Search bioRxiv⌕ Search

Biology subjects

Chase, J.

Publications and source records attributed to Chase, J..

At least 19 recordsLinked to original sources

Sequencing by Expansion (SBX) -- a novel, high-throughput single-molecule sequencing technology

Remarkable advances in high-throughput sequencing have enabled major biological discoveries and clinical applications, but achieving wider distribution and use depends critically on further improvements in scale and cost reduction. Nanopore sequencing has long held the promise for such progress, but has had limited market penetration. This is because efficient and accurate nanopore sequencing of nucleic acids has been challenged by fundamental signal-to-noise limitations resulting from the poor spatial resolution and molecular distinction of nucleobases. Here, we describe Sequencing by Expansion (SBX), a single-molecule sequencing technology that overcomes these limitations by using a biochemical conversion process to encode the sequence of a target nucleic acid molecule into an Xpandomer, a highly measurable surrogate polymer. Expanding over 50 times longer than the parent DNA templates, Xpandomers are engineered with high signal-to-noise reporter codes to enable facile, high-accuracy nanopore sequencing. We demonstrate the performance of SBX and present the specialized molecular structures, chemistries, enzymes and methods that enable it. The innovative molecular and systems engineering in SBX create a transformative technology to address the needs of existing and emerging sequencing applications.

genomics↗

Genetic changes linked to two different syndromic forms of autism enhance reinforcement learning in adolescent male but not female mice

Autism Spectrum Disorder (ASD) is characterized by restricted and repetitive behaviors and social differences, both of which may manifest, in part, from underlying differences in corticostriatal circuits and reinforcement learning. Here, we investigated reinforcement learning in developing mice with mutations in either Tsc2 or Shank3, both high-confidence ASD risk genes associated with major syndromic forms of ASD. Using an odor-based two-alternative forced choice (2AFC) task, we tested early adolescent mice of both sexes and found male Tsc2 and Shank3B heterozygote (Het) mice showed enhanced learning performance compared to their wild type (WT) siblings. No gain of function was observed in females. Using a novel reinforcement learning (RL) based computational model to infer learning rate as well as policy-level task engagement and disengagement, we found that the gain of function in males was driven by an enhanced positive learning rate in both Tsc2 and Shank3B Het mice. The gain of function in Het males was absent when mice were trained with a probabilistic reward schedule. These findings in two ASD mouse models reveal a convergent learning phenotype that shows similar sensitivity to sex and environmental uncertainty. These data can inform our understanding of both strengths and challenges associated with autism, while providing further evidence that sex and experience of uncertainty modulate autism-related phenotypes. Significance StatementReinforcement learning is a foundational form of learning that is widely used in behavioral interventions for autism. Here, we measured reinforcement learning in developing mice carrying genetic mutations linked to two different syndromic forms of autism. We found that males showed strengths in reinforcement learning compared to their wild type siblings, while females showed no differences. This gain of function in males was no longer observed when uncertainty was introduced into the reward schedule for correct choices. These findings support a model in which diverse genetic changes interact with sex to generate common phenotypes underlying autism. Our data further support the idea that autism risk genes may produce strengths as well as challenges in behavioral function.

neuroscience↗

Degradation of fish food webs in the anthropocene

The Anthropocene is marked by profound changes in biodiversity and the ecosystems in which species live 1-4. A primary signature of this change is the often rapid change in species composition through time (i.e., species turnover) rather than changes in the numbers of species per se 5-7. Less well known, however, is which types of species are winning and which are losing as ecosystems change through time 8,9, as well as whether and how these changes influence higher-level processes in the food webs in which species are embedded 10,11. Here, we combine a compilation of long-term observations of [~]15,000 freshwater and marine fish communities surveyed for 1949-2019 years, together with information about their diets and trophic status in order to evaluate how the food webs in which these fish communities are embedded are changing through time. We found widespread alteration to fish food web topology and functioning. This includes an increase in connectance and generalism in food webs, which has led to greater predation pressure, as indicated by higher diet overlap and increased prey vulnerability. We also identified a decrease in modularity, which has reduced the compartmentalization within local networks. These changes extend across the trophic structure of food webs, causing a cascading shift in the proportion of species across trophic levels. Our study highlights the complex responses of biodiversity change of fish food webs in the Anthropocene, which can ultimately influence the functions of these ecosystems and human well-being 12,13.

