Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study

The Mantel correlogram is an elegant way to compute a correlogram for multivariate data. However, recent papers raised concerns about the power of the Mantel test itself. Hence the question: Is the Mantel correlogram powerful enough to be useful? To explore this issue, we compared the performances of the Mantel correlogram to those of other methods, using numerical simulations based on random, normally distributed data. For a single response variable, we compared it to the Moran and Geary correlograms. Type I error rates of the three methods were correct. Power of the Mantel correlogram was nearly as high as that of the univariate methods. For the multivariate case, the test of the multivariate variogram developed in the context of multiscale ordination is in fact a Mantel test, so that the power of the two methods is the same by definition. We devised an alternative permutation test based on the variance, which yielded similar results. Overall, the power of the Mantel test was high, the method successfully detecting spatial correlation at rates similar to the permutation test of the variance statistic in multivariate variograms. We conclude that the Mantel correlogram deserves its place in the ecologist's toolbox.     

Determining patterns of variability in ecological communities: time lag analysis revisited

All ecological communities experience change over time. One method to quantify temporal variation in the patterns of relative abundance of communities is time lag analysis (TLA). It uses a distance-based approach to study temporal community dynamics by regressing community dissimilarity over increasing time lags (one-unit lags, two-unit lags, three-unit lags). Here, we suggest some modifications to the method and revaluate its potential for detecting patterns of community change. We apply Hellinger distance based TLA to artificial data simulating communities with different levels of directional and stochastic dynamics and analyse their effects on the slope and its statistical significance. We conclude that statistical significance of the TLA slope (obtained by a Monte Carlo permutation procedure) is a valid criterion to discriminate between (i) communities with directional change in species composition, regardless whether it is caused by directional abundance change of the species or by stochastic change according to a Markov process, and (ii) communities that are composed of species with population sizes oscillating around a constant mean or communities whose species abundances are governed by a white noise process. TLA slopes range between 0.02 and 0.25, depending on the proportions of species with different dynamics; higher proportions of species with constant means imply shallower slopes; and higher proportions of species with stochastic dynamics or directional change imply steeper slopes. These values are broadly in line with TLA slopes from real world data. Caution must be exercised when TLA is used for the comparison of community time series with different lengths since the slope depends on time series length and tends to decrease non-linearly with it.     

Elevated land runoff after European settlement perturbs persistent foraminiferal assemblages on the Great Barrier Reef

Coral reefs are under pressure from a variety of human-induced disturbances, but demonstration of ecosystem changes and identification of stressors are often difficult. We tested whether global change or increased agricultural runoff after European settlement of Northeast Australia (ca. 1860) has affected inshore reefs of the Great Barrier Reef. Eleven sediment cores were retrieved from inner reefs, intermediate reefs, and outer-island reefs, and benthic foraminiferal assemblages were analyzed in dated (14C, 210Pb, 137Cs) core sections (N = 82 samples). Data were grouped into six age bands (< 55, 55-150, 150-500, 500-1000, 1000-1500, and > 1500 yr). Principal component analysis and two-factor (Zone and Age) permutational analysis of variance (PERMANOVA) suggested that assemblages from the three zones were significantly different from each other over several millennia, with symbiont-bearing (mixotrophic) species dominating the outer reefs. A significant interaction term indicated that within-zone patterns varied. Assemblages in outer reefs unaffected from increased land runoff were persistent until present times. In both other zones, assemblages were also persistent until 150 yr ago, suggesting that benthic foraminiferal assemblages are naturally highly persistent over long (> 2000 yr) timescales. Assemblages in core sections < 55 yr old from inner reefs were significantly (post hoc t test) different from those older than 150 yr. Similarly, assemblages < 55 yr old from intermediate reefs were significantly different compared to older assemblages. A multivariate regression tree (environmental variables: Zone and Age) explained 56.8% of the variance in foraminiferal assemblages and confirmed patterns identified by PERMANOVA. With some exceptions, changes on the inner and intermediate reefs were consistent with a model predicting that increased nutrients and higher turbidity enhance relative abundance of heterotrophic species. Given that assemblages did not change in outer-island reefs (not impacted by runoff) we argue that changes in assemblages due to global change can be rejected as an explanation. Thus, the findings are more consistent with the hypothesis that agricultural runoff since European settlement altered foraminiferal assemblages than with the hypothesis that global forcing caused changes.     

