Within Reach? Habitat Availability as a Function of Individual Mobility and Spatial Structuring.

Organisms need access to particular habitats for their survival and reproduction. However, even if all necessary habitats are available within the broader environment, they may not all be easily reachable from the position of a single individual. Many species distribution models consider populations in environmental (or niche) space, hence overlooking this fundamental aspect of geographical accessibility. Here, we develop a formal way of thinking about habitat availability in environmental spaces by describing how limitations in accessibility can cause animals to experience a more limited or simply different mixture of habitats than those more broadly available. We develop an analytical framework for characterizing constrained habitat availability based on the statistical properties of movement and environmental autocorrelation. Using simulation experiments, we show that our general statistical representation of constrained availability is a good approximation of habitat availability for particular realizations of landscape-organism interactions. We present two applications of our approach, one to the statistical analysis of habitat preference (using step-selection functions to analyze harbor seal telemetry data) and a second that derives theoretical insights about population viability from knowledge of the underlying environment. Analytical expressions for habitat availability, such as those we develop here, can yield gains in analytical speed, biological realism, and conceptual generality by allowing us to formulate models that are habitat sensitive without needing to be spatially explicit.

Predicting coral community recovery using multi-species population dynamics models.

Predicting whether, how, and to what degree communities recover from disturbance remain major challenges in ecology. To predict recovery of coral communities we applied field survey data of early recovery dynamics to a multi-species integral projection model that captured key demographic processes driving coral population trajectories, notably density-dependent larval recruitment. After testing model predictions against field observations, we updated the model to generate projections of future coral communities. Our results indicated that communities distributed across an island landscape followed different recovery trajectories but would reassemble to pre-disturbed levels of coral abundance, composition, and size, thus demonstrating persistence in the provision of reef habitat and other ecosystem services. Our study indicates that coral community dynamics are predictable when accounting for the interplay between species life-history, environmental conditions, and density-dependence. We provide a quantitative framework for evaluating the ecological processes underlying community trajectory and characteristics important to ecosystem functioning.

Predicting coral community recovery using multi-species population dynamics models.

Predicting whether, how, and to what degree communities recover from disturbance remain major challenges in ecology. To predict recovery of coral communities we applied field survey data of early recovery dynamics to a multi-species integral projection model that captured key demographic processes driving coral population trajectories, notably density-dependent larval recruitment. After testing model predictions against field observations, we updated the model to generate projections of future coral communities. Our results indicated that communities distributed across an island landscape followed different recovery trajectories but would reassemble to pre-disturbed levels of coral abundance, composition, and size, thus demonstrating persistence in the provision of reef habitat and other ecosystem services. Our study indicates that coral community dynamics are predictable when accounting for the interplay between species life-history, environmental conditions, and density-dependence. We provide a quantitative framework for evaluating the ecological processes underlying community trajectory and characteristics important to ecosystem functioning.

Modeling Adaptive and Nonadaptive Responses of Populations to Environmental Change.

Understanding how the natural world will be impacted by environmental change over the coming decades is one of the most pressing challenges facing humanity. Addressing this challenge is difficult because environmental change can generate both population-level plastic and evolutionary responses, with plastic responses being either adaptive or nonadaptive. We develop an approach that links quantitative genetic theory with data-driven structured models to allow prediction of population responses to environmental change via plasticity and adaptive evolution. After introducing general new theory, we construct a number of example models to demonstrate that evolutionary responses to environmental change over the short-term will be considerably slower than plastic responses and that the rate of adaptive evolution to a new environment depends on whether plastic responses are adaptive or nonadaptive. Parameterization of the models we develop requires information on genetic and phenotypic variation and demography that will not always be available, meaning that simpler models will often be required to predict responses to environmental change. We consequently develop a method to examine whether the full machinery of the evolutionarily explicit models we develop will be needed to predict responses to environmental change or whether simpler nonevolutionary models that are now widely constructed may be sufficient.

Some directions in ecological theory.

