divided taxa into different phylogenetic groups (bins) to ensure adequate phylogenetic signal to infer selection from phylogenetic diversity then, the processes (selection, dispersal, and drift or others) dominating each bin were identified, according to the deviation of observed phylogenetic and taxonomic diversity from random patterns simulated by null models finally, the relative abundance of bins governed by each process was aggregated to evaluate its influence on entire community assembly. The rarefied protist ZOTU table was used to be applicable to ecological null model. Then, the analysis was performed separately for the two sites, using the “iCAMP version 1.2.9” with recommended default settings on a pipeline built on Galaxy platform ( Results were summarized for each group of samples from the same habitat (rhizosphere or bulk soil) and the same time point, and then the succession of relative importance of each ecological process was compared between habitats and sites. The significance of differences was calculated based on bootstrapping with 1000 replications.

Part 2: Choanoflagellate colonies, bacterial signals and animal origins

00:00:07.22 So, Hello.
00:00:09.01 My name is Nicole King.
00:00:10.11 I'm an investigator in the Howard Hughes Medical Institute
00:00:12.02 and a professor at the
00:00:13.26 University of California at Berkeley,
00:00:15.20 and I'm excited to be here today
00:00:17.17 to tell you about organisms
00:00:19.15 that we study in my laboratory, the choanoflagellates,
00:00:23.01 and tell you about how they interact with bacteria
00:00:24.09 and how these interactions
00:00:26.11 might inform us about animal origins.
00:00:29.27 Now, I want to provide a little bit of introduction
00:00:32.10 to the motivation for the research in my lab.
00:00:34.25 There's been a lot of focus in the past
00:00:37.20 on understanding how different animal body forms diversified,
00:00:41.21 and understanding how different animals
00:00:43.18 are related to each other on the phylogenetic tree.
00:00:45.29 But, in fact, we know relatively little
00:00:47.29 about the nature of the organisms
00:00:49.22 from which animals first evolved,
00:00:51.25 and in my laboratory we're particularly interested
00:00:53.28 in understanding the genomic innovations
00:00:56.19 and the influences of cell biology
00:00:59.29 and interspecies interactions
00:01:01.29 and understanding how that might have contributed
00:01:04.10 to what we call the transition to multicellularity.
00:01:06.13 That is, how did ancestrally unicellular organisms
00:01:10.15 evolve into organisms
00:01:13.05 that are capable of simple multicellularity,
00:01:15.09 such as this hypothetical colony.
00:01:18.26 In Part I of my talk,
00:01:23.22 I previously spoke about how
00:01:26.05 an unusual group of organisms called the choanoflagellates
00:01:29.03 can help us understand animal origins,
00:01:31.24 and I told you that
00:01:34.13 the first animals likely had genes in their genomes
00:01:37.25 that had evolved much earlier.
00:01:39.15 And so, by comparing choanoflagellates to animals,
00:01:43.06 we're learning about the nature of the first animals.
00:01:45.22 In this part of my talk,
00:01:47.29 I'm going to focus on one particular species
00:01:50.16 of choanoflagellate,
00:01:52.15 and I'm going to tell you that this choanoflagellate
00:01:54.09 actually can transition from being single-celled
00:01:56.17 to having simple multicellularity,
00:01:58.29 and I'm going to tell you about how we've been developing this
00:02:01.29 into a new model system
00:02:03.24 so that we can learn about how that transition,
00:02:06.02 from being single-celled to multicellular,
00:02:08.09 is regulated.
00:02:09.23 And, what we don't know now,
00:02:11.19 but we hope to learn,
00:02:13.05 is whether the regulation of multicellularity in this organism
00:02:15.21 might give us specific insights into the ancestry
00:02:18.14 of multicellularity in animals.
00:02:22.08 Now, the organisms that we study
00:02:24.16 are in fact not animals.
00:02:26.18 They are the sister group of animals.
00:02:28.11 They are called choanoflagellates
00:02:30.06 and they sit on this very special part of the phylogenetic tree
00:02:32.26 because they are the closest living relatives of animals.
00:02:36.04 And our understanding that choanoflagellates
00:02:38.19 sit here on the tree,
00:02:40.07 and that they are our, essentially, evolutionary cousins,
00:02:42.04 comes from multiple lines of evidence.
00:02:44.08 It comes from comparisons of the cell biology
00:02:46.15 of choanoflagellates to the cell biology of animals.
00:02:50.02 It comes from many different independent types
00:02:52.11 of phylogenetic analyses.
00:02:54.12 And it comes from comparisons between
00:02:56.24 the genomes of choanoflagellates and the genomes of animals.
00:03:00.05 From all of these different sets of data,
00:03:02.03 it's now become very clear
00:03:04.11 that the study of choanoflagellates,
00:03:06.14 our closest living relatives,
00:03:08.24 tells us about the biology of our last common ancestor.
00:03:14.05 So, I introduced this in Part I,
00:03:16.24 but I just want to quickly
00:03:19.21 review the biology of choanoflagellates
00:03:22.00 because it becomes essential for understanding
00:03:24.00 what I'm about to tell you in the later part of this talk.
00:03:28.09 Choanoflagellates are microbial eukaryotes.
00:03:31.05 They have a spheroid.
00:03:34.05 spherical or ovoid cell body,
00:03:36.14 an apical collar of actin-filled microvilli,
00:03:39.11 and a long apical flagellum.
00:03:41.14 And this flagellum can undulate
00:03:43.26 from side to side,
00:03:45.21 and this undulation creates water currents
00:03:47.26 that allow choanoflagellates
00:03:50.26 to swim through the water column,
00:03:53.22 but these water currents also pull water
00:03:57.27 from the media up against the collar,
00:04:00.04 and those water currents can carry bacteria,
00:04:02.22 and bacteria are actually the primary prey target
00:04:06.10 for choanoflagellates.
00:04:07.28 Choanoflagellates are voracious bacteriovores.
00:04:10.12 They love eating bacteria,
00:04:11.27 they're very good at,
00:04:13.22 and this is an essential part of their biology
00:04:15.28 - their ability to capture and ingest bacteria.
00:04:19.24 So, choanoflagellates have a lot of really interesting aspects
00:04:22.10 to their biology,
00:04:24.13 one of which, obviously, is this ability to eat bacteria,
00:04:27.02 but one of the aspects of their biology
00:04:29.03 which really excited me when I first learned about it
00:04:31.13 as a postdoc
00:04:33.04 is that some choanoflagellates can form
00:04:35.11 these beautiful multi-celled colonies.
00:04:38.04 These colonies are in fact, to me,
00:04:40.18 reminiscent of some types of marine invertebrate embryos,
00:04:44.19 and these colonies
00:04:47.11 raise all sorts of questions about
00:04:49.29 how do the cells interact
00:04:52.03 and how is this process regulated?
00:04:55.05 Moreover, you'll remember that I told you
00:04:58.21 that one of the major questions in my laboratory
00:05:00.28 is how, evolutionarily,
00:05:03.09 did the ancestors of animals evolve the ability
00:05:06.08 to form simple multicellular morphologies.