ecology↗

Nutrient addition in grasslands worldwide reveals proportional plant diversity decline across spatial scales but little change in beta diversity

Nutrient enrichment typically causes local plant diversity declines. A common but untested expectation is that nutrient enrichment also reduces variation in nutrient conditions among localities and selects for a smaller pool of species, causing greater diversity declines at larger than local scales and thus biotic homogenization. Here we apply a framework that links changes in species richness across scales to changes in the numbers of spatially restricted and widespread species for a standardized nutrient addition experiment across 72 grasslands on six continents. Overall, we find proportionally similar species loss at local and larger scales, suggesting similar declines of spatially restricted and widespread species, and no biotic homogenization after 4 years and up to 14 years of treatment. These patterns of diversity changes are generally consistent across species groups. Thus, nutrient enrichment poses threats to plant diversity, including for widespread species that are often critical for ecosystem functions.

ecology↗

Synthesis reveals biotic homogenisation and differentiation are both common

It is commonly thought that the biodiversity crisis includes widespread decreases in the uniqueness of different sites in a landscape (biotic homogenization). Using a typology relating homogenization and differentiation to local and regional diversity changes, we synthesize patterns across 283 metacommunities surveyed for 10-91 years, and 54 species checklists (13-500+ years). On average, there is a 0.2% increase in species shared among communities/year (i.e., weak homogenization), but across data sets, differentiation frequently occurs, with no statistically significant change being most common. Local (not regional) diversity frequently underlies composition change, and homogenization is strongly associated with checklist data that have longer durations and large spatial scales. Conservation and management can benefit from the multiscale perspective used here as it disentangles the implications of both the differentiation and homogenization currently unfolding. One-Sentence SummaryBiotic homogenization is most prevalent at large temporal and spatial scales.

ecology↗

Interacting abiotic and biotic drivers shape woody invasions across Hawaiian forests

The same features that generate native biodiversity patterns across and within oceanic islands over evolutionary time - climate, soil age, topography, and biotic interactions - also influence their vulnerability to biological invasions. Here, we identify the factors that shape the richness and abundance of alien woody species in forest communities across the Hawaiian archipelago, and assess the relative importance of abiotic, biotic, and anthropogenic factors and their interactions on the establishment and dominance of woody alien species. Using a database of 460 forest plots distributed across the six major Hawaiian islands, we examine variation in i) relative alien species richness and abundance as a function of abiotic and anthropogenic factors (e.g., temperature, aridity, soil age, and the human influence index) and ii) establishment and dominance of alien species as a function of the same abiotic and anthropogenic factors, as well as phylogenetic and trait distinctiveness. We found that relative alien species richness and abundance were higher in areas where temperature was high and aridity low. Gradients in temperature, aridity, soil age, and human influence also modulated the importance of biotic factors in determining establishment of alien species. In contrast, whether these alien species could become locally dominant was not strongly influenced by abiotic or biotic factors, or their interactions. Our results suggest that environmental filtering mediates the strength of biotic filtering in determining where woody aliens are able to colonize and establish on these oceanic islands, but not whether they become dominant. The context dependence of multi-species invasions highlights the complexity of developing management strategies to mitigate the biodiversity and ecosystem impacts of biological invasions.

ecology↗

Local biodiversity change reflects interactions among changing abundance, evenness and richness