SOCPROG programs: analysing animal social structures

SOCPROG is a set of programs which analyses data on animal associations. Data usually come from observations of the social behaviour of individually identifiable animals. Associations among animals, sampling periods, restrictions on the data and association indices can be defined very flexibly. SOCPROG can analyse data sets including 1,000 or more individuals. Association matrices are displayed using sociograms, principal coordinates analysis, multidimensional scaling and cluster analyses. Permutation tests, Mantel and related tests and matrix correlation methods examine hypotheses about preferred associations among individuals and classes of individual. Weighted network statistics are calculated and can be tested against null hypotheses. Temporal analyses include displays of lagged association rates (rates of reassociation following an association). Models can be fitted to lagged association rates. Multiple association measures, including measures produced by other programs such as genetic or range use data, may be analysed using Mantel tests and principal components analysis. SOCPROG also performs mark-recapture population analyses and movement analyses. SOCPROG is written in the programming language MATLAB and may be downloaded free from the World Wide Web.     

Classification and ordination of benthic samples from Bedeque Bay,an estuary in Prince Edward Island,Canada

An attempt was made to identify the causes of the distribution of benthos within Bedeque Bay using multivariate techniques programmed for the computer. Both classification by a hierarchical cluster analysis, and ordination by principal components analysis suggested that a large proportion of the variance in the data was directly or indirectly correlated with a salinity gradient. Classification divided the species into two main groups, a in the upper half of the estuary where lower salinities and larger salinity fluctuations occurred, and group b in the lower half of the estuary with a more stable salinity regime. The group b species were further subdivided into those preferring soft mud and those preferring sandier sediments. The group a species were divided into a well-developed oyster association and various sub-groups less strongly associated with oysters. Five principal components were required to account for 50% of the variance in the data. The first axis accounted for 20% of the variance and was shown by a non-parametric test to be correlated with the salinity gradient. Axes II to V could not be interpreted, but possibly represented complex species interactions. By providing hard substrates and altering the natura of the sediment, oysters and mussels produced conditions suitable for many other species.     

Defining Critical Habitats of Threatened and Endemic Reef Fishes with a Multivariate Approach

Understanding critical habitats of threatened and endemic animals is essential for mitigating extinction risks, developing recovery plans, and siting reserves, but assessment methods are generally lacking. We evaluated critical habitats of 8 threatened or endemic fish species on coral and rocky reefs of subtropical eastern Australia, by measuring physical and substratum‐type variables of habitats at fish sightings. We used nonmetric and metric multidimensional scaling (nMDS, mMDS), Analysis of similarities (ANOSIM), similarity percentages analysis (SIMPER), permutational analysis of multivariate dispersions (PERMDISP), and other multivariate tools to distinguish critical habitats. Niche breadth was widest for 2 endemic wrasses, and reef inclination was important for several species, often found in relatively deep microhabitats. Critical habitats of mainland reef species included small caves or habitat‐forming hosts such as gorgonian corals and black coral trees. Hard corals appeared important for reef fishes at Lord Howe Island, and red algae for mainland reef fishes. A wide range of habitat variables are required to assess critical habitats owing to varied affinities of species to different habitat features. We advocate assessments of critical habitats matched to the spatial scale used by the animals and a combination of multivariate methods. Our multivariate approach furnishes a general template for assessing the critical habitats of species, understanding how these vary among species, and determining differences in the degree of habitat specificity. Definición de Hábitats Críticos para Peces Arrecifales Amenazados y Endémicos Mediante un Método Multivariado     

