Biology subjects

Gotelli, N. J.

Publications and source records attributed to Gotelli, N. J..

12 recordsLinked to original sources

bioRxiv2024

Body size is a better predictor of intra- than interspecific variation of animal stoichiometry across realms

Animal stoichiometry affects fundamental processes ranging from organismal physiology to global element cycles. However, it is unknown whether animal stoichiometry follows predictable scaling relationships with body mass and whether adaptation to life on land or water constrains patterns of elemental allocation. To test both interspecific and intraspecific body-size scaling relationships of the nitrogen (N), phosphorus (P), and N:P content of animals, we used a subset of the StoichLife database encompassing 9,933 individual animals (vertebrates and invertebrates) belonging to 1,543 species spanning 10 orders of magnitude of body size from terrestrial, freshwater, and marine realms. Across species, body mass did not explain much variation in %N and %P composition, although the %P of invertebrates decreased with size. The effects of body size on species elemental content were small in comparison to the effects of taxonomy. Body size was a better predictor of intraspecific than interspecific elemental patterns. Between 42 to 45% in intraspecific stoichiometric variation was explained by body size for 27% of vertebrate species and 35% of invertebrate species. Further, differences between organisms inhabiting aquatic and terrestrial realms were observed only in invertebrate interspecific %N, suggesting that the realm does not play an important role in determining elemental allocation of animals. Based on our analysis of the most comprehensive animal stoichiometry database, we conclude that (i) both body size and realm are relatively weak predictors of animal stoichiometry across taxa, and (ii) body size is a good predictor of intraspecific variation in animal elemental content, which is consistent with tissue-scaling relationships that hold broadly across large groups of animals. This research reveals a lack of general scaling patterns in the elemental content across animals and instead points to a large variation in scaling relationships within and among lineages.

ecology↗

bioRxiv2023

Disentangling non-random structure from random placement when estimating β-diversity through space or time

There is considerable interest in understanding patterns of {beta}-diversity that measure the amount of change in species composition through space or time. Most hypotheses for {beta}-diversity evoke nonrandom processes that generate spatial and temporal within species aggregation; however, {beta}-diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the non-random contribution of species compositional differences across samples to {beta}-diversity. We isolate the effect of within-species spatial or temporal aggregation on beta-diversity using a coverage standardized metric of {beta}-diversity ({beta}C). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across -, {gamma}-, and {beta}-scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and can be used to examine species turnover both within and between meta-communities. Open Research statement: all code and data used in this manuscript are available at the following link: https://github.com/MoBiodiv/beta_concept

ecology↗

bioRxiv2022

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↗

bioRxiv2021

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↗

bioRxiv2020

Regulation by the pitcher plant Sarracenia purpurea of the structure of its inquiline food web

AO_SCPLOWBSTRACTC_SCPLOWPhytotelmata, the water-filled habitats in pitcher plants, bromeliad tanks, and tree-holes, host multitrophic food webs that are model experimental systems for studying food-web structure and dynamics. However, the plant usually is considered simply as an inert container, not as an interacting part of the food web. We used a manipulative field experiment with a response-surface design to determine effects of nutrient enrichment (multiple levels of NH4NO3, PO4, and captured prey), top predator (removed or present), and the plant itself (with or without plastic tubes inserted into the pitchers to isolate the food web from the plant) on the macrobial food web within the modified leaves ("pitchers") of the carnivorous pitcher plant Sarracenia purpurea. Connection to the plant, addition of NH4NO3 and removal of the top predator significantly increased the food webs saturation, defined as its trophic depth and number of interactions. No effects on food-web saturation resulted from addition of PO4 or supplemental prey. Plants such as S. purpurea that create phytotelmata are more than inert containers and their inhabitants are more than commensal inquilines. Rather, both the plant and the inquilines are partners in a complex network of interactions.

ecology↗

bioRxiv2020

Clockwise and counterclockwise hysteresis characterize state changes in the same aquatic ecosystem

Incremental increases in a driver variable, such as nutrients or detritus, can trigger abrupt shifts in aquatic ecosys-tems. Once these ecosystems change state, a simple reduction in the driver variable may not return them to their original state. Because of the long time scales involved, we still have a poor understanding of the dynamics of ecosys-tem recovery after a state change. A model system for understanding ecosystem recovery is the aquatic microecosystem that inhabits the cup-shaped leaves of the pitcher plant Sarracenia purpurea. With enrichment of organic matter, this system flips within 1 to 3 days from an oxygen-rich state to an oxygen-poor (hypoxic) state. In a replicated green-house experiment, we enriched pitcher plant leaves at different rates with bovine serum albumin (BSA), a molecular substitute for detritus. Changes in dissolved oxygen ([O2]) and undigested BSA concentration were monitored during enrichment and recovery phases. At low enrichment rates, ecosystems showed a substantial lag in the recovery of [O2] (clockwise hysteresis). At intermediate enrichment rates, [O2] tracked the levels of undigested BSA with the same profile during the enrichment and recovery phases (no hysteresis). At high enrichment rates, we observed a novel response: changes in [O2] were proportionally larger during the recovery phase than during the enrichment phase (counter-clockwise hysteresis). These experiments demonstrate that detrital enrichment rate can modulate a diversity of hysteretic responses in a single aquatic ecosystem. With counter-clockwise hysteresis, rapid reduction of a driver variable following high enrichment rates may be a viable restoration strategy.

ecology↗

bioRxiv2020

Resolving the species pool dependence of beta-diversity using coverage-based rarefaction

