Incorporate Density Dependence Research Articles

The importance of negative density dependence for rare species persistence

Determining the predictors of extinction risk is a major goal of conservation biology. Theory suggests that population characteristics such as low abundance and declining trends should equate to high extinction risk. However, rare species persist and account for nearly half of all species across global communities. Here we employ the Chicago Botanic Garden's long-term, rare species monitoring dataset to investigate population dynamics and hypothesized predictors of extinction risk of 73 populations of 43 rare species. Specifically, we ask how important negative density dependence is for rare species persistence and how well population size, population trends, life cycle duration, and clonality predict estimates of extinction risk in rare species. Extinction risk was estimated using density-dependent and density-independent population viability analyses. Based on our simulations, we found that only 33 of our populations had concerning extinction risks (>20 %). The key to these relatively low extinction risks for so many rare, listed species was negative density dependence. Population size, population trends, life cycle duration, and clonality were not good predictors of extinction risk based on our modeling efforts, though their relationships to extinction risk did agree with theoretical expectations. Our results highlight the potential importance of negative density dependence for rare species persistence and the importance of incorporating density dependence in population projection models for extinction risk assessment.

Ipmr: Flexible implementation of Integral Projection Models in R

Abstract Integral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, body mass). Researchers use IPMs to investigate questions ranging from linking drivers to population dynamics, planning conservation and management strategies, and quantifying selective pressures in natural populations. The popularity of stage‐structured population models has been supported by R scripts and packages (e.g. IPMpack, popbio, popdemo, lefko3) aimed at ecologists, which have introduced a broad repertoire of functionality and outputs. However, pressing ecological, evolutionary and conservation biology topics require developing more complex IPMs, and considerably more expertise to implement them. Here, we introduce ipmr, a flexible R package for building, analysing and interpreting IPMs. The ipmr framework relies on the mathematical notation of the models to express them in code format. Additionally, this package decouples the model parameterization step from the model implementation step. The latter point substantially increases ipmr's flexibility to model complex life cycles and demographic processes. ipmr can handle a wide variety of models, including those that incorporate density dependence, discretely and continuously varying stochastic environments, and multiple continuous and/or discrete traits. ipmr can accommodate models with individuals cross‐classified by age and size. Furthermore, the package provides methods for demographic analyses (e.g. asymptotic and stochastic growth rates) and visualization (e.g. kernel plotting). ipmr is a flexible R package for integral projection models. The package substantially reduces the amount of time required to implement general IPMs. We also provide extensive documentation with six vignettes and help files, accessible from an R session and online.

The importance of density dependence in juvenile mosquito development and survival: A model-based investigation

Mosquitoes are vectors of numerous pathogens that cause infectious diseases, and they pose a significant global health burden as a result. As such, more reliable field-relevant models to study mosquito population dynamics and life history traits such as development time and survival of mosquito larva would be of great value. In Aedes mosquitoes, progression through early life stages is known to be density-dependent. Despite its importance, density dependence is largely ignored or oversimplified in many existing simulation models, leading to less accurate predictions of development and survival during the early life stages. Furthermore, density dependence is frequently assumed to impact only larval survival and not development time in models, despite empirical evidence for density-dependent development. Here, we develop a discrete-time model of mosquito larval population dynamics which accounts for density impacts on both survival and development time. We demonstrate the validity of our model using publicly available semi-field data of larval density and pupation time across a six-month experiment. Using our model, we found that incorporating density dependence during larval development is important to the accurate prediction of mosquito pupation. This is especially true when considering density-dependent development time for mosquito larva as opposed to density-dependent larval survival. We determined that the incorporation of simple functional forms to describe density dependence in simulation models gives improved prediction results over models that ignore density dependence entirely. Such simple functional forms can easily be incorporated into existing models, and thus help improve field-relevant models of mosquito population dynamics, particularly in Aedes and other container-inhabiting mosquitoes that are known to experience density dependence during larval development.

Accounting for the temporal variation of spatial effect improves inference and projection of population dynamics models

Population dynamics models incorporating density dependence and habitat heterogeneity are useful tools to explain and project the spatiotemporal variation of wildlife abundance. Despite their wide application in ecology and conservation biology, the inference and projection of these models may be problematic when residual spatial autocorrelation (SAC) is found. We aimed to improve the inference and projection of population dynamics models by accounting for residual SAC. We considered three Gompertz models that incorporated density dependence and the effect of wetland habitat to explain and project the abundance of Mallard (Anas platyrhynchos). We compared a conventional model that did not account for residual SAC (ENV) with two novel models accounting for residual SAC, one incorporating a spatial effect (a spatially autocorrelated process error) that did not vary over time (STA) and the other incorporating a spatial effect that varied over time (DYN). We evaluated model inference using data from 1974 to 1998 and projection using data from 1999 to 2010. We then forecasted Mallard abundance from 2011 to 2100 under different levels of wetland habitat loss. The DYN model eliminated residual SAC and had better model fit than the ENV and STA models (ΔD¯=2498.3 and 1988.8, respectively). The projection coverage rate of the DYN model was the closest to the nominal value among the three models. The DYN model forecasted smaller areas with decrease in Mallard abundance under future wetland habitat loss than the ENV and STA models. The novel and conventional population dynamics models we considered in this study, combined with the practical model evaluation approach, can provide reliable inference and projection of wildlife abundance, and thus have wide application in ecological studies and conservation practices that aim to understand and project the spatiotemporal variation of wildlife abundance under environmental changes. In particular, when conservation decision-making is based on model projections, the DYN may be used to minimize the risk of reducing conservation effort in areas that still have high conservation value, due to its favorable projection performance.