Quantitative approaches to the analysis of stable isotope food web data

Ecologists use stable isotopes (delta13C, delta15N) to better understand food webs and explore trophic interactions in ecosystems. Traditionally, delta13C vs. delta15N bi-plots have been used to describe food web structure for a single time period or ecosystem. Comparisons of food webs across time and space are increasing, but development of statistical approaches for testing hypotheses regarding food web change has lagged behind. Here we present statistical methodologies for quantitatively comparing stable isotope food web data. We demonstrate the utility of circular statistics and hypothesis tests for quantifying directional food web differences using two case studies: an arthropod salt marsh community across a habitat gradient and a freshwater fish community from Lake Tahoe, USA, over a 120-year time period. We calculated magnitude and mean angle of change (theta) for each species in food web space using mean delta13C and delta15N of each species as the x, y coordinates. In the coastal salt marsh, arthropod consumers exhibited a significant shift toward dependence on Spartina, progressing from a habitat invaded by Phragmites to a restored Spartina habitat. In Lake Tahoe, we found that all species from the freshwater fish community shifted in the same direction in food web space toward more pelagic-based production with the introduction of nonnative Mysis relicta and onset of cultural eutrophication. Using circular statistics to quantitatively analyze stable isotope food web data, we were able to gain significant insight into patterns and changes in food web structure that were not evident from qualitative comparisons. As more ecologists incorporate a food web perspective into ecosystem analysis, these statistical tools can provide a basis for quantifying directional food web differences from standard isotope data.     

Evolutionary Consequences of Extinctions in Populations of a Hawaiian Honeycreeper

We report on the evolutionary change in bill size of a species of Hawaiian honeycreeper resulting from an apparent dietary shift caused by dramatic declines and extinctions of lobelioids, a historically favored nectar source. Although it now feeds mainly on the flowers of the ohia tree ( Metrosideros polymorpha ), early Hawaiian avifaunal accounts report that the i'iwi ( Vestiaria coccinea ), which has a long decurved bill, fed primarily on the flowers of Hawaiian Lobelioideae, which typically have long decurved corollas. A coevolutionary association of i'iwi bill and flower morphology has often been asserted. We test the hypothesis that the shift in the i'iwi's diet from the long corolla lobelioid flowers to ohia flowers, which lack corollas, resulted in directional selection for shorter bills. We evaluate this hypothesis by comparing the morphological characters of museum specimens from the island of Hawaii collected before 1902 with recent specimens from the Hakalau National Wildlife Refuge, Hawaii. We examine evidence of change in morphological characters using multivariate analysis and a nonparametric cubic spline technique. Results from all analyses are congruent: bill length is shorter in recent specimens.     

The effects of direct and indirect constraints on biological communities

Human activities are expected to result in a diversity of directional or stochastic constraints that affect species either directly or by indirectly impacting their resources. However, there is no theoretical framework to predict the complex and various effects of these constraints on ecological communities. We developed a dynamic model that mimics the use of different resource types by a community of competing species. We investigated the effects of different environmental constraints (affecting either directly the growth rate of species or having indirect effects on their resources) on several biodiversity indicators. Our results indicate that (i) in realistic community models (assuming uneven resource requirements among species) the effects of perturbations are strongly buffered compared to neutral models; (ii) the species richness of communities can be maximized for intermediate levels of direct constraints (unimodal response), even in the absence of trade-off between competitive ability and tolerance to constraints; (iii) no such unimodal response occurs with indirect constraints; (iv) an increase in the environmental (e.g., climatic) variance may have different effects on community biomass and species richness.     

Integrated models to support multiobjective ecological restoration decisions

Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state‐and‐transition, species‐distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade‐offs in management outcomes through integrated modeling and structured decision‐support approaches has wide application for situations in which trade‐offs exist between competing conservation objectives.     