00:05:08.25 And here we have, in living color,
00:05:11.08 an organism that actually does it,
00:05:13.08 every day.
00:05:15.12 And so, what we've been doing in my laboratory
00:05:17.02 is to understand.
00:05:18.23 is to study this process
00:05:21.07 in minute detail the same way
00:05:24.23 that a Drosophila biologist might try to study
00:05:26.29 how a fruit fly goes from an egg
00:05:29.00 to an embryo to an adult,
00:05:31.23 or a mouse biologist
00:05:34.07 studies the same process of development.
00:05:36.02 We're trying to study, in mechanistic detail,
00:05:38.22 the process by which this organism, S. rosetta,
00:05:41.16 goes from being a single cell
00:05:44.05 to a multi-celled colony.
00:05:46.19 And, in particular,
00:05:47.25 we're focusing on trying to understand
00:05:50.13 the mechanisms of cell adhesion and cell signaling
00:05:52.03 within the colony.
00:05:53.09 We're trying to understand
00:05:54.24 what triggers this transition
00:05:56.13 from being single-celled to colonial,
00:05:58.11 and eventually we hope to learn
00:06:02.00 whether the mechanisms underlying this transition
00:06:03.22 in choanoflagellates
00:06:05.15 are related to mechanisms underlying animal development,
00:06:08.02 in which case we would infer
00:06:10.01 that they are ancient and evolved
00:06:12.07 before the origin and diversification of animals.
00:06:14.23 So, I'm going to tell you a lot
00:06:17.18 about this organism, S. rosetta.
00:06:19.14 And, the first thing I need to tell you
00:06:21.23 is that it does a lot of exciting things.
00:06:24.20 You know, I think many of us think
00:06:26.27 about protozoa as being simple organisms
00:06:31.01 that lead a rather mundane life,
00:06:34.07 but this organism not only can switch between
00:06:37.18 being single-celled and colonial,
00:06:39.20 it actually has a really wild and crazy life history.
00:06:42.18 It has many different morphologies
00:06:45.24 that it can produce,
00:06:47.22 and these are all coming
00:06:51.28 from an organism.
00:06:53.14 a single genotype is encoding
00:06:55.03 for all of these different forms,
00:06:57.09 and many of these forms
00:06:59.10 can differentiate into other forms.
00:07:02.02 So, this cell in the center
00:07:04.02 we call the 'slow swimmer',
00:07:06.08 and cultures that only have slow swimmers in them
00:07:09.03 are capable of producing rosette colonies,
00:07:13.02 chain colonies,
00:07:14.26 fast swimmer cells.
00:07:16.15 and these fast swimmers can differentiate
00:07:18.16 into these attached cells.
00:07:20.20 So, there's a lot of dynamic cell differentiation that's going on,
00:07:23.09 and it seems to have at least some
00:07:26.00 environmental component because we can,
00:07:28.05 in the laboratory, push the choanoflagellate
00:07:30.22 toward different types of cells
00:07:33.14 by changing the environmental conditions
00:07:35.10 in which we grow the cells.
00:07:37.23 For the sake of simplicity
00:07:39.11 and also to focus on things that we know the most about,
00:07:41.16 today, I'm only going to focus on
00:07:44.07 this part of the life history,
00:07:46.16 and try to understand,
00:07:48.25 what is it that allows this cell to differentiate
00:07:52.01 into these other multicellular forms.
00:07:54.16 In particular,
00:07:57.12 we're going to talk about the rosette form,
00:07:59.19 because this is the form in which S. rosetta
00:08:01.28 was actually isolated from nature,
00:08:04.03 and this is the one that is most similar
00:08:07.12 to the multicellular form
00:08:09.22 that we hypothesize
00:08:12.01 was required for the ancestry of animals.
00:08:14.21 So, we want to know,
00:08:16.21 how do rosettes form?
00:08:19.09 Are they forming
00:08:21.14 from multiple cells swimming together and sticking?
00:08:24.14 And that would be similar
00:08:26.05 to the slime mold Dictyostelium.
00:08:28.11 or are they more similar to animals
00:08:30.09 in the way in which they form?
00:08:31.24 That is to say,
00:08:33.12 does a single cell divide repeatedly
00:08:35.11 to form a rosette.
00:08:37.14 We also like to know,
00:08:39.08 how are the cells inside of the rosette
00:08:41.10 adhering to each other?
00:08:42.25 How do you get that stable structure?
00:08:44.20 Again, trying to draw analogies to embryogenesis.
00:08:48.09 And finally, we have this enigma,
00:08:52.09 which is that this single cell
00:08:54.10 is capable of producing three different types of morphologies.
00:08:59.22 It can divide to produce more copies of itself,
00:09:02.14 it can produce these chains,
00:09:05.26 or it can produce rosettes,
00:09:07.29 and we'd like to know
00:09:10.01 how is that differentiation process regulated.
00:09:13.14 So, let me start with question number 1:
00:09:15.13 how are these rosettes forming?
00:09:18.07 To investigate this
00:09:19.26 we used multiple different approaches,
00:09:21.23 but I think the simplest one is to just watch,
00:09:24.09 and what we found is that
00:09:27.00 when cultures were shifting
00:09:29.07 from having only single-celled individuals
00:09:31.29 to rosettes
00:09:34.11 it always happened through cell division.
00:09:36.29 And so, what you're going to see in this movie here
00:09:39.15 is that this is a founding cell that's going to divide repeatedly
00:09:42.23 to produce a spherical multi-celled colony.
00:09:45.18 So let's watch.
00:09:48.05 The single cell divides over and over again.
00:09:50.12 The cells remain attached,
00:09:52.18 and in the end of this 15 hour movie
00:09:55.28 we have a spherical colony.
00:09:58.01 And, this I think is also nicely shown here
00:10:00.10 in these stills taken by confocal microscopy.
00:10:04.04 Now, I want to make the point
00:10:06.27 that even though this looks 2-dimensional,
00:10:08.24 these colonies are actually 3-dimensional
00:10:10.23 and are producing a nice sphere.
00:10:14.06 Okay, so, the answer to our first question, then,
00:10:16.11 is that the rosettes are forming through cell division,
00:10:19.12 and that provides a very nice parallel
00:10:21.21 to the way in which embryos of animals form,
00:10:24.12 in which you have a single cell, the zygote,
00:10:27.03 and that zygote divides over and over again
00:10:29.07 to produce a multicellular embryo.
00:10:31.20 How are the cells in choanoflagellate colonies
00:10:34.22 actually sticking together?
00:10:36.11 And, to answer this,
00:10:38.14 we had to use electron microscopy.
00:10:41.14 If you look at choanoflagellate cells
00:10:43.20 using a scanning electron microscope,
00:10:45.29 what you see is that the cells are actually connected
00:10:49.11 by these fine intercellular bridges that you can see here,
00:10:52.28 and we think, although we don't know yet,
00:10:55.20 but we think that these are the product of
00:10:57.27 incomplete cytokinesis.
00:10:59.13 That is to say that the cleavage plane
00:11:01.14 that forms when cells are dividing
00:11:03.25 doesn't close completely,
00:11:05.09 and so there's a little remnant of membrane
00:11:07.23 that remains between those cells
00:11:10.01 and it produces this intercellular bridge.
00:11:14.11 But that's not the only source of cell adhesion
00:11:17.13 between these cells.
00:11:20.07 If you examine the cells under different conditions,
