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.

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.

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.

Rapid population decline in migratory shorebirds relying on Yellow Sea tidal mudflats as stopover sites.

Migratory animals are threatened by human-induced global change. However, little is known about how stopover habitat, essential for refuelling during migration, affects the population dynamics of migratory species. Using 20 years of continent-wide citizen science data, we assess population trends of ten shorebird taxa that refuel on Yellow Sea tidal mudflats, a threatened ecosystem that has shrunk by >65% in recent decades. Seven of the taxa declined at rates of up to 8% per year. Taxa with the greatest reliance on the Yellow Sea as a stopover site showed the greatest declines, whereas those that stop primarily in other regions had slowly declining or stable populations. Decline rate was unaffected by shared evolutionary history among taxa and was not predicted by migration distance, breeding range size, non-breeding location, generation time or body size. These results suggest that changes in stopover habitat can severely limit migratory populations.

Resetting predator baselines in coral reef ecosystems.

What did coral reef ecosystems look like before human impacts became pervasive? Early efforts to reconstruct baselines resulted in the controversial suggestion that pristine coral reefs have inverted trophic pyramids, with disproportionally large top predator biomass. The validity of the coral reef inverted trophic pyramid has been questioned, but until now, was not resolved empirically. We use data from an eight-year tag-recapture program with spatially explicit, capture-recapture models to re-examine the population size and density of a key top predator at Palmyra atoll, the same location that inspired the idea of inverted trophic biomass pyramids in coral reef ecosystems. Given that animal movement is suspected to have significantly biased early biomass estimates of highly mobile top predators, we focused our reassessment on the most mobile and most abundant predator at Palmyra, the grey reef shark (Carcharhinus amblyrhynchos). We estimated a density of 21.3 (95% CI 17.8, 24.7) grey reef sharks/km2, which is an order of magnitude lower than the estimates that suggested an inverted trophic pyramid. Our results indicate that the trophic structure of an unexploited reef fish community is not inverted, and that even healthy top predator populations may be considerably smaller, and more precarious, than previously thought.

Growth and life history variability of the grey reef shark (Carcharhinus amblyrhynchos) across its range.

For broadly distributed, often overexploited species such as elasmobranchs (sharks and rays), conservation management would benefit from understanding how life history traits change in response to local environmental and ecological factors. However, fishing obfuscates this objective by causing complex and often mixed effects on the life histories of target species. Disentangling the many drivers of life history variability requires knowledge of elasmobranch populations in the absence of fishing, which is rarely available. Here, we describe the growth, maximum size, sex ratios, size at maturity, and offer a direct estimate of survival of an unfished population of grey reef sharks (Carcharhinus amblyrhynchos) using data from an eight year tag-recapture study. We then synthesized published information on the life history of C. amblyrhynchos from across its geographic range, and for the first time, we attempted to disentangle the contribution of fishing from geographic variation in an elasmobranch species. For Palmyra's unfished C. amblyrhynchos population, the von Bertalanffy growth function (VBGF) growth coefficient k was 0.05 and asymptotic length L∞ was 163.3 cm total length (TL). Maximum size was 175.5 cm TL from a female shark, length at maturity was estimated at 116.7-123.2 cm TL for male sharks, maximum lifespan estimated from VBGF parameters was 18.1 years for both sexes combined, and annual survival was 0.74 year-1. Consistent with findings from studies on other elasmobranch species, we found significant intraspecific variability in reported life history traits of C. amblyrhynchos. However, contrary to what others have reported, we did not find consistent patterns in life history variability as a function of biogeography or fishing. Ultimately, the substantial, but not yet predictable variability in life history traits observed for C. amblyrhynchos across its geographic range suggests that regional management may be necessary to set sustainable harvest targets and to recover this and other shark species globally.

Rapid evolution accelerates plant population spread in fragmented experimental landscapes.

Predicting the speed of biological invasions and native species migrations requires an understanding of the ecological and evolutionary dynamics of spreading populations. Theory predicts that evolution can accelerate species' spread velocity, but how landscape patchiness--an important control over traits under selection--influences this process is unknown. We manipulated the response to selection in populations of a model plant species spreading through replicated experimental landscapes of varying patchiness. After six generations of change, evolving populations spread 11% farther than nonevolving populations in continuously favorable landscapes and 200% farther in the most fragmented landscapes. The greater effect of evolution on spread in patchier landscapes was consistent with the evolution of dispersal and competitive ability. Accounting for evolutionary change may be critical when predicting the velocity of range expansions.

Impacts of sea level rise and climate change on coastal plant species in the central California coast.

Local increases in sea level caused by global climate change pose a significant threat to the persistence of many coastal plant species through exacerbating inundation, flooding, and erosion. In addition to sea level rise (SLR), climate changes in the form of air temperature and precipitation regimes will also alter habitats of coastal plant species. Although numerous studies have analyzed the effect of climate change on future habitats through species distribution models (SDMs), none have incorporated the threat of exposure to SLR. We developed a model that quantified the effect of both SLR and climate change on habitat for 88 rare coastal plant species in San Luis Obispo, Santa Barbara, and Ventura Counties, California, USA (an area of 23,948 km(2)). Our SLR model projects that by the year 2100, 60 of the 88 species will be threatened by SLR. We found that the probability of being threatened by SLR strongly correlates with a species' area, elevation, and distance from the coast, and that 10 species could lose their entire current habitat in the study region. We modeled the habitat suitability of these 10 species under future climate using a species distribution model (SDM). Our SDM projects that 4 of the 10 species will lose all suitable current habitats in the region as a result of climate change. While SLR accounts for up to 9.2 km(2) loss in habitat, climate change accounts for habitat suitability changes ranging from a loss of 1,439 km(2) for one species to a gain of 9,795 km(2) for another species. For three species, SLR is projected to reduce future suitable area by as much as 28% of total area. This suggests that while SLR poses a higher risk, climate changes in precipitation and air temperature represents a lesser known but potentially larger risk and a small cumulative effect from both.

Estimating relative risk of within-lake aquatic plant invasion using combined measures of recreational boater movement and habitat suitability.

Effective monitoring, prevention and impact mitigation of nonindigenous aquatic species relies upon the ability to predict dispersal pathways and receiving habitats with the greatest risk of establishment. To examine mechanisms affecting species establishment within a large lake, we combined observations of recreational boater movements with empirical measurements of habitat suitability represented by nearshore wave energy to assess the relative risk of Eurasian watermilfoil (Myriophyllum spicatum) establishment. The model was evaluated using information from a 17 year (1995-2012) sequence of M. spicatum presence and absence monitoring. M. spicatum presence was not specifically correlated with recreational boater movements; however its establishment appears to be limited by wave action in Lake Tahoe. Of the sites in the "High" establishment risk category (n = 37), 54% had current or historical infestations, which included 8 of the 10 sites with the highest relative risk. Of the 11 sites in the "Medium" establishment risk category, 5 had current or historical M. spicatum populations. Most (76%) of the sites in the "Low" establishment risk category were observed in locations with higher wave action. Four sites that received zero boater visits from infested locations were occupied by M. spicatum. This suggests that the boater survey either represents incomplete coverage of boater movement, or other processes, such as the movement of propagules by surface currents or introductions from external sources are important to the establishment of this species. This study showed the combination of habitat specific and dispersal data in a relative risk framework can potentially reduce uncertainty in estimates of invasion risk.

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.

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.

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.

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.

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.

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.

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.