Biodiversity metrics often integrate data on the presence and abundance of multiple species. Yet understanding covariation of changes to the numbers of individuals, the evenness of species relative abundances, and the total number of species remains limited. Using individual-based rarefaction curves, we introduce a conceptual framework to understand how expected positive relationships among changes in abundance, evenness and richness arise, and how they can break down. We then examined interdependencies between changes in abundance, evenness and richness in more than 1100 assemblages sampled either through time or across space. As predicted, richness changes were greatest when abundance and evenness changed in the same direction, and countervailing changes in abundance and evenness acted to constrain the magnitude of changes in species richness. Site-to-site changes in abundance, evenness, and richness were often decoupled, and pairwise relationships between changes in these components across assemblages were weak. In contrast, changes in species richness and relative abundance were strongly correlated for assemblages varying through time. Temporal changes in local biodiversity showed greater inertia and stronger relationships between the component changes when compared to site-to-site variation. Local variation in assemblage diversity was rarely due to a passive sample from a more or less static species abundance distribution. Instead, changing species relative abundances often dominated local variation in diversity. Moreover, how changing relative abundances combined with changes to total abundance frequently determined the magnitude of richness changes. Embracing the interdependencies between changing abundance, evenness and richness can provide new information for better understanding biodiversity change in the Anthropocene.

ecology↗

Differential impact of a dyskeratosis congenita mutation in TPP1 on mouse hematopoiesis and germline

Telomerase extends chromosome ends in somatic and germline stem cells to ensure continued proliferation. Mutations in genes critical for telomerase function result in telomeropathies such as dyskeratosis congenita (DC), frequently resulting in spontaneous bone marrow failure. While knockout of telomerase in mice has been instrumental in highlighting the importance of telomere length maintenance at an organismal level, it may not be representative of human telomeropathy mutations in vivo. A DC mutation in the shelterin protein TPP1 (K170{Delta}) that compromises telomerase recruitment to telomeres but leaves other functions of TPP1 and the integrity of the telomerase holoenzyme intact is a physiologically relevant tool to evaluate telomerase-dependent telomere length maintenance in mice. We used CRISPR-Cas9 to generate a mutant mouse knocked in for the equivalent of the TPP1 K170{Delta} mutation (TPP1 K82{Delta}) and investigated both its bone marrow and germline compartments in unprecedented detail. TPP1 K82{Delta} caused progressive telomere erosion with increasing generation number but did not induce steady-state hematopoietic defects. Strikingly, K82{Delta} caused mouse infertility, consistent with gross morphological defects in the testis and sperm, the appearance of either empty or severely disorganized seminiferous tubules, and a decrease in both spermatogonia and spermatocytes. It is intriguing that both TPP1 K82{Delta} mice and previously characterized telomerase knockout mice show no spontaneous bone marrow failure but rather succumb to a robust infertility phenotype at steady state. We speculate that telomere length maintenance contributes differently to the evolutionary fitness of humans and mice. Telomere length maintenance in the human bone marrow can ensure progression to reproductive age, while that in the mouse germline can help meet the elevated demand for sperm to produce multiple offspring.

cell biology↗

Disentangling metacommunity processes using multiple metrics in space and time

Metacommunity ecology has focused on using observational and analytical approaches to disentangle the role of critical assembly processes, such as dispersal limitation and environmental filtering. Many methods have been proposed for this purpose, most notably multivariate analyses of species abundance and its association with variation in spatial and environmental conditions. These approaches tend to focus on few emergent properties of metacommunities and have largely ignored temporal community dynamics. By doing so, these are limited in their ability to differentiate metacommunity dynamics. Here, we develop a Virtual ecologist approach to evaluate critical metacommunity assembly processes based on a number of summary statistics of community structure across space and time. Specifically, we first simulate metacommunities emphasizing three main processes that underlie metacommunity dynamics (density-independent responses to abiotic conditions, density-dependent biotic interactions, and dispersal). We then calculate a number of commonly used summary statistics of community structure in space and time, and use random forests to evaluate their utility for understanding the strength of these three processes. We found that: (i) time series are necessary to disentangle metacommunity processes, (ii) each of the three studied processes is distinguished with different descriptors, (iii) each summary statistic is differently sensitive to temporal and spatial sampling effort. Some of the most useful statistics include the coefficient of variation of abundances through time and metrics that incorporate variation in the relative abundances (evenness) of species. Surprisingly, we found that when we only used a single snapshot of community variation in space, the most commonly used approaches based on variation partitioning were largely uninformative regarding assembly processes, particularly, variation in dispersal. We conclude that a combination of methods and summary statistics will be necessary to understand the processes that underlie metacommunity assembly through space and time.