Reducing over-reporting of deterministic co-occurrence patterns in biotic communities

Null models of species co-occurrence are widely used to infer the existence of various ecological processes. Here we investigate the susceptibility of the most commonly used of these models (the C-score in conjunction with the sequential swap algorithm) to type 1 and type 2 errors. To do this we use simulated datasets with a range of numbers of sites, species and coefficients of variation (CV) in species abundance. We find that this model is particularly susceptible to type 1 errors when applied to large matrices and those with low CV in species abundance. As expected, type 2 error rates decrease with increasing numbers of sites and species, although they increase with increasing CV in species abundance. Despite this, power remains acceptable over a wide range of parameter combinations. The susceptibility of this analytical method to type 1 errors indicates that many previous studies may have incorrectly reported the existence of deterministic patterns of species co-occurrence. We demonstrate that in order to overcome the problem of high type 1 error rates, the number of swaps used to generate null distributions for smaller matrices needs to be increased to over 50,000 swaps (well beyond the 5000 commonly used in published analyses and the 30,000 suggested by Lehsten and Harmand, 2006). We also show that this approach reduces type 1 error rates in real datasets. However, even using this solution, larger datasets still suffer from high type 1 error rates. Such datasets therefore require the use of very large numbers of swaps, which calls for improvements in the most commonly used software. In general, users of this powerful analytical method must be aware that they need surprisingly large numbers of swaps to obtain unbiased estimates of structuring in biotic communities.     

Effect of IUCN Red List category on public attention to mammals

Cultural data is a powerful tool to analyze public awareness of key societal issues, including the conservation of nature. I used two publicly available repositories of cultural data, Google Trends and Google Ngram, to quantify the effect of the International Union for the Conservation of Nature (IUCN) Red List conservation status on public attention toward 4539 mammal species. With Google Trends, I calculated whether Google searches for their common and scientific names have been increasing or decreasing over time. I also ran an anomaly detection analysis to investigate whether a change in red-list status directly results in an increase in Google searches. Additionally, I quantified the mentions of species’ common and scientific names in English texts with Google Ngram. Overall, Google searches for most mammal species remained at similar levels or increased since 2008. The severity of species’ IUCN Red List status was a significant predictor of increasing Google searches, although the effect size was relatively small. Red-list status seemed strongly confounded with mammal body size. Species that moved to a higher-risk category spiked significantly in Google searches directly after the new designation. The mention of species’ common names in the Google Ngram's English 2019 corpus significantly increased as the red-list category increased. These results provide valuable insight into the importance of the IUCN Red List for increasing public awareness and the usefulness of publicly available cultural data on examining the effectiveness of specific conservation efforts and thus evaluating targets for support and funding.     

Estimating trend precision and power to detect trends across grouped count data

Ecologists commonly use grouped or clustered count data to estimate temporal trends in counts, abundance indices, or abundance. For example, the U.S. Breeding Bird Survey data represent multiple counts of birds from within each of multiple, spatially defined routes. Despite a reliance on grouped counts, analytical methods for prospectively estimating precision of trend estimates or statistical power to detect trends that explicitly acknowledge the characteristics of grouped count data are undescribed. These characteristics include the fact that the sampling variance is an increasing function of the mean, and that sampling and group-level variance estimates are generally estimated on different scales (the sampling and log scales, respectively). We address these issues for repeated sampling of a single population using an analytical approach that has the flavor of a generalized linear mixed model, specifically that of a negative binomial-distributed count variable with random group effects. The count mean, including grand intercept, trend, and random group effects, is modeled linearly on the log scale, while sampling variance of the mean is estimated on the log scale via the delta method. Results compared favorably with those derived using Monte Carlo simulations. For example, at trend = 5% per temporal unit, differences in standard errors and in power were modest relative to those estimated by simulation (< or = /11/% and < or = /16/%, respectively), with relative differences among power estimates decreasing to < or = /7/% when power estimated by simulations was > or = 0.50. Similar findings were obtained using data from nine surveys of fingernail clams in the Mississippi River. The proposed method is suggested (1) where simulations are not practical and relative precision or power is desired, or (2) when multiple precision or power calculations are required and where the accuracy of a fraction of those calculations will be confirmed using simulations.     