The role of theory within ecology has changed dramatically in recent decades. Once primarily a source of qualitative conceptual framing, ecological theories and models are now often used to develop quantitative explanations of empirical patterns and to project future dynamics of specific ecological systems. In this essay, I recount my own experience of this transformation, in which accelerating computing power and the widespread incorporation of stochastic processes into ecological theory combined to create some novel integration of mathematical and statistical models. This stronger integration drives theory towards incorporating more biological realism, and I explore ways in which we can grapple with that realism to generate new general theoretical insights. This enhanced realism, in turn, may lead to frameworks for projecting ecological responses to anthropogenic change, which is, arguably, the central challenge for 21st-century ecology. In an era of big data and synthesis, ecologists are increasingly seeking to infer causality from observational data; but conventional biometry provides few tools for this project. This is a realm where theorists can and should play an important role, and I close by pointing towards some analytical and philosophical approaches developed in our sister discipline of economics that address this very problem. While I make no grand prognostications about the likely discoveries of ecological theory over the coming century, you will find in this essay a scattering of more or less far-fetched ideas that I, at least, think are interesting and (possibly) fruitful directions for our field.

Fishery management priorities vary with self­recruitment in sedentary marine populations.

Fisheries science often uses population models that assume no external recruitment, but nearshore marine populations harvested on small scales of <200 km often exhibit an unknown mix of self-recruitment and recruitment from external sources. Since empirical determination of self-recruitment vs. external recruitment is difficult, we used a modeling approach to examine the sensitivity of fishery management priorities to recruitment assumptions (self [closed], external [open]) in a local population of harvested giant clams (Tridacna maxima) on Mo'orea, French Polynesia. From 2006 to 2010, we measured growth, fecundity, recruitment, and survival (resulting from natural and fishing mortality). We used these data to parameterize both a closed (complete self-recruitment) and an open (no self-recruitment) integral projection model (IPM), and then calculated elasticities of demographic rates (growth, survival, recruitment) to future population abundance in 20 years. The models' lowest projected abundance was 93.4% (95% CI, [86.5%, 101.8%]) of present abundance, if the local population is entirely open and the present level of fishing mortality persists. The population will exhibit self-sustaining dynamics (1 ≤ λ ≤ 1.07) as for a closed population if the ratio of self-recruits per gram of dry gonad is >0.775 (equivalent to 52.85% self-recruitment under present conditions). Elasticity analysis of demographic parameters indicated that future abundance can most effectively be influenced by increasing survival of mid-sized clams (∼80­120 mm) if the population is self-sustaining, and by increasing survival of juvenile clams (∼40­70 mm) if the population is non-self-sustaining (as for an open population). Our results illustrate that management priorities can vary depending on the amount of self-recruitment in a local population.

Consequences of dispersal heterogeneity for population spread and persistence.

Dispersal heterogeneity is increasingly being observed in ecological populations and has long been suspected as an explanation for observations of non-Gaussian dispersal. Recent empirical and theoretical studies have begun to confirm this. Using an integro-difference model, we allow an individual's diffusivity to be drawn from a trait distribution and derive a general relationship between the dispersal kernel's moments and those of the underlying heterogeneous trait distribution. We show that dispersal heterogeneity causes dispersal kernels to appear leptokurtic, increases the population's spread rate, and lowers the critical reproductive rate required for persistence in the face of advection. Wavespeed has been shown previously to be determined largely by the form of the dispersal kernel tail. We qualify this by showing that when reproduction is low, the precise shape of the tail is less important than the first few dispersal moments such as variance and kurtosis. If the reproductive rate is large, a dispersal kernel's asymptotic tail has a greater influence over wavespeed, implying that estimating the prevalence of traits which correlate with long-range dispersal is critical. The presence of multiple dispersal behaviors has previously been characterized in terms of long-range versus short-range dispersal, and it has been found that rare long-range dispersal essentially determines wavespeed. We discuss this finding and place it within a general context of dispersal heterogeneity showing that the dispersal behavior with the highest average dispersal distance does not always determine wavespeed.

Identifying critical regions in small-world marine metapopulations.

The precarious state of many nearshore marine ecosystems has prompted the use of marine protected areas as a tool for management and conservation. However, there remains substantial debate over their design and, in particular, how to best account for the spatial dynamics of nearshore marine species. Many commercially important nearshore marine species are sedentary as adults, with limited home ranges. It is as larvae that they disperse greater distances, traveling with ocean currents sometimes hundreds of kilometers. As a result, these species exist in spatially complex systems of connected subpopulations. Here, we explicitly account for the mutual dependence of subpopulations and approach protected area design in terms of network robustness. Our goal is to characterize the topology of nearshore metapopulation networks and their response to perturbation, and to identify critical subpopulations whose protection would reduce the risk for stock collapse. We define metapopulation networks using realistic estimates of larval dispersal generated from ocean circulation simulations and spatially explicit metapopulation models, and we then explore their robustness using node-removal simulation experiments. Nearshore metapopulations show small-world network properties, and we identify a set of highly connected hub subpopulations whose removal maximally disrupts the metapopulation network. Protecting these subpopulations reduces the risk for systemic failure and stock collapse. Our focus on catastrophe avoidance provides a unique perspective for spatial marine planning and the design of marine protected areas.