ecology↗

Emergence of stable motifs in consumer-resource communities

Understanding how and why complex communities can be stable has preoccupied ecologists for over a century. Data show that real communities tend to exhibit characteristic motifs and topologies. Despite a large body of theory investigating both ecological (niche partitioning) and evolutionary (speciation and extinction) mechanisms, a general explanation for why particular motifs are more common than others remains elusive. Here we develop a mechanistic framework that investigates the set of possible motifs that can emerge under minimal conditions of a nutrient-limited system with no external inputs, and no spatial heterogeneity. Focusing on consumer-resource communities structured by competition and predation, we find that the emergent motifs under these minimal conditions are vertical trophic chains that maximize energy transfer and biomass production. Not only are such motifs stable to perturbations of species abundances, but they are also robust to species additions and removals. Our findings provide a mechanistic explanation for why tri-trophic chains are overrepresented in real food webs. They suggest that, because they maximize energy transfer, and can emerge and persist under minimal conditions, vertical trophic chains may constitute the fundamental architecture of consumer-resource communities.

ecology↗

The Internal Structure of Metacommunities

Current analyses of metacommunity data largely focus on global attributes across the entire metacommunity, such as mean alpha, beta, and gamma diversity, as well as the partitioning of compositional variation into single estimates of contributions of space and environmental effects and, more recently, possible contributions of species interactions. However, this view neglects the fact that different species and sites in the landscape can vary widely in how they contribute to these metacommunity-wide attributes. We argue for a new conceptual framework with matched analytics with the goals of studying the complex and interactive relations between process and pattern in metacommunities that is focused on the variation among species and among sites which we call the internal structure of the metacommunity. To demonstrate how the internal structure could be studied, we create synthetic data using a process-based colonization-extinction metacommunity model. We then use Joint Species Distribution Models to estimate how the contributions of space, environment and biotic interactions driving metacommunity assembly differ among species and sites. We find that this approach to the internal structure of metacommunities provides useful information about the distinct ways that different species and different sites contribute to metacommunity structure. Although it has limitations, our work points at a more general approach to understand how other possible complexities might affect internal structure and might thus be incorporated into a more cohesive metacommunity theory.

ecology↗

Island species-area relationships in the Andaman islands emerge because rarer species are disproportionately favored on larger islands.

The Island Species-Area relationship (ISAR) describes how the number of species increases with increasing size of an island (or island-like habitat), and is of fundamental importance in island biogeography and conservation. Here, we use a framework based on individual-based rarefactions to infer whether ISARs result from random sampling, or whether some process are acting beyond sampling (e.g., disproportionate effects and/or habitat heterogeneity). Using data on total and relative abundances of four taxa (birds, butterflies, amphibians and reptiles) across the Andamans and Nicobar archipelago, we examine how different metrics of biodiversity (total species richness, rarefied species richness, and abundance-weighted effective numbers of species emphasizing common species) vary with island area. Total species richness increased for all taxa, as did rarefied species richness for a given sampling effort. This indicates that the ISAR did not result because of random sampling, but that instead, species were disproportionately favored on larger islands. This disproportionate effect was primarily due to changes in the abundance of rarer species, because there was no effect on the abundance-weighted diversity measure for all taxa except butterflies. Furthermore, for the two taxa for which we had plot-level data (lizards and frogs), within-island {beta} -diversity did not increase with island size, suggesting that heterogeneity effects were unlikely to be driving these ISARS. Overall, our results indicate that the ISAR of these taxa is most likely because rarer species are more likely to survive and persist beyond that which would have been expected by random sampling alone, and emphasizes the role of these larger islands in the preservation and conservation of species.