Evidence of Local Adaptation in the Demographic Response of American Ginseng to Interannual Temperature Variation

Abstract: Bioclimatic envelope models of species’ responses to climate change are used to predict how species will respond to increasing temperatures. These models are frequently based on the assumption that the northern and southern boundaries of a species’ range define its thermal niche. However, this assumption may be violated if populations are adapted to local temperature regimes and have evolved population‐specific thermal optima. Considering the prevalence of local adaptation, the assumption of a species‐wide thermal optimum may be violated for many species. We used spatially and temporally extensive demographic data for American ginseng (Panax quinquefolius L.) to examine range‐wide variation in response of population growth rate (λ) to climatic factors. Our results suggest adaptation to local temperature, but not precipitation. For each population, λ was maximized when annual temperatures were similar to site‐specific, long‐term mean temperatures. Populations from disparate climatic zones responded differently to temperature variation, and there was a linear relation between population‐level thermal optima and the 30‐year mean temperature at each site. For species that are locally adapted to temperature, bioclimatic envelope models may underestimate the extent to which increasing temperatures will decrease population growth rate. Because any directional change from long‐term mean temperatures will decrease population growth rates, all populations throughout a species’ range will be adversely affected by temperature increase, not just populations at southern and low‐elevation boundaries. Additionally, when a species’ local thermal niche is narrower than its range‐wide thermal niche, a smaller temperature increase than would be predicted by bioclimatic envelope approaches may be sufficient to decrease population growth.     

Connecting today's climates to future climate analogs to facilitate movement of species under climate change

Increasing connectivity is an important strategy for facilitating species range shifts and maintaining biodiversity in the face of climate change. To date, however, few researchers have included future climate projections in efforts to prioritize areas for increasing connectivity. We identified key areas likely to facilitate climate‐induced species’ movement across western North America. Using historical climate data sets and future climate projections, we mapped potential species’ movement routes that link current climate conditions to analogous climate conditions in the future (i.e., future climate analogs) with a novel moving‐window analysis based on electrical circuit theory. In addition to tracing shifting climates, the approach accounted for landscape permeability and empirically derived species’ dispersal capabilities. We compared connectivity maps generated with our climate‐change‐informed approach with maps of connectivity based solely on the degree of human modification of the landscape. Including future climate projections in connectivity models substantially shifted and constrained priority areas for movement to a smaller proportion of the landscape than when climate projections were not considered. Potential movement, measured as current flow, decreased in all ecoregions when climate projections were included, particularly when dispersal was limited, which made climate analogs inaccessible. Many areas emerged as important for connectivity only when climate change was modeled in 2 time steps rather than in a single time step. Our results illustrate that movement routes needed to track changing climatic conditions may differ from those that connect present‐day landscapes. Incorporating future climate projections into connectivity modeling is an important step toward facilitating successful species movement and population persistence in a changing climate.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

Empirical assessment of state-and-transition models with a long-term vegetation record from the Sonoran Desert