Integrated transcriptome and cell phenotype analysis suggest involvement of PARP1 cleavage, Hippo/Wnt, TGF-ß and MAPK signaling pathways in ovarian cancer cells response to cannabis and PARP1 inhibitor treatment.

Introduction: Cannabis sativa is utilized mainly for palliative care worldwide. Ovarian cancer (OC) is a lethal gynecologic cancer. A particular cannabis extract fraction ('F7') and the Poly(ADP-Ribose) Polymerase 1 (PARP1) inhibitor niraparib act synergistically to promote OC cell apoptosis. Here we identified genetic pathways that are altered by the synergistic treatment in OC cell lines Caov3 and OVCAR3. Materials and methods: Gene expression profiles were determined by RNA sequencing and quantitative PCR. Microscopy was used to determine actin arrangement, a scratch assay to determine cell migration and flow cytometry to determine apoptosis, cell cycle and aldehyde dehydrogenase (ALDH) activity. Western blotting was used to determine protein levels. Results: Gene expression results suggested variations in gene expression between the two cell lines examined. Multiple genetic pathways, including Hippo/Wnt, TGF-ß/Activin and MAPK were enriched with genes differentially expressed by niraparib and/or F7 treatments in both cell lines. Niraparib + F7 treatment led to cell cycle arrest and endoplasmic reticulum (ER) stress, inhibited cell migration, reduced the % of ALDH positive cells in the population and enhanced PARP1 cleavage. Conclusion: The synergistic effect of the niraparib + F7 may result from the treatment affecting multiple genetic pathways involving cell death and reducing mesenchymal characteristics.

Synchrony in dynamics of giant kelp forests is driven by both local recruitment and regional environmental controls.

Populations of many species display spatially synchronous fluctuations in abundance. Synchrony is most commonly attributed to three processes: factors that influence recruitment (e.g., dispersal, early survival), large-scale environmental variability, and spatially autocorrelated trophic interactions. However it is often difficult to link population synchrony to a specific dominant process, particularly when multiple synchronizing forces are operating. We utilized a new satellite-based data set of giant kelp (Macrocystis pyrifera) canopy biomass to examine population synchrony in southern California kelp forests on spatial scales ranging from 50 m to 300 km and temporal scales ranging from 1 to 11 years. We examined the relationship between synchrony and distance for adult kelp populations, kelp recruits, sea urchin abundance (a major grazer of kelp), and environmental variables known to influence kelp population dynamics. Population synchrony in giant kelp decreased with distance between populations: an initial rapid exponential decrease between 50 m and 1.3 km was followed by a second, large-scale decrease between distances of 1.3 km and 172 km. The 50-m to 1.3-km spatial scale corresponded to the scales of synchrony in the abundance of sea urchins and young kelp recruits, suggesting that local drivers of predation and recruitment influence small-scale synchrony in kelp populations. The spatial correlation patterns of environmental variables, particularly wave height, were similar to the synchrony-distance relationship of kelp populations from 1.3 km to 172 km, suggesting that regional environmental variability, i.e., the Moran effect, was the dominant process affecting synchrony at larger spatial scales. This two-step pattern in the relationship between kelp biomass synchrony and distance was apparent in each of the 11 years of our study. Our results highlight the potential for synthesizing approaches from both landscape and population ecology in order to identify the multiple processes that generate synchrony in population dynamics.

The value of coordinated management of interacting ecosystem services.

Coordinating decisions and actions among interacting sectors is a critical component of ecosystem-based management, but uncertainty about coordinated management's effects is compromising its perceived value and use. We constructed an analytical framework for explicitly calculating how coordination affects management decisions, ecosystem state and the provision of ecosystem services in relation to ecosystem dynamics and socio-economic objectives. The central insight is that the appropriate comparison strategy to optimal coordinated management is optimal uncoordinated management, which can be identified at the game theoretic Nash equilibrium. Using this insight we can calculate coordination's effects in relation to uncoordinated management and other reference scenarios. To illustrate how this framework can help identify ecosystem and socio-economic conditions under which coordination is most influential and valuable, we applied it to a heuristic case study and a simulation model for the California Current Marine Ecosystem. Results indicate that coordinated management can more than double an ecosystem's societal value, especially when sectors can effectively manipulate resources that interact strongly. However, societal gains from coordination will need to be reconciled with observations that it also leads to strategic simplification of the ecological food web, and generates both positive and negative impacts on individual sectors and non-target species.