00:11:22.25 and then look at them either in SEM or in TEM,
00:11:27.28 what you can see is that there is
00:11:30.21 a fine meshwork of material covering the cells
00:11:33.12 and also filling the inside of the colony,
00:11:35.25 and this is actually extracellular matrix, or ECM.
00:11:38.29 And so, it's the combination of the intercellular bridges
00:11:45.07 and extracellular matrix
00:11:47.14 that is contributing to the structural integrity of the rosette.
00:11:51.21 Okay, so just to summarize, then,
00:11:53.18 I've just told you that when choanoflagellates form.
00:11:56.26 when S. rosetta forms rosette colonies
00:11:59.20 it forms it through incomplete cytokinesis,
00:12:02.05 and what I've also told you
00:12:05.00 is that the cells in these rosettes
00:12:06.22 are adhering through a combination
00:12:08.17 of intercellular bridges and extracellular matrix,
00:12:11.14 but of course I think the question
00:12:13.18 we should all be interested in and wondering about is,
00:12:16.24 how is this transition regulated?
00:12:19.28 And what determines
00:12:22.18 whether this single-celled form of S. rosetta
00:12:25.17 divides to produce more of itself,
00:12:28.15 or produces chains,
00:12:30.25 or produces rosettes?
00:12:32.20 What is determining the regulation
00:12:35.03 of this developmental switch?
00:12:37.10 And here's where I was really stymied in my research.
00:12:40.01 And so, what I need to tell you
00:12:42.16 is a story of frustration
00:12:45.04 that finally ended with serendipity
00:12:47.08 and I think an exciting new discovery.
00:12:50.27 So, let me back up and tell you
00:12:53.23 about how I started studying S. rosetta.
00:12:56.13 When I began my postdoc,
00:12:58.06 there were no labs out that were studying choanoflagellates,
00:13:01.25 and so I was fortunate to be taken into the lab
00:13:04.18 of a leading evo-devo researcher,
00:13:07.14 Sean Carroll,
00:13:09.06 but I had to go to the ATCC,
00:13:11.03 the American Type Culture Collection,
00:13:13.17 to work with a protistologist named Tom Nerad,
00:13:16.06 to learn how to study choanoflagellates.
00:13:18.00 And while I was there,
00:13:19.27 he and a group of other scientists
00:13:21.28 were studying diverse microbial eukaryotes
00:13:23.21 from the environment,
00:13:25.16 and he observed one choanoflagellate
00:13:27.08 that was capable of forming beautiful rosette colonies,
00:13:30.08 and this is S. rosetta.
00:13:32.12 So, he was kind enough to put S. rosetta into culture.
00:13:35.29 He isolated a single colony, grew it up,
00:13:38.18 and froze it down so that I could study
00:13:41.07 S. rosetta in perpetuity.
00:13:43.07 So, I brought it back to Madison,
00:13:45.10 where I was doing my postdoc,
00:13:47.02 and started growing this choanoflagellate.
00:13:49.02 And, let me tell you,
00:13:51.01 it was a very frustrating finding when I brought it back to Madison,
00:13:54.22 because S. rosetta cultures, in the laboratory,
00:13:57.29 rarely have rosettes.
00:13:59.24 They were largely unicellular,
00:14:01.22 and so you can see, here would be a best case scenario.
00:14:03.29 Lots of single-celled choanoflagellates,
00:14:06.04 you can see the round cells,
00:14:08.20 and only the occasional rosette colony,
00:14:11.28 and lots of bacteria.
00:14:13.20 And so, no matter what I did,
00:14:16.08 these cultures would not robustly form rosette colonies,
00:14:18.24 and so that meant that I wasn't going to be able
00:14:23.07 to do the types of experiments that I wanted to do
00:14:24.28 to study the mechanisms of rosette development.
00:14:27.26 So, I worked on this for a long time,
00:14:29.22 without success,
00:14:31.21 and then ended up bringing.
00:14:33.25 fortunately, other things worked,
00:14:36.11 but S. rosetta was recalcitrant,
00:14:38.21 and so I brought it with me when I started my own lab at Berkeley
00:14:41.24 and continued to experience frustration,
00:14:46.12 and continued to be unable
00:14:49.10 to get this thing to form rosettes
00:14:51.08 in any robust or predictable way.
00:14:53.06 And so, finally, I switched my research objectives
00:14:56.17 and decided that if I couldn't get rosette colonies
00:15:00.05 from this species,
00:15:02.12 at least I could sequence its genome,
00:15:04.09 and that might tell me something,
00:15:06.00 by comparing its genome to the
00:15:08.03 genomes of single-celled choanoflagellates,
00:15:09.11 might tell me something about the mechanisms regulating development.
00:15:12.17 And so, an undergraduate in the lab at the time,
00:15:15.03 Rick Zuzow, helped me to get this choanoflagellate
00:15:17.07 ready for genome sequencing,
00:15:19.17 and at one point.
00:15:20.20 one challenge I need to point out is that
00:15:22.21 because choanoflagellates eat bacteria,
00:15:24.29 this creates a real problem for genome sequencing
00:15:27.11 because the bacterial DNA
00:15:30.27 can make it difficult
00:15:34.29 to get a high-quality genome assembly from the choanoflagellate.
00:15:38.02 So, the first thing he had to do, then,
00:15:40.03 was to treat these cultures with cocktails of antibiotics,
00:15:45.24 and he tried two different cocktails of antibiotics
00:15:49.12 and they gave two very interesting results.
00:15:51.28 So, one cocktail of antibiotics,
00:15:54.11 actually, when he treated, when he used that,
00:15:57.28 it resulted in a culture that had a bloom of rosette development.
00:16:00.23 And so, you can imagine, we were thrilled!
00:16:03.00 It was so exciting and we had no idea
00:16:05.15 why this treatment with antibiotics
00:16:08.05 led to rosette development, but it did.
00:16:11.08 Perhaps even more interestingly,
00:16:13.08 when he treated with a different cocktail of antibiotics,
00:16:17.01 he recovered a culture that produced no rosettes, ever.
00:16:21.09 And so, we find that.
00:16:23.24 we found that different cocktails of antibiotics
00:16:26.04 led to different results,
00:16:29.14 and we started to wonder what was going on.
00:16:32.12 Now, it could have been
00:16:35.06 any of a number of possible explanations.
00:16:37.25 It could have been that the antibiotics
00:16:40.15 were directly stressing the choanoflagellates in different ways.
00:16:43.10 It could be that the choanoflagellates
00:16:45.13 were starving
00:16:48.27 when exposed to one set of antibiotics, but not the other.
00:16:51.20 But, it was hard to reconcile these different observations,
00:16:54.08 but the one possible explanation
00:16:57.12 that sort of was consistent with what we were seeing
00:17:00.11 was the possibility that bacteria from the environment
00:17:03.06 were actually regulating the switch to rosette development.
00:17:06.06 And so, to test that,
00:17:08.06 Rick took bacteria from the original environmental sample
00:17:11.15 and added them to this culture
00:17:14.01 that didn't form rosettes,
00:17:16.08 and asked whether those environmental bacteria
00:17:19.17 could stimulate rosette development
00:17:21.17 in these non-rosette forming cultures.
00:17:23.19 And, in fact, that did work.
00:17:26.22 So, environmental bacteria
00:17:29.00 were sufficient to induce rosette development.
00:17:31.20 So, that was very exciting,
00:17:33.21 very unexpected,