ecology↗

A process-based framework for metacommunity ecology

The metacommunity concept has the potential to integrate local and regional dynamics within a general community ecology framework. To this end, the concept must move beyond the discrete archetypes that have largely defined it (e.g. neutral vs. species sorting) and better incorporate local scale species interactions and coexistence mechanisms. Here, we present a fundamental reconception of the framework that explicitly links local coexistence theory to the spatial processes inherent to metacommunity theory, allowing for a continuous range of competitive community dynamics. These dynamics emerge from the three underlying processes that shape ecological communities: 1) density-independent responses to abiotic conditions, 2) density-dependent biotic interactions, and 3) dispersal. Stochasticity is incorporated in the demographic realization of each of these processes. We formalize this framework using a simulation model that explores a wide range of competitive metacommunity dynamics by varying the strength of the underlying processes. Using this model and framework, we show how existing theories, including the traditional metacommunity archetypes, are linked by this common set of processes. We then use the model to generate new hypotheses about how the three processes combine to interactively shape diversity, functioning, and stability within metacommunities. Statement of authorshipThis project was conceived at the sTURN working group, of which all authors are members. PLT developed the framework and model with input from all authors. PLT wrote the model code. PLT and LMG performed the simulations. PLT produced the figures and wrote the first draft with input from LMG and JMC. All authors provided feedback and edits on several versions of the manuscript. Data accessibilityAll code for running the simulation model and producing the figures is archived on Zenodo - https://doi.org/10.5281/zenodo.3833035.

ecology↗

A cross-scale assessment of productivity-diversity relationships

AimBiodiversity and ecosystem productivity vary across the globe and considerable effort has been made to describe their relationships. Biodiversity-ecosystem functioning research has traditionally focused on how experimentally controlled species richness affects net primary productivity (S[->]NPP) at small spatial grains. In contrast, the influence of productivity on richness (NPP[->]S) has been explored at many grains in naturally assembled communities. Mismatches in spatial scale between approaches have fostered debate about the strength and direction of biodiversity-productivity relationships. Here we examine the direction and strength of productivitys influence on diversity (NPP[->]S) and of diversitys influence on productivity (S[->]NPP), and how this varies across spatial grains. Locationcontiguous USA Time period1999 - 2015 Major taxa studiedwoody species (angiosperms and gymnosperms) MethodsUsing data from North American forests at grains from local (672 m2) to coarse spatial units (median area = 35,677 km2), we assess relationships between diversity and productivity using structural equation and random forest models, while accounting for variation in climate, environmental heterogeneity, management, and forest age. ResultsWe show that relationships between S and NPP strengthen with spatial grain. Within each grain, S[->]NPP and NPP[->]S have similar magnitudes, meaning that processes underlying S[->]NPP and NPP[->]S either operate simultaneously, or that one of them is real and the other is an artifact. At all spatial grains, S was one of the weakest predictors of forest productivity, which was largely driven by biomass, temperature, and forest management and age. Main conclusionsWe conclude that spatial grain mediates relationships between biodiversity and productivity in real-world ecosystems and that results supporting predictions from each approach (NPP[->]S and S[->]NPP) serve as an impetus for future studies testing underlying mechanisms. Productivity-diversity relationships emerge at multiple spatial grains, which should widen the focus of national and global policy and research to larger spatial grains.

ecology↗

Biodiversity trends are stronger in marine than terrestrial assemblages

Human activities have fundamentally altered biodiversity. Extinction rates are elevated and model projections suggest drastic biodiversity declines. Yet, observed temporal trends in recent decades are highly variable, despite consistent change in species composition. Here, we uncover clear spatial patterns within this variation. We estimated trends in the richness and composition of assemblages in over 50,000 time-series, to provide the most comprehensive assessment of temporal change in biodiversity across the planet to date. The strongest, most consistent pattern shows compositional change dominated by species turnover, with marine taxa experiencing up to fourfold the variation in rates of change of terrestrial taxa. Richness change ranged from no change to richness gains or losses of ~10% per year, with tropical marine biomes experiencing the most extreme changes. Earth is undergoing a process of spatial reorganisation of species and, while few areas are unaffected, biodiversity change is consistently strongest in the oceans.

ecology↗

Integrating global patterns and drivers of tree diversity across a continuum of spatial grains