Understanding how species are non-randomly distributed in space, as well as how the resulting spatial structure of diversity responds to ecological, biogeographic and anthropogenic drivers is a critical piece of the biodiversity puzzle. However, most metrics that quantify the spatial structure of diversity (i.e., community differentiation), such as Whittakers classical {beta}-diversity metric are influenced by sampling effects. As a result, these measures are influenced by species pool size, species abundance distributions and numbers of individuals. Null models have been proposed to evaluate the degree of differentiation among communities due to spatial structuring relative to that expected from sampling effects. However, to date, these null models do not accommodate the influence of sample completeness (i.e. the proportion of the species pool in the sample). Here, we develop an approach that makes use of individual- and coverage-based rarefaction and extrapolation. Using spatially explicit simulations, we show that our derived metric, {beta}C, captures changes in intraspecific aggregation independently of changes in the species pool size. We then provide two case studies examining spatial structure in forest plots spanning latitudinal gradients: (1) a re-analysis of the "Gentry" plot dataset, and (2) comparing a high diversity plot in Barro Colorado Island, Panama with a low diversity plot in Harvard Forest, Massachusetts, USA. We find no evidence for systematic changes in spatial structure with latitude in these datasets. As it is rooted in biodiversity sampling theory and explicitly controls for sample completeness, our approach represents an important advance over existing null models for spatial aggregation. Potential applications of the approach range from better descriptors of biogeographic diversity patterns to the consolidation of local and regional diversity trends in the current biodiversity crisis. Open research statementThe novel code for the calculation of {beta}C can be found in supplementary material S4. Empirical data sets utilized for this research are as follows: Phillips & Miller (2002), Orwig et al. (2015), Condit et al. (2019). Our research repository including the novel code is also available at https://github.com/t-engel/betaC and will be uploaded to Zenodo upon acceptance of this manuscript.

ecology↗

bioRxiv2019

A multiscale framework for disentangling the roles of evenness, density and aggregation on diversity gradients

Disentangling the drivers of diversity gradients can be challenging. The Measurement of Biodiversity (MoB) framework decomposes changes in species diversity into three components of community structure: the species abundance distribution (SAD), the total community abundance, and the within-species spatial aggregation. Here we extend MoB from categorical treatment comparisons to quantify variation along continuous geographic or environmental gradients. Our approach requires sites along a gradient, each consisting of georeferenced plots of abundance-based species composition data. We demonstrate our method using a case study of ants sampled along an elevational gradient of 28 sites in mixed deciduous forest of the Great Smoky Mountains National Park, USA. MoB analysis revealed that ant species richness decreased along the elevational gradient because of changes in the SAD and in spatial aggregation, but not because of changes in the number of individuals. Specifically, with increasing elevation, species evenness was lower and species were less aggregated. These results do not support the more-individuals hypothesis; alternative hypotheses are required to explain why evenness and aggregation decrease with elevation. Our extension of MoB has the potential to elucidate the drivers of diversity along environmental gradients and should be useful for a variety of assemblage-level data collected along gradients.

ecology↗

bioRxiv2018

Expanded view of the ecological genomics of ant responses to climate change

Given the abundance, broad distribution, and diversity of roles that ants play in many ecosystems, they are an ideal group to serve as ecosystem indicators of climatic change. At present, only a few whole-genome sequences of ants are available (19 of > 16,000 species), mostly from tropical and sub-tropical species. To address this limited sampling, we sequenced genomes of temperate-latitude species from the genus Aphaenogaster, a genus with important seed dispersers. In total, we sampled seven colonies of six species: A. ashmeadi, A. floridana, A. fulva, A. miamiana, A. picea, and A. rudis. The geographic ranges of these species collectively span eastern North America from southern Florida to southern Canada, which encompasses a latitudinal gradient in which many climatic variables are changing rapidly. For the six genomes, we assembled an average of 271,039 contigs into 47,337 scaffolds. The mean genome size was 370.5 Mb, ranging from 310.3 to 429.7, which is comparable to that of other sequenced ant genomes (212.8 to 396.0 Mb) and flow cytometry estimates (210.7 to 690.4 Mb). In an analysis of currently sequenced ant genomes and the new Aphaenogaster sequences, we found that after controlling for both spatial autocorrelation and phylogenetics ant genome size was marginally correlated with sample site climate similarity. Of all examined climate variables, minimum temperature showed the strongest correlation with genome size, with ants from locations with colder minimum temperatures having larger genomes. These results suggest that temperature extremes could be a selective force acting on ant genomes and point to the need for more extensive sequencing of ant genomes.

genomics↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2016

Regime shifts, alternative states and hysteresis in the Sarracenia microecosystem

Changes in environmental conditions can lead to rapid shifts in the state of an ecosystem (\"regime shifts\"), which, even after the environment has returned to previous conditions, subsequently recovers slowly to the previous state (\"hysteresis\"). Large spatial and temporal scales of dynamics, and the lack of frameworks linking observations to models, are challenges to understanding and predicting ecosystem responses to perturbations. The naturally-occurring microecosystem inside leaves of the northern pitcher plant (Sarracenia purpurea) exhibits oligotrophic and eutrophic states that can be induced by adding insect prey. Here, we further develop a model for simulating these dynamics, parameterize it using data from a prey addition experiment and conduct a sensitivity analysis to identify critical zones within the parameter space. Simulations illustrate that the microecosystem model displays regime shifts and hysteresis. Parallel results were observed in the plant itself after experimental enrichment with prey. Decomposition rate of prey was the main driver of system dynamics, including the time the system remains in an anoxic state and the rate of return to an oxygenated state. Biological oxygen demand in fluenced the shape of the systems return trajectory. The combination of simulated results, sensitivity analysis and use of empirical results to parameterize the model more precisely demonstrates that the Sarracenia microecosystem model displays behaviors qualitatively similar to models of larger ecological systems.

ecology↗