00:17:35.11 and of course the next thing we wanted to know was,
00:17:38.05 which bacteria were actually
00:17:41.19 providing this stimulus for rosette development?
00:17:44.00 And so, what Rick and other members of the lab did
00:17:47.00 was to go into this original environmental sample
00:17:48.29 and isolate multiple independent strains of bacteria
00:17:54.25 and test them one at a time
00:17:57.07 in this rosette-deficient culture,
00:17:59.10 and ask whether those bacteria
00:18:01.22 were capable of inducing rosette development.
00:18:04.20 And so, we tested 64 different environmental isolates,
00:18:09.12 one at a time,
00:18:11.12 and what we found in the end was that,
00:18:13.07 of all of these, only one species
00:18:15.13 was capable of inducing rosette development.
00:18:19.00 So, what was that species?
00:18:22.14 It was the previously undescribed
00:18:25.12 bacterial speices Algoriphagus machipongonensis.
00:18:29.01 So, we can add Algoriphagus to cultures
00:18:32.05 of single-celled choanoflagellates,
00:18:34.02 and that is sufficient to induce them
00:18:36.02 to form rosette colonies.
00:18:38.04 I need to make a couple of important points
00:18:40.12 about Algoriphagus.
00:18:42.22 First of all, it was co-isolated with S. rosetta,
00:18:45.03 so it's a natural environment co-habitant with S. rosetta,
00:18:49.00 and it's also a sufficient prey target.
00:18:52.13 So, we can grow S. rosetta
00:18:54.25 only in the presence of Algoriphagus
00:18:56.24 and it's perfectly viable
00:18:58.21 and it happily form rosette colonies.
00:19:00.26 The other exciting and interesting thing about Algoriphagus
00:19:03.22 is that it's a member of a much larger group of bacteria
00:19:06.04 called the Bacteroidetes,
00:19:08.15 and Bacteroidetes bacteria
00:19:11.15 are some of the most abundant bacteria in your gut,
00:19:14.06 and they're also abundant and important bacteria
00:19:17.09 in diverse environmental settings,
00:19:19.16 including the oceans and soil.
00:19:22.08 And, in each of these settings,
00:19:24.01 there's a growing interest in the ways in which
00:19:26.15 bacteria might be influencing the biology of eukaryotes
00:19:29.07 with which they're associated.
00:19:31.15 So, we're excited about the possibility
00:19:34.02 that this interaction which I've just described to you
00:19:36.12 might be used to help understand
00:19:39.16 the mechanisms underlying interactions
00:19:41.10 between bacteria and eukaryotes.
00:19:43.26 Now, why.
00:19:46.01 why would we be so excited about bacteria?
00:19:47.27 And I've hinted at that a little bit,
00:19:49.13 but I want to tell you.
00:19:51.05 I want to back up and give you a little bit of context
00:19:53.03 for why bacteria are such an important factor
00:19:58.08 to try to investigate
00:20:00.12 when you're thinking about animal origins.
00:20:01.23 And, to do that, I need to go in the way-back machine.
00:20:04.06 I need to remind you about the history of life on Earth.
00:20:08.06 And, to that do,
00:20:10.13 I'm going to use this time chart
00:20:12.13 in which we're thinking about life, the history of life,
00:20:14.07 starting with the present here on the top,
00:20:16.09 going back to the start of Earth
00:20:19.09 and the solidification of the crust,
00:20:21.13 and say that we think that the last universal common ancestor
00:20:24.25 of life
00:20:26.16 lived on the order of over 3 billion years ago.
00:20:29.05 And, the earliest fossil evidence
00:20:32.00 we have for life
00:20:34.00 is that of stromatolites.
00:20:36.16 These are large multicellular
00:20:38.26 aggregations of bacteria.
00:20:40.23 They are essentially a bacterial biofilm,
00:20:43.01 and we preserve representatives of these types of morphologies,
00:20:46.17 here, today.
00:20:48.25 Here is an example of a modern stromatolite,
00:20:50.26 and these complex forms
00:20:53.10 are produced by bacteria,
00:20:55.16 and there's a lot of really terrific work
00:20:57.19 that's being done to address
00:21:01.00 the ways in which bacterial metabolism
00:21:04.04 has influenced the geochemistry of Earth,
00:21:07.23 but also the life of other organisms.
00:21:10.27 And, what we now realize,
00:21:13.26 is that animals whose fossils
00:21:17.05 have not been recovered.
00:21:19.01 you know, the oldest animal fossils
00:21:21.16 are no older than about 5-600 million years old
00:21:24.17 and the oldest multicellular eukaryotes in general
00:21:27.14 are on the order of a billion years old,
00:21:29.16 those multicellular eukaryotes
00:21:31.20 evolved in environments that were already dominated
00:21:36.10 by teeming hoards of bacteria.
00:21:38.22 If we're going to understand the origin
00:21:40.24 of multicellular eukaryotes,
00:21:42.20 we need to understand how their progenitors
00:21:45.02 coped with a world
00:21:47.09 that was already populated and colonized by bacteria.
00:21:51.19 So, that's one important point that I want to make
00:21:53.21 about the bacterial context of animal origins.
00:21:56.18 The second point that I want to make
00:21:58.20 harkens back to something I talked about in Part I,
00:22:01.02 and that is that,
00:22:02.19 through the study of choanoflagellates,
00:22:04.09 we've been able to reconstruct
00:22:06.14 some important aspects of the biology of the first animals.
00:22:08.29 And so, you may remember that I mentioned
00:22:11.02 that we think the first animals
00:22:13.08 probably had collar cells
00:22:15.06 and, more importantly,
00:22:17.28 that those first animals were involved in bacterivory,
00:22:21.05 that is to say, they ate bacteria.
00:22:23.08 They make a living by eating bacteria.
00:22:25.15 And so, we now know think that interactions with bacteria
00:22:29.03 were an obligate part of the life history
00:22:31.17 of the first animals.
00:22:33.28 The final point that I want to make
00:22:38.28 is that, if you look at living animals,
00:22:41.19 what you can see is that development
00:22:43.25 in many of these diverse organisms
00:22:45.27 is regulated by bacterial signals.
00:22:48.19 The challenge in these cases.
00:22:50.18 now, obviously, there's been a lot of interest,
00:22:52.28 but the challenge has been that we're looking at
00:22:55.05 large, complex multicellular organisms
00:22:57.28 that are growing in association
00:23:00.04 with diverse and complex communities of microbiota,
00:23:04.19 and this has made it very difficult
00:23:06.22 to try to learn something about the mechanisms
00:23:08.29 underlying these important interactions.
00:23:11.25 And so, what we are now doing
00:23:15.02 is using this interaction
00:23:17.21 between the bacteria Algoriphagus and the choanoflagellate S. rosetta
00:23:21.01 as a simple bioassay
00:23:24.09 to discover bacterial signaling molecules
00:23:26.11 that we think will help us understand
00:23:28.20 the regulation of this developmental switch,
00:23:31.16 but will potentially have relevance
00:23:33.29 to other systems as well.
00:23:35.20 Now, I have to say that
00:23:38.01 this has been a very exciting but also challenging process,
00:23:41.15 in part because we had absolutely no idea
00:23:44.03 what the nature of the signaling molecules were.
00:23:48.20 After casting about in the dark for a little while,
