Biology subjects

Blowes, S. A.

Publications and source records attributed to Blowes, S. A..

11 recordsLinked to original sources

bioRxiv2024

Known unknowns and model selection in ecological evidence synthesis

Quantitative evidence synthesis is a prominent path towards generality in ecology. Generality is typically discussed in terms of central tendencies, such as an average effect across a compilation of studies, and the role of heterogeneity for assessing generality is not as well developed. Heterogeneity examines the transferability of ecological effects across contexts, though between-study variance is typically assumed as constant (i.e., homoscedastic). Here, I use two case studies to show how relaxing the assumption of homoscedasticity and cross validation can combine to further the goals of evidence syntheses. First, I examine scale-dependent heterogeneity for a meta-analysis of plant native-exotic species richness relationships, quantifying the relationships among unexplained effect size variation, spatial grain and extent. Second, I examine relationships among patch size, study-level covariates and unexplained variation in species richness using a database of fragmentation studies. Heteroscedastic models quantify where effects can be transferred with more or less certainty, and provide new descriptions of transferability for both case studies. Cross validation can be applied to a single or multiple models, adapted to either the goal of intervention or generalization, and showed that assuming homoscedasticity can limit transferability for both case studies.

ecology↗

bioRxiv2024

Geometric and demographic effects explain contrasting fragmentation-biodiversity relationships across scales

There is consensus that habitat loss is a major driver of biodiversity loss, while the effects of fragmentation, given a constant total habitat amount, are still debated. Here, we use a process-based metacommunity model to show how strongly scale- and context-dependent fragmentation-biodiversity relationships can emerge from the interplay of two types of fragmentation effects - geometric and demographic. Geometric effects arise from the spatial distribution of species and landscape modification, whereas demographic effects reflect long-term changes in species demographic rates following landscape modification. We introduce a novel approach to partitioning these two types of effects and assess how key ecological processes and factors, such as dispersal, habitat heterogeneity, and edge effects, influence geometric, demographic, and net fragmentation effects across spatial scales. We conclude that the framework of geometric and demographic effects can reconcile previous apparently conflicting results and hopefully unlock and advance the debate on biodiversity change in modified landscapes.

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↗

bioRxiv2023

A meta-analysis examining how fish biodiversity varies with marine protected area size and age

Marine Protected Areas (MPAs) are a well-established conservation practice worldwide, but their effectiveness in protecting or replenishing fish biodiversity remains uneven. Understanding the patterns of this heterogeneity is central to general guidelines for MPA design and can ultimately provide guidance on how to maximize MPA potential. Here, we examine associations between the degree of protection, duration of protection, and protected area size, with fish biodiversity inside of protected areas relative to that of sites nearby, but outside of protected areas. We quantitatively synthesize 116 published estimates of species richness from 72 marine protected areas, and 38 estimates of Shannon entropy from 21 marine protected areas. We show that species richness is on average 18% (95% confidence intervals: 10% to 29%) higher in protected areas relative to areas open to fishing; and, on average Shannon entropy is 13% (95% confidence intervals: -2% to 31%) higher within protected areas relative to outside. We find no relationship between the degree or duration of protection with the ratio of species richness inside versus outside of protected areas; both fully and partially protected areas contribute to the accumulation of species inside protected areas, and protected areas of all ages contribute similarly on average to biodiversity conservation. In contrast to our expectations, increasing protected area size was associated with a decreased ratio of species richness sampled at sites inside versus outside the protected area, possibly due, for example, to insufficient enforcement and/or low compliance. Finally, we discuss why meta-analyses such as ours that summarize effect sizes of local scale biodiversity responses, that is, those at a single site, can only give a partial answer to the question of whether larger protected areas harbor more species than comparable unprotected areas.

ecology↗

bioRxiv2023

Widespread reductions in body size are paired with stable assemblage biomass.

Biotic responses to global change include directional shifts in organismal traits. Body size, an integrative trait that determines demographic rates and ecosystem functions, is often thought to be shrinking in the Anthropocene. Here, we assess the prevalence of body size change in six taxon groups across 5,032 assemblage time-series spanning 1960-2020. Using the Price equation to partition this change into within-species body size versus compositional changes, we detect prevailing decreases in body size through time. Change in assemblage composition contributes more to body size changes than within-species trends, but both components show substantial variation in magnitude and direction. The biomass of assemblages remains remarkably stable as decreases in body size trade-off with increases in abundance. One-Sentence SummaryVariable within-species and compositional trends combine into shrinking body size, abundance increases and stable biomass.

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↗

bioRxiv2020

A geometric basis for surface habitat complexity and biodiversity

Structurally complex habitats tend to contain more species and higher total abundances than simple habitats. This ecological paradigm is grounded in first principles: species richness scales with area, and surface area and niche density increase with three-dimensional complexity. Here we present a geometric basis for surface habitats that unifies ecosystems and spatial scales. The theory is framed by fundamental geometric constraints among three structure descriptors—surface height, rugosity and fractal dimension—and explains 98% of surface variation in a structurally complex test system: coral reefs. We then show how coral biodiversity metrics (species richness, total abundance and probability of interspecific encounter) vary over the theoretical structure descriptor plane, demonstrating the value of the theory for predicting the consequences of natural and human modifications of surface structure.Competing Interest StatementThe authors have declared no competing interest.View Full Text

ecology↗

bioRxiv2019

Temperature-related biodiversity change across temperate marine and terrestrial systems

Climate change is reshaping global biodiversity as species respond to changing temperatures. However, the net effects of climate-driven species redistribution on local assemblage diversity remain unknown. Here, we relate trends in species richness and abundance from 21,500 terrestrial and marine assemblage time series across temperate regions (23.5-60.0{degrees}) to changes in air or sea surface temperature. We find a strong coupling between biodiversity and temperature changes in the marine realm, which is conditional on the baseline climate. We detect increases in species richness with increasing temperature that is twice as pronounced in warmer locations, while abundance declines with warming in the warmest marine locations. In contrast, we did not detect systematic temperature-related richness or abundance trends on land, despite a greater magnitude of warming. We also found no evidence for an interaction between biodiversity change and latitude, further emphasizing the importance of baseline climate in structuring assemblages. As the world is committed to further warming, significant challenges remain in maintaining local biodiversity amongst the non-uniform inflow and outflow of "climate migrants" across distinct regions, especially in the ocean.

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

Forest loss as a catalyst of population and biodiversity change

Global assessments have highlighted land-use change as a key driver of biodiversity change. However, we lack real-world global-scale estimates of how habitat transformations such as forest loss and gain are reshaping biodiversity over time. Here, we quantify the influence of 150 years of forest cover change on populations and ecological assemblages worldwide and across taxa by analyzing change in 6,667 time series. We found that forest loss simultaneously intensified ongoing increases and decreases in abundance, species richness and temporal species replacement (turnover) by up to 48%. Temporal lags in these responses extended up to 50 years and increased with species generation time. Our findings demonstrate that land-use change precipitates divergent population and biodiversity change, highlighting the complex biotic consequences of deforestation and afforestation.\n\nOne Sentence SummaryDeclines in forest cover amplify both gains and losses in population abundance and biodiversity over time.

ecology↗

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↗