What drives biodiversity and where are the most biodiverse places on Earth? The answer critically depends on spatial scale (grain), and is obscured by lack of data and mismatches in their grain. We resolve this with cross-scale models integrating global data on tree species richness (S) from 1338 local forest surveys and 287 regional checklists, enabling estimation of drivers and patterns of biodiversity at any desired grain. We uncover grain-dependent effects of both environment and biogeographic regions on S, with a positive regional effect of Southeast Asia at coarse grain that disappears at fine grains. We show that, globally, biodiversity cannot be attributed to purely environmental or regional drivers, since regions are environmentally distinct. Finally, we predict global maps of biodiversity at two grains, identifying areas of exceptional species turnover in China, East Africa, and North America. Our cross-scale approach unifies disparate results from previous studies regarding environmental versus biogeographic predictors of biodiversity, and enables efficient integration of heterogeneous data.

ecology↗

Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities

Because biodiversity is multidimensional and scale-dependent, it is challenging to estimate its change. However, it is unclear (1) how much scale-dependence matters for empirical studies, and (2) if it does matter, how exactly we should quantify biodiversity change. To address the first question, we analyzed studies with comparisons among multiple assemblages, and found that rarefaction curves frequently crossed, implying reversals in the ranking of species richness across spatial scales. Moreover, the most frequently measured aspect of diversity--species richness--was poorly correlated with other measures of diversity. Second, we collated studies that included spatial scale in their estimates of biodiversity change in response to ecological drivers and found frequent and strong scale-dependence, including nearly 10% of studies which showed that biodiversity changes switched directions across scales. Having established the complexity of empirical biodiversity comparisons, we describe a synthesis of methods based on rarefaction curves that allow more explicit analyses of spatial and sampling effects on biodiversity comparisons. We use a case study of nutrient additions in experimental ponds to illustrate how this multi-dimensional and multi-scale perspective informs the responses of biodiversity to ecological drivers.\n\nStatement of AuthorshipJC and BM conceived the study and the overall approach, and all authors participated in multiple working group meetings to develop and refine the approach. BM collected the data for the meta-analysis that led to Fig. 2,3; JC collected the data for the metaanalysis that led to Figure 4 and S1; SB and FM did the analyses for Figures 2-4; DM, FM and XX wrote the code for the analysis used for the recipe and case study in Figure 6. JC, BM and NG wrote first drafts of most sections, and all authors contributed substantially to revisions.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/275701_fig2.gif\" ALT=\"Figure 2\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@485725org.highwire.dtl.DTLVardef@151643corg.highwire.dtl.DTLVardef@8bc9e0org.highwire.dtl.DTLVardef@1729374_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Bivariate relationships between N, SPIE and S for 346 communities across the 37 datasets taken from McGill (2011b)(see Appendix 1). (A) S as a function of N; (B) S as a function of SPIE. (N vs SPIE not shown). Black lines depict the relationships across studies (and correspond to R2 fixed); colored points and lines show the relationships within studies. All axes are log-scale. Insets are histograms of the study-level slopes, with the solid line representing the slope across all studies. Gray bars indicate the study-level slope did not differ from zero, blue indicates a significant positive slope, and red indicates a significant negative slope.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC=\"FIGDIR/small/275701_fig3.gif\" ALT=\"Figure 3\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@12ee0e9org.highwire.dtl.DTLVardef@affc46org.highwire.dtl.DTLVardef@1db6453org.highwire.dtl.DTLVardef@97ab06_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO Representative rarefaction curves, the proportion of curves that crossed, and counts of how often curves crossed. (A) Rarefaction curves for different local communities within two datasets: marine invertebrates (nematodes) along a gradient from a waste plant outlet (Lambshead 1986), and trees in a Ugandan rainforest (Eggeling 1947); axes are log-transformed. (B) Counts of how many times pairs of rarefaction curves (from the same community) crossed; y-axis is on a log-scale.