00:23:51.14 we had a hint that came from looking
00:23:55.06 at what is unusual about the Bacteroidetes,
00:23:57.11 which are the bacteria. the large group of bacteria
00:23:59.22 of which Algoriphagus is a member.
00:24:03.25 So, Bacteroidetes
00:24:06.25 actually have, like other members of this group,
00:24:08.20 an outer membrane and an inner membrane.
00:24:11.28 They have components called LPS and peptidoglycan,
00:24:14.17 which are known inducers
00:24:18.09 of immune system components in animals,
00:24:22.04 but they also have an unusual group of lipids
00:24:24.18 called the sphingolipids
00:24:26.23 and the closely related sulfonolipids,
00:24:28.29 and I say that these are unusual,
00:24:30.26 but in fact they're quite common in eukaryotes.
00:24:32.29 It's in bacteria in which
00:24:36.15 you don't often see these types of lipids.
00:24:39.13 And so, for a variety of reasons,
00:24:42.02 we focused on this group of lipids as a potential source
00:24:45.12 of the signaling activity.
00:24:48.01 And, to do this,
00:24:50.14 we have established really one of the best collaborations
00:24:53.27 in my career.
00:24:55.12 It's been a fantastic experience.
00:24:57.01 We're been collaborating with Jon Clardy,
00:24:58.24 who's at Harvard Medical School
00:25:01.01 and has done fantastic work in many systems
00:25:04.17 in recovering bioactive molecules.
00:25:07.07 And so, what he and his group did
00:25:10.10 was they took Algoriphagus,
00:25:12.18 they extracted the sphingolipid fraction
00:25:16.10 from its outer membrane,
00:25:18.13 and then.
00:25:21.21 these sphingolipids are very difficult to deal with,
00:25:23.20 so at our first pass,
00:25:25.23 we've now started using different approaches,
00:25:28.01 but in the fist pass people from his lab
00:25:31.18 used a process called prep-TLC,
00:25:34.05 and this is thin layer chromatography,
00:25:36.19 to separate out all those sphingolipids,
00:25:39.28 and then they would scrape them off of this plate
00:25:43.01 and send them to my lab where we would test them
00:25:45.00 in the bioassay
00:25:46.27 and see whether those fractions were capable of
00:25:49.23 inducing rosettes or not.
00:25:51.10 And, based on that,
00:25:53.03 then we could take the fractions that were capable of inducing
00:25:55.23 and analyze them by mass spectroscopy.
00:25:58.25 And so, this was an iterative process.
00:26:01.09 We would send the bacterial samples,
00:26:03.09 they would fractionate them,
00:26:04.27 they would send us the fractions,
00:26:06.20 we would test them, we would send them the information.
00:26:08.02 it was back and forth,
00:26:11.01 and through a long series of analyses
00:26:13.23 we eventually were able to identify
00:26:15.18 the first bacterial molecule that was capable
00:26:18.12 of inducing rosette development
00:26:20.15 and that is this molecule.
00:26:22.17 We've name it RIF-1 for rosette-inducing factor 1,
00:26:25.00 and we now have a structure for it,
00:26:26.23 which is very exciting,
00:26:28.18 and we also know something about its chemistry.
00:26:31.28 So, RIF-1 is not a sphingolipid.
00:26:35.01 It is in fact in a different class of molecules
00:26:37.16 called the sulfonolipids,
00:26:39.14 and the sulfonolipids differ from sphingolipids
00:26:42.23 in that they have a sulfonic acid headgroup
00:26:45.13 at one end.
00:26:47.16 This class of molecules
00:26:50.09 has not previously been shown to be involved in signaling,
00:26:52.21 so this is exciting because it's the tip of the iceberg.
00:26:55.04 These types of molecules
00:26:57.09 might have wide-ranging roles
00:26:59.05 and we can just start to study them now.
00:27:01.06 What is known is that, in bacteria,
00:27:03.06 they seem to have a role in regulating gliding motility.
00:27:07.28 So, this molecule
00:27:11.01 we can fractionate and isolate from bacteria,
00:27:12.26 but it is actually functioning in a way
00:27:15.10 that is consistent with it having a real role in the environment.
00:27:18.09 And so, we took purified RIF-1
00:27:21.11 and tried to determine
00:27:24.15 the concentration of the molecule
00:27:26.13 that was required to induce rosette development,
00:27:28.19 and the exciting result is that in fact
00:27:32.03 RIF-1 is tremendously potent.
00:27:34.14 It is able to induce rosette development.
00:27:36.21 here again I'm showing you
00:27:39.10 the extent of rosette development along the y-axis
00:27:41.25 and the concentration of RIF-1 along the x-axis,
00:27:46.09 and what I hope you can see is that
00:27:48.00 we are getting maximal induction of rosette development
00:27:50.22 at concentrations that are in the femtomolar range,
00:27:54.16 and so this.
00:27:56.10 not only is it active at these levels,
00:27:58.12 but these are the levels in which we find RIF-1
00:28:01.19 in the conditioned media,
00:28:03.09 and so the activity of RIF-1 is entirely consistent
00:28:06.03 with it having an important function
00:28:08.09 at environmental concentrations.
00:28:10.04 So, that was very exciting.
00:28:11.27 So, it's a new class of signaling molecule,
00:28:13.22 it's active at environmentally-relevant concentrations,
00:28:16.26 that's all good, but now we get to the nitty-gritty.
00:28:20.01 I want to show you that, in fact,
00:28:22.08 the maximal induction that we're seeing
00:28:24.07 is only on the order of about 5% of cells
00:28:27.04 going into rosettes,
00:28:29.02 so it suggests that RIF-1 is important,
00:28:31.00 it's sufficient for rosette induction,
00:28:32.28 but it's not the whole story.
00:28:34.27 And so, what we've now done
00:28:37.20 is go back to our simple bioassay
00:28:39.20 to see, now, if we can more rapidly discover
00:28:42.00 other potential bacterial signaling molecules,
00:28:44.15 and in fact we have.
00:28:47.15 So, again this is through our collaboration
00:28:49.16 with the Clardy lab.
00:28:51.01 We've gone back now
00:28:53.09 and analyzed more broadly,
00:28:55.07 not just the sphingolipids,
00:28:57.11 but the entire lipid fraction,
00:29:01.02 and we've been able, through this process,
00:29:02.27 to find other bioactive signaling molecules.
00:29:05.19 Here in this part of the eluate we find RIF-1,
00:29:10.24 but also many, many other sulfonolipids,
00:29:13.15 all of which are inactive,
00:29:15.08 and that makes an important point.
00:29:17.02 RIF-1 isn't active because it's a sulfonolipid.
00:29:19.22 RIF-1 is a special sulfonolipid.
00:29:22.09 Most other sulfonolipids can't induce (rosette development).
00:29:25.25 So, there's a tight structure-activity relationship
00:29:28.00 between RIF-1 and its ability to regulate rosette development.
00:29:31.11 But, what we've also found is that there are
00:29:33.28 other classes of sulfonolipids
00:29:36.04 that are structurally similar to RIF-1
00:29:38.00 that are also capable of inducing.
00:29:40.07 There's an entirely different class of lipids
00:29:42.16 called the LPEs, or the lysophosphatidylethanolamines.
00:29:45.22 These are able to synergize with RIF-1
00:29:48.18 and the other sulfonolipids
00:29:50.17 to promote rosette development.
00:29:52.18 And finally, there's another type of lipids
00:29:54.29 that's an antagonist of the RIFs,
00:29:58.07 and if you incubate it with the choanoflagellate