Changing seascapes, stochastic connectivity, and marine metapopulation dynamics.

The probability of dispersal from one habitat patch to another is a key quantity in our efforts to understand and predict the dynamics of natural populations. Unfortunately, an often overlooked property of this potential connectivity is that it may change with time. In the marine realm, transient landscape features, such as mesoscale eddies and alongshore jets, produce potential connectivity that is highly variable in time. We assess the impact of this temporal variability by comparing simulations of nearshore metapopulation dynamics when potential connectivity is constant through time (i.e., when it is deterministic) and when it varies in time (i.e., when it is stochastic). We use mathematical analysis to reach general conclusions and realistic biophysical modeling to determine the actual magnitude of these changes for a specific system: nearshore marine species in the Southern California Bight. We find that in general the temporal variability of potential connectivity affects two important quantities: metapopulation growth rates when the species is rare and equilibrium abundances. Our biophysical models reveal that stochastic outcomes are almost always lower than their deterministic counterparts, sometimes by up to 40%. This has implications for how we use spatial information, such as connectivity, to manage nearshore (and other) systems.

Demographic heterogeneity, cohort selection, and population growth.

Demographic heterogeneity--variation among individuals in survival and reproduction--is ubiquitous in natural populations. Structured population models address heterogeneity due to age, size, or major developmental stages. However, other important sources of demographic heterogeneity, such as genetic variation, spatial heterogeneity in the environment, maternal effects, and differential exposure to stressors, are often not easily measured and hence are modeled as stochasticity. Recent research has elucidated the role of demographic heterogeneity in changing the magnitude of demographic stochasticity in small populations. Here we demonstrate a previously unrecognized effect: heterogeneous survival in long-lived species can increase the long-term growth rate in populations of any size. We illustrate this result using simple models in which each individual's annual survival rate is independent of age but survival may differ among individuals within a cohort. Similar models, but with nonoverlapping generations, have been extensively studied by demographers, who showed that, because the more "frail" individuals are more likely to die at a young age, the average survival rate of the cohort increases with age. Within ecology and evolution, this phenomenon of "cohort selection" is increasingly appreciated as a confounding factor in studies of senescence. We show that, when placed in a population model with overlapping generations, this heterogeneity also causes the asymptotic population growth rate lambda to increase, relative to a homogeneous population with the same mean survival rate at birth. The increase occurs because, even integrating over all the cohorts in the population, the population becomes increasingly dominated by the more robust individuals. The growth rate increases monotonically with the variance in survival rates, and the effect can be substantial, easily doubling the growth rate of slow-growing populations. Correlations between parent and offspring phenotype change the magnitude of the increase in lambda, but the increase occurs even for negative parent-offspring correlations. The effect of heterogeneity in reproductive rate on lambda is quite different: growth rate increases with reproductive heterogeneity for positive parent-offspring correlation but decreases for negative parent-offspring correlation. These effects of demographic heterogeneity on lambda have important implications for population dynamics, population viability analysis, and evolution.

Variability in population abundance and the classification of extinction risk.

Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50-80%; vulnerable, decline in abundance of 30-50%; least concern or near threatened, decline in abundance of 0-30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state-space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state-space models were low (maximum 3-14%), but 33-75% of small declines (30-50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.

Analyzing variability and the rate of decline of migratory shorebirds in Moreton Bay, Australia.

Estimating the abundance of migratory species is difficult because sources of variability differ substantially among species and populations. Recently developed state-space models address this variability issue by directly modeling both environmental and measurement error, although their efficacy in detecting declines is relatively untested for empirical data. We applied state-space modeling, generalized least squares (with autoregression error structure), and standard linear regression to data on abundance of wetland birds (shorebirds and terns) at Moreton Bay in southeast Queensland, Australia. There are internationally significant numbers of 8 species of waterbirds in the bay, and it is a major terminus of the large East Asian-Australasian Flyway. In our analyses, we considered 22 migrant and 8 resident species. State-space models identified abundances of 7 species of migrants as significantly declining and abundance of one species as significantly increasing. Declines in migrant abundance over 15 years were 43-79%. Generalized least squares with an autoregressive error structure showed abundance changes in 11 species, and standard linear regression showed abundance changes in 15 species. The higher power of the regression models meant they detected more declines, but they also were associated with a higher rate of false detections. If the declines in Moreton Bay are consistent with trends from other sites across the flyway as a whole, then a large number of species are in significant decline.