Resilience-based frameworks, including state-and-transition models (STM), are being increasingly called upon to inform policy and guide ecosystem management, particularly in rangelands. Yet, multiple challenges impede their effective implementation: (1) paucity of empirical tests of resilience concepts, such as alternative states and thresholds, and (2) heavy reliance on expert models, which are seldom tested against empirical data. We developed an analytical protocol to identify unique plant communities and their transitions, and applied it to a long-term vegetation record from the Sonoran Desert (1953-2009). We assessed whether empirical trends were consistent with resilience concepts, and evaluated how they may inform the construction and interpretation of expert STMs. Seven statistically distinct plant communities were identified based on the cover of 22 plant species in 68 permanent transects. We recorded 253 instances of community transitions, associated with changes in species composition between successive samplings. Expectedly, transitions were more frequent among proximate communities with similar species pools than among distant communities. But unexpectedly, communities and transitions were not strongly constrained by soil type and topography. Only 18 transitions featured disproportionately large compositional turnover (species dissimilarity ranged between 0.54 and 0.68), and these were closely associated with communities that were dominated by the common shrub (burroweed, Haplopappus tenuisecta); indicating that only some, and not all, communities may be prone to large compositional change. Temporal dynamics in individual transects illustrated four general trajectories: stability, nondirectional drift, reversibility, and directional shifts that were not reversed even after 2-3 decades. The frequency of transitions and the accompanying species dissimilarity were both positively correlated with fluctuation in precipitation, indicating that climatic drivers require more attention in STMs. Many features of the expert models, including the number of communities and participant species, were consistent with empirical trends, but expert models underrepresented recent increases in cacti while overemphasizing the introduced Lehmann's lovegrass (Eragrostis lehmanniana). Quantification of communities and transitions within long-term vegetation records presents several quantitative metrics such as transition frequency, magnitude of accompanying compositional change, presence of unidirectional trajectories, and lack of reversibility within various timescales, which can clarify resilience concepts and inform the construction and interpretation of STMs.     

Medium-term impacts of hydraulic clam dredgers on a macrobenthic community of the Adriatic Sea (Italy)

Hydraulic dredging targeting the bivalve Chamelea gallina in the northern and central Adriatic Sea has been taking place for over 30 years. In the period 2000–2001, 73 commercial dredgers harvested the resource within the sandy coastal area of the Ancona Maritime District (central Adriatic Sea). Despite this, no study aimed at investigating the impact of the fishery on the macrobenthic community of the area has ever been carried out. Sampling was done at 6 monthly intervals in an attempt to relate the impact of hydraulic dredging to different levels of fishing intensity. Data regarding two depth strata (4–6; 7–10 m) were analysed separately by means of permutational multivariate analysis of variance. The results revealed an overall condition of moderate disturbance within the benthic community, especially so within the 4–6 m depth stratum. The response of the benthic community to varying intensities of fishing activity was rapid, occurring within 6 months. Differences in the response of benthic community to differing intensities of fishing activity were found between the two depth strata considered. Significant differences in multivariate location of the benthic community were revealed between the three disturbance levels in both depth strata. Differences in multivariate dispersion were detected above a threshold level of fishing intensity, only within the shallow community. Differences were found between depth strata relating to species diversity and evenness, with significant differences between levels of fishing intensity being evident only within the 4–6 m depth stratum. The results emphasised that, even in a benthic community that is typical of a moderately disturbed environment, the effects of fishing on community structure were still discernible over and above the natural variation.     

Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula

As important marine mesopredators and sensitive indicators of Antarctic ecosystem change, penguins have been a major focus of long-term biological research in the Antarctic. However, the vast majority of such studies have been constrained by logistics and relate mostly to the temporal dynamics of individual breeding populations from which regional trends have been inferred, often without regard for the complex spatial heterogeneity of population processes and the underlying environmental conditions. Integrating diverse census data from 70 breeding sites across 31 years in a robust, hierarchical analysis, we find that trends from intensely studied populations may poorly reflect regional dynamics and confuse interpretation of environmental drivers. Results from integrated analyses confirm that Pygoscelis adeliae (Adélie Penguins) are decreasing at almost all locations on the Antarctic Peninsula. Results also resolve previously contradictory studies and unambiguously establish that P. antarctica (Chinstrap Penguins), thought to benefit from decreasing sea ice, are instead declining regionally. In contrast, another open-water species, P. papua (Gentoo Penguin), is increasing in abundance and expanding southward. These disparate population trends accord with recent mechanistic hypotheses of biological change in the Southern Ocean and highlight limitations of the influential but oversimplified "sea ice" hypothesis. Aggregating population data at the regional scale also allows us to quantify rates of regional population change in a way not previously possible.