00:30:02.03 and then add RIF-1, -2, or -3,
00:30:04.23 you find that you block rosette development.
00:30:07.05 So, there are many different, diverse bioactive lipids
00:30:10.08 available in Algoriphagus,
00:30:12.15 and we can mix them together
00:30:14.17 and reconstitute the full induction activity
00:30:17.00 that you normally get from live Algoriphagus bacteria.
00:30:21.02 So, there are diverse bioactive molecules
00:30:23.13 that we have discovered already using our bioassay,
00:30:26.04 just from Algoriphagus,
00:30:27.26 but in addition
00:30:30.13 we've also surveyed lots of other diverse bacteria,
00:30:33.13 and biologists will do this and what I'm going to tell you is,
00:30:36.01 I'm showing you a phylogenetic tree of diverse bacteria
00:30:39.05 and you don't need to worry about the fact that
00:30:41.20 you can't read which bacteria I'm showing you.
00:30:43.24 The point I want to make is that there's a lot of diverse bacteria
00:30:45.27 we've tested now,
00:30:47.26 and shown in these squares
00:30:50.04 I'm indicating whether they induce rosettes or not.
00:30:53.04 Those shown with black do induce.
00:30:54.29 Those shown with white don't,
00:30:57.25 and those in grey induce at a low level.
00:31:00.03 And so, we're find that across the tree of bacterial diversity,
00:31:02.28 we're finding many different bacteria
00:31:05.28 that induce and some of them seem to use
00:31:08.12 different types of bioactive molecules
00:31:10.22 to induce rosette development.
00:31:13.00 Finally, we'd like to know
00:31:15.16 whether this bioassay might help us
00:31:18.07 find something that's of biomedical relevance,
00:31:20.02 and so we've actually surveyed
00:31:22.18 the bacteria of the vertebrate gut system,
00:31:24.23 looking at different parts of the intestinal tract,
00:31:27.13 and testing whether they're capable of
00:31:29.28 inducing rosette development,
00:31:31.20 and we find, in fact, that they can.
00:31:33.17 So, bacteria from the stomach and the small intestine
00:31:35.29 do not induce rosette development,
00:31:38.08 but bacteria from the cecum and the colon do,
00:31:41.12 and if we follow the strategy that we followed previously
00:31:44.04 in the discovery of Algoriphagus,
00:31:46.14 we can do it here
00:31:48.27 and culture these bacteria and see if we can identify
00:31:51.05 the species that induce rosette development,
00:31:53.12 and so we've done that,
00:31:55.08 and we've now discovered the specific bacteria
00:31:57.10 from the gut system
00:31:59.06 that are capable of inducing rosette development
00:32:00.27 and we're focusing on isolating the bioactive molecules
00:32:03.27 from these organisms as well.
00:32:06.25 Okay, so let me just recap what I've told you.
00:32:10.16 I've told you that rosette development is regulated.
00:32:13.21 is the process of incomplete cytokinesis,
00:32:16.24 and cells in rosettes
00:32:19.04 are held together through a combination
00:32:21.24 of intercellular bridges and ECM.
00:32:24.09 Moreover, what we're discovered,
00:32:26.00 and it was quite unexpected,
00:32:28.04 we found that the developmental switch
00:32:30.07 that controls whether a single cell
00:32:32.10 is going to form a rosette, chain colonies,
00:32:35.26 or another single cell,
00:32:37.20 that's regulated not solely by genetics
00:32:40.18 of the choanoflagellate,
00:32:42.09 but actually, importantly,
00:32:44.18 by signals that are released
00:32:47.02 by environmental bacteria.
00:32:50.24 So, over Part I and Part II,
00:32:53.27 I've been telling you about these
00:32:56.18 really interesting organisms, the choanoflagellates,
00:32:59.16 that were discovered in the 1800s,
00:33:01.29 that we've now brought into the molecular
00:33:03.23 and genomic era.
00:33:05.15 And, through the study of choanoflagellates,
00:33:06.28 we're finding that we're able to reconstruct
00:33:09.02 the biology of the first animals
00:33:11.28 in increasingly resolution,
00:33:13.29 and one of the most exciting things
00:33:16.11 I think we've found through these types of studies
00:33:18.20 is that many of the genes that are essential
00:33:20.27 for regulating cell-cell interactions in animals
00:33:23.21 and regulating the process of development
00:33:26.08 actually evolved before the origin of animals
00:33:29.00 and are conserved in the genomes
00:33:30.28 of living choanoflagellates.
00:33:32.13 So, that's been great,
00:33:34.05 to learn that choanoflagellates provide this window
00:33:36.00 into animal origins.
00:33:38.27 In addition, I told you about
00:33:42.00 a transition to multicellularity
00:33:44.02 that actually happens in the life history of
00:33:46.04 a living choanoflagellate, the choanoflagellate S. rosetta.
00:33:49.06 And, the very exciting discovery that we've made
00:33:52.03 by studying this process in mechanistic detail
00:33:56.02 is that the developmental switch
00:33:59.00 to form multicelled rosette colonies
00:34:01.08 is actually regulated by environmental bacteria.
00:34:04.23 So, it's been very exciting,
00:34:07.04 we've been collaborating with Jon Clardy
00:34:09.11 to uncover bioactive molecules,
00:34:11.06 and this now bring us to the point in which
00:34:13.24 we can start thinking
00:34:15.10 about the choanoflagellate side of the story.
00:34:16.03 How is it that choanoflagellates
00:34:18.09 are actually sensing these bacterial signaling molecules?
00:34:21.12 Moreover, we're curious about whether this interaction
00:34:24.25 is something special to the choanoflagellate lineage
00:34:28.01 or whether it actually is informative
00:34:30.06 about mechanisms underlying animal origins.
00:34:32.21 And so, to that end,
00:34:33.29 in the long run we'd like to know
00:34:36.06 whether the mechanisms regulating this signaling interaction
00:34:40.15 between bacteria and choanoflagellates
00:34:43.03 might be conserved in the interactions
00:34:45.06 between animals and their commensal bacteria.
00:34:49.00 So, it's been a real pleasure
00:34:51.10 telling you about this work
00:34:53.07 and I want to take this opportunity to thank
00:34:55.12 a number of people, many of which,
00:34:58.11 you know, I can't list all of them,
00:35:00.00 but I really want to thank everybody in my lab
00:35:01.26 and I've highlighted here, in yellow,
00:35:03.26 the people who have actually contributed to the work
00:35:05.21 that I discussed here.
00:35:07.17 One current member, Arielle Woznika,
00:35:09.11 and many different alumni
00:35:11.29 who have been essential to this project.
00:35:14.05 Moreover, I have to express my gratitude
00:35:17.10 to Jon Clardy,
00:35:19.19 who's been a really fantastic collaborator
00:35:21.05 and I've learned so much from him
00:35:23.02 and it's been a wonderful experience,
00:35:24.29 and then of course our funding agencies.
00:35:26.29 If you find yourself fascinated by choanoflagellates
00:35:28.29 and you want to learn more,
00:35:32.17 we invite many, many people, we want to grow this community,
00:35:35.02 and you can learn more about choanoflagellates
00:35:37.13 in these various locations,
00:35:39.06 and importantly we have a choanoflagellate workshop
00:35:41.10 every two years, so please come and join us.
00:35:44.17 And it's been a pleasure talking to you.