Turbulent dispersal promotes species coexistence.

Several recent advances in coexistence theory emphasize the importance of space and dispersal, but focus on average dispersal rates and require spatial heterogeneity, spatio-temporal variability or dispersal-competition tradeoffs to allow coexistence. We analyse a model with stochastic juvenile dispersal (driven by turbulent flow in the coastal ocean) and show that a low-productivity species can coexist with a high-productivity species by having dispersal patterns sufficiently uncorrelated from those of its competitor, even though, on average, dispersal statistics are identical and subsequent demography and competition is spatially homogeneous. This produces a spatial storage effect, with an ephemeral partitioning of a 'spatial niche', and is the first demonstration of a physical mechanism for a pure spatiotemporal environmental response. 'Turbulent coexistence' is widely applicable to marine species with pelagic larval dispersal and relatively sessile adult life stages (and perhaps some wind-dispersed species) and complements other spatial and temporal storage effects previously documented for such species.

A stochastic model for annual reproductive success.

Demographic stochasticity can have large effects on the dynamics of small populations as well as on the persistence of rare genotypes and lineages. Survival is sensibly modeled as a binomial process, but annual reproductive success (ARS) is more complex and general models for demographic stochasticity do not exist. Here we introduce a stochastic model framework for ARS and illustrate some of its properties. We model a sequence of stochastic events: nest completion, the number of eggs or neonates produced, nest predation, and the survival of individual offspring to independence. We also allow multiple nesting attempts within a breeding season. Most of these components can be described by Bernoulli or binomial processes; the exception is the distribution of offspring number. Using clutch and litter size distributions from 53 vertebrate species, we demonstrate that among-individual variability in offspring number can usually be described by the generalized Poisson distribution. Our model framework allows the demographic variance to be calculated from underlying biological processes and can easily be linked to models of environmental stochasticity or selection because of its parametric structure. In addition, it reveals that the distributions of ARS are often multimodal and skewed, with implications for extinction risk and evolution in small populations.

Locating gaps in the California Current System ocean acidification monitoring network.

Ocean acidification is a global issue with particular regional significance in the California Current System, where social, economic, and ecological impacts are already occurring. Although ocean acidification is a concern that unifies the entire West Coast region, managing for this phenomenon at a regional scale is complex and further complicated by the large scale and dynamic nature of the region. Currently, data collection relevant to ocean acidification on the West Coast is piecemeal, and cannot capture the primary sources of variability in ocean acidification through time and across the region, hindering collaboration among regional managers. We developed a tool to analyze gaps in the West Coast ocean acidification monitoring network. We describe this tool and discuss how it can enable scientists and marine managers in the California Current System to fill information gaps and better understand and thus respond to ocean acidification through the implementation of management solutions at the local level.

Analogies for a No-Analog World: Tackling Uncertainties in Reintroduction Planning.

Species reintroductions involve considerable uncertainty, especially in highly altered landscapes. Historical, geographic, and taxonomic analogies can help reduce this uncertainty by enabling conservationists to better assess habitat suitability in proposed reintroduction sites. We illustrate this approach using the example of the California grizzly, an iconic species proposed for reintroduction.

Marine reserve effects on fishery profit.

Some studies suggest that fishery yields can be higher with reserves than under conventional management. However, the economic performance of fisheries depends on economic profit, not fish yield. The predictions of higher yields with reserves rely on intensive fishing pressures between reserves; the exorbitant costs of harvesting low-density populations erode profits. We incorporated this effect into a bioeconomic model to evaluate the economic performance of reserve-based management. Our results indicate that reserves can still benefit fisheries, even those targeting species that are expensive to harvest. However, in contrast to studies focused on yield, only a moderate proportion of the coast in reserves (with moderate harvest pressures outside reserves) is required to maximize profit. Furthermore, reserve area and harvest intensity can be traded off with little impact on profits, allowing for management flexibility while still providing higher profit than attainable under conventional management.