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC=\"FIGDIR/small/275701_fig4.gif\" ALT=\"Figure 4\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@15d497borg.highwire.dtl.DTLVardef@18354fforg.highwire.dtl.DTLVardef@1413a54org.highwire.dtl.DTLVardef@15c8451_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 4.C_FLOATNO Results of a meta-analysis of scale-dependent responses to a number of different ecological drivers (see Appendix 2). Points represent the log response ratio comparing species richness in control compared to treatments in a given comparison measured at the smallest (x-value) and largest (y-value) scale. The solid line indicates the 1: 1 line expected if effect sizes were not scale-dependent. Points above and below this line indicate effect sizes that are larger or smaller, respectively, as scale increases; points in the upper left and lower right quadrats represent cases where the direction of change shifted from positive to negative, or vice versa, with increasing scale. The dashed line indicates the best fit correlation, which is significantly different than the 1:1 line (P<0.01), indicating that overall, effect sizes tend to be larger at smaller scales than at larger scales. Colors for points indicate categorizations into different ecological drivers.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/275701_figS1.gif\" ALT=\"Figure 1\">\nView larger version (15K):\norg.highwire.dtl.DTLVardef@f30150org.highwire.dtl.DTLVardef@1db2db9org.highwire.dtl.DTLVardef@970507org.highwire.dtl.DTLVardef@cb37e6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure S1.C_FLOATNO Results showing the ratio of log-response ratio effect sizes from experiments where species richness responses were measured at two spatial scales (small scale/large scale). The dashed line at 0 would indicate studies where the effect sizes were the same at the smaller and larger scale. For each category of ecological driver, the means are above 1, indicating that the measured effect size is larger at the smaller relative to larger size, and this difference is statistically significant for land use, invasive species, and grazing/predation.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC=\"FIGDIR/small/275701_fig6.gif\" ALT=\"Figure 6\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@1c313d0org.highwire.dtl.DTLVardef@49df71org.highwire.dtl.DTLVardef@1ec92a3org.highwire.dtl.DTLVardef@8f1693_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO Effect of nutrient additions on several measurements of biodiversity from Table 1 (see data in Appendix 3). Each biodiversity measure was calculated at the -scale (1 mesocosm) (Panels A,B), {gamma}-scale (15 mesocosms) (Panels C,D), as well as the -scale (i.e. turnover across scales, {gamma}/)(Panels D,E,F). See Chase (2010) for details on the experimental design and the mobr package (McGlinn et al. submitted, https://github.com/MoBiodiv/mobr) for details on the statistical methods\n\nC_FIG\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC=\"FIGDIR/small/275701_fig1.gif\" ALT=\"Figure 1\">\nView larger version (15K):\norg.highwire.dtl.DTLVardef@133d65org.highwire.dtl.DTLVardef@123b3faorg.highwire.dtl.DTLVardef@fce7ccorg.highwire.dtl.DTLVardef@1d62dab_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO A. Individual-based rarefaction curves of three hypothetical communities (labelled A,B, C) where ranked differences between communities are consistent across scales. B. Individual-based rarefaction curves of three hypothetical communities (labelled A,B, C) where rankings between communities switch because of differences in the total numbers of species, and their relative abundances. Dotted vertical lines illustrate sampling scales where rankings switch. These curves were generated using the sim_sad function from the mobsim R package (May et al. 2018).\n\nC_FIG\n\n\n\nData accessibility statementAll data for meta-analyses and case study will be deposited in a publically available repository with DOI upon acceptance (available in link for submission).

ecology↗

MoB (Measurement of Biodiversity): a method to separate the scale-dependent effects of species abundance distribution, density, and aggregation on diversity change

O_LILittle consensus has emerged regarding how proximate and ultimate drivers such as productivity, disturbance, and temperature may affect species richness and other aspects of biodiversity. Part of the confusion is that most studies examine species richness at a single spatial scale and ignore how the underlying components of species richness can vary with spatial scale.\nC_LIO_LIWe provide an approach for the measurement of biodiversity (MoB) that decomposes changes in species rarefaction curves into proximate components attributed to: 1) the species abundance distribution, 2) density of individuals, and 3) the spatial arrangement of individuals. We decompose species richness by comparing spatial and nonspatial sample- and individual-based species rarefaction curves that differentially capture the influence of these components to estimate the relative importance of each in driving patterns of species richness change.\nC_LIO_LIWe tested the validity of our method on simulated data, and we demonstrate it on empirical data on plant species richness in invaded and uninvaded woodlands. We integrated these methods into a new R package (mobr).\nC_LIO_LIThe metrics that mobr provides will allow ecologists to move beyond comparisons of species richness in response to ecological drivers at a single spatial scale towards a dissection of the proximate components that determine species richness across scales.\nC_LI

ecology↗