  • />Part 1: The origin of animal multicellularity


We thank all contributors to Mountains, Climate and Biodiversity for their invaluable work and many colleagues who have given us motivation and ideas to work on this theme. Alexander Rohrmann, Veronica Torres Acosta and Matthias Bernet provided valuable feedback and corrections on Table 1. Christine Bacon provided examples for Figure 1. We thank Peter Linder, Luis Valente and Jan Schnitzler for comments. AA is funded by the Swedish Research Council (B0569601), the Swedish Foundation for Strategic Research and the Knut and Alice Wallenberg Foundation through a Wallenberg Academy Fellowship.


Invited Presentations and Panels

Invasive Species are Neither, and Why it Matters. Maricopa Audubon Society, November 3, 2015 also at Boyce Thompson Southwest Arboretum, December 14, 2015.

Webinar Presentation: Thinking About Invasive Species. University of New Mexico University of California, Berkeley University of Alaska, Fairbanks. The Human Dimension of Natural History Biology 402-502, 2 September 2014.

Webinar Presentation: How Did Weeds Became Invaders? University of Nebraska, Lincoln, North American Invasive Plant Short Course. 6 February 2014.

Presentation: Hearts, Minds and Invasive Species. Beyond Pesticides 31st National Pesticide Forum. Sustainable Families, Farms and Food: Resilient Communities Through Organic Practices. University of New Mexico, Albuquerque NM, 6 April 2013

Workshop Panelist: Organic Land Management and Cutting Edge Alternatives. Beyond Pesticides 31st National Pesticide Forum. Sustainable Families, Farms and Food: Resilient Communities Through Organic Practices. University of New Mexico, Albuquerque NM, 6 April 2013

Presentation: Biological invasions arent invasionsAnd why that matters to science and society. University of Nevada, Reno. Ecology, Evolution and Conservation Biology Colloquium Series.
Reno, NV 21 February 2013.

Moderator: Film discussion panel, Watershed: Exploring a new water ethic for the New West. Arizona State University, Global Institute of Sustainability and the Arizona Riparian Council, Tempe AZ,
27 September 2012.

Panelist: Science Magazine, ScienceNOW Live Chat: Invasive SpeciesThreats or Just Misunderstood? 26 July 2012.

Panelist: Adopting and Adapting a Father: Charles Eltons Meaning to Invasion Biology. University of Sydney Environmental Humanities Group Conference: Rethinking Invasion Ecologies: Natures, Cultures, and Societies in the Age of the Anthropocene. Sydney, NSW, Australia. June 2012.

Plenary panelist: The Surprising History of Biological Invasions: Certain uncertainties, philosophical failures and metaphorical mistakes. American Fisheries Society, Idaho Chapter Annual Conference, Coeur dAlene Idaho, March 2012.

Natives and Aliens: Not Even a Good Idea. 30th Public Interest Environmental Law Conference, Eugene, Oregon, March 2012.

Presentation: Pragmatic Environmentalism. Arizona Association of Environmental Professionals. Scottsdale, AZ, 24 May 2011.

Panelist: Second Annual 21st Century Challenges for Conservation Biology. Central Arizona Chapter, Society for Conservation Biology. Tempe, AZ April 2011.

Presentation: Where Natives Come From. Northern Arizona University, Forestry Graduate Students Association Seminar Series. Flagstaff, AZ, 27 October 2010.

Presentation: Phytogeography: 175 Years of Negative Nativeness. From Linnaeus to the Encyclopedia of Life: Tracking Diversity in the Natural World. MBL-ASU History of Biology Summer Seminar. Woods Hole, MA May 2010.

Panelist: 21st Century Challenges for Conservation Biology. Society for Conservation Biology, Central Arizona Chapter. Tempe, AZ April 2010.

Presentation: No Place Like Home: The Competing Social Metaphors of Ecological Science. University of Tasmania: School of Government and Environment Centre special joint symposium. Hobart, Australia, July 2009.

Keynote Presentation The Dagger and the Asterisk. Symposium: Fifty years of Invasion Ecology  the Legacy of Charles Elton. Stellenbosch, South Africa. November 2008.

Presentation: Is it Really an Invasion? McDowell Sonoran Conservancy, Scottsdale AZ, June 2007.

Presentation: Monsters from Another World: Arizona Aliens. Desert Botanical Garden, Phoenix AZ, July 2002. (With J.C. Stromberg)

Meetings, Workshops, Conferences, and Symposia: Contributed Presentations and Posters

"Unwanted! Anthropomorphizing and Personifying Introduced Species as Criminals" with Rachel Hall, Louisiana State University. American Society for Environmental History Annual Meeting, San Francisco, CA, March 2014.

Changing conservation goals and strategies: A succession of failed metaphors? (Symposium Paper) Ecological Society of America Annual Meeting, Portland OR August 2012.

Prescriptive Political Biogeography: National Identity and Invading Alien Species. Nature and Nation Network Second Workshop: The State of Nature. Bucharest, Romania, December 2011.

The Last Nineteenth Century Naturalist: Charles Eltons Visits to the American Tropics. International Society for the History, Philosophy, and Social Studies of Science Biennial Meeting, Salt Lake City, Utah July 2011.

Found then Drowned: Three Noteworthy Collections from Tempe Town Lake. 8th Arizona Botany Meeting. E. Makings, L. Butler, Matthew Chew & Juliet Stromberg presented by Elizabeth Makings. Phoenix AZ, February 2011.

A Modified Kind of Man and a Modified Kind of Nature: Charles Eltons Vision of Millennial Conservation. History of Science Society Annual Meeting, Montreal Quebec November 2010.

Biotic Nativeness: A historical look at a simply negative idea. School of Life Sciences Seminar Series, Arizona State University, Tempe, AZ. October 2009.

Anekeitaxonomy: Botany, Place and Belonging. World Congress of Environmental History, Copenhagen, Denmark, August 2009.

Tamarisk: Five Framings. International Society for the History, Philosophy, and Social Studies of Science Biennial Meeting, Brisbane, Australia, July 2009.

Tamarisk: From Good and Pretty to Bad and Ugly (invited conference dinner presentation) Arizona Riparian Council Annual Meeting, Camp Verde, Arizona. April 2009.

The Role of Scientists in Tamarisk and River Management: Perpetuation of a Mythology (poster, with J. Stromberg, P. Nagler and E. Glenn). Tamarisk and Russian Olive Research Conference, Reno, NV February 2009 Ecological Society of America Annual Meeting, Albuquerque, NM, August 2009.

Ecology and the De-Natured World. (Symposium Paper). Ecological Society of America Annual Meeting, Milwaukee, WI August 2008.

Invasion Biologys Forgotten Forerunners (poster). Ecological Society of America / Society for Ecological Restoration Joint Annual Meeting, San Jose, CA August 2007. also at Symposium: Fifty years of Invasion Ecology  the Legacy of Charles Elton. Stellenbosch, South Africa. November 2008.

H.C. Watson and the Civil Claims of British Plants, International Society for the History, Philosophy, and Social Studies of Science Biennial Meeting, Exeter, UK, July 2007.

5000 Years of Tamarisk in 5 Minutes. Special Session Panel, Ecological Society of America Annual Meeting, Memphis, TN August 2006.

Anekeitaxonomy: Biologizing Belonging. Southwest Colloquium in the History and Philosophy of the Life Sciences, Davis, CA March 2006.

The Anti(?)-Aesthetics of Invasion Biology. International Society for the History, Philosophy, and Social Studies of Science Biennial Meeting, Guelph, Ontario, Canada July 2005.

Nativeness Considered (with Andrew L. Hamilton, UC San Diego) Southwest Colloquium in the History and Philosophy of the Life Sciences, Tempe AZ March 2005

Endangered with Criticized Habitat: A Bird in the Wrong Bush. American Society for Environmental History Annual Meeting, Victoria, BC, Canada April 2004.

The Costs of Caricature: Is Invasion Biology Exploiting Science Illiteracy? (Poster) American Institute of Biological Sciences (AIBS) Annual Meeting, Washington, DC, March 2004.
also at Gordon Research Conference on Science and Technology Policy, Big Sky, MT August 2004.

Biological Invasions as Ecological (and other) Models (with M.D. Laubichler) Southwest Colloquium in the History and Philosophy of the Life Sciences, Austin, TX March 2004.

The Stone the Builders Rejected: Marston Bates, Charles Elton and the Foundations of Invasion Ecology. International Society for the History, Philosophy, and Social Studies of Science Biennial Meeting, Vienna, Austria, July 2003.

The Tangled Tale of Tamarix: A Study in Status, Southwest Colloquium in the History and Philosophy of the Life Sciences, Tempe, AZ February 2003.

The Invasion of the Second Greatest Threat, History of Science Society Annual Meeting, Milwaukee, WI, November 2002.

Watch the video: Introduction To Animal Diversity. Iken Edu (July 2022).


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