Density dependence in demography and dispersal generates fluctuating invasion speeds

Density dependence plays an important role in population regulation and is known to generate temporal fluctuations in population density. However, the ways in which density dependence affects spatial population processes, such as species invasions, are less understood. Although classical ecological theory suggests that invasions should advance at a constant speed, empirical work is illuminating the highly variable nature of biological invasions, which often exhibit nonconstant spreading speeds, even in simple, controlled settings. Here, we explore endogenous density dependence as a mechanism for inducing variability in biological invasions with a set of population models that incorporate density dependence in demographic and dispersal parameters. We show that density dependence in demography at low population densities-i.e., an Allee effect-combined with spatiotemporal variability in population density behind the invasion front can produce fluctuations in spreading speed. The density fluctuations behind the front can arise from either overcompensatory population growth or density-dependent dispersal, both of which are common in nature. Our results show that simple rules can generate complex spread dynamics and highlight a source of variability in biological invasions that may aid in ecological forecasting.

Biomass prediction using a density-dependent diameter distribution model

Prediction of aboveground biomass, particularly at large spatial scales, is necessary for estimating global-scale carbon sequestration. Since biomass can be measured only by sacrificing trees, total biomass on plots is never observed. Rather, allometric equations are used to convert individual tree diameter to individual biomass, perhaps with noise. The values for all trees on a plot are then summed to obtain a derived total biomass for the plot. Then, with derived total biomasses for a collection of plots, regression models, using appropriate environmental covariates, are employed to attempt explanation and prediction. Not surprisingly, when out-of-sample validation is examined, such a model will predict total biomass well for holdout data because it is obtained using exactly the same derived approach. Apart from the somewhat circular nature of the regression approach, it also fails to employ the actual observed plot level response data. At each plot, we observe a random number of trees, each with an associated diameter, producing a sample of diameters. A model based on this random number of tree diameters provides understanding of how environmental regressors explain abundance of individuals, which in turn explains individual diameters. We incorporate density dependence because the distribution of tree diameters over a plot of fixed size depends upon the number of trees on the plot. After fitting this model, we can obtain predictive distributions for individual-level biomass and plot-level total biomass. We show that predictive distributions for plot-level biomass obtained from a density-dependent model for diameters will be much different from predictive distributions using the regression approach. Moreover, they can be more informative for capturing uncertainty than those obtained from modeling derived plot-level biomass directly. We develop a density-dependent diameter distribution model and illustrate with data from the national Forest Inventory and Analysis (FIA) database. We also describe how to scale predictions to larger spatial regions. Our predictions agree (in magnitude) with available wisdom on mean and variation in biomass at the hectare scale.

Density-dependent state-space model for population-abundance data with unequal time intervals.

The Gompertz state-space (GSS) model is a stochastic model for analyzing time-series observations of population abundances. The GSS model combines density dependence, environmental process noise, and observation error toward estimating quantities of interest in biological monitoring and population viability analysis. However, existing methods for estimating the model parameters apply only to population data with equal time intervals between observations. In the present paper, we extend the GSS model to data with unequal time intervals, by embedding it within a state-space version of the Ornstein-Uhlenbeck process, a continuous-time model of an equilibrating stochastic system. Maximum likelihood and restricted maximum likelihood calculations for the Ornstein-Uhlenbeck state-space model involve only numerical maximization of an explicit multivariate normal likelihood, and so the extension allows for easy bootstrapping, yielding confidence intervals for model parameters, statistical hypothesis testing of density dependence, and selection among sub-models using information criteria. Ecologists and managers previously drawn to models lacking density dependence or observation error because such models accommodated unequal time intervals (for example, due to missing data) now have an alternative analysis framework incorporating density dependence, process noise, and observation error.

Determinants of the population growth of the West Nile virus mosquito vector Culex pipiens in a repeatedly affected area in Italy

BackgroundThe recent spread of West Nile Virus in temperate countries has raised concern. Predicting the likelihood of transmission is crucial to ascertain the threat to Public and Veterinary Health. However, accurate models of West Nile Virus (WNV) expansion in Europe may be hampered by limited understanding of the population dynamics of their primary mosquito vectors and their response to environmental changes.MethodsWe used data collected in north-eastern Italy (2009–2011) to analyze the determinants of the population growth rate of the primary WNV vector Culex pipiens. A series of alternative growth models were fitted to longitudinal data on mosquito abundance to evaluate the strength of evidence for regulation by intrinsic density-dependent and/or extrinsic environmental factors. Model-averaging algorithms were then used to estimate the relative importance of intrinsic and extrinsic variables in describing the variations of per-capita growth rates.ResultsResults indicate a much greater contribution of density-dependence in regulating vector population growth rates than of any environmental factor on its own. Analysis of an average model of Cx. pipiens growth revealed that the most significant predictors of their population dynamics was the length of daylight, estimated population size and temperature conditions in the 15 day period prior to sampling. Other extrinsic variables (including measures of precipitation, number of rainy days, and humidity) had only a minor influence on Cx. pipiens growth rates.ConclusionsThese results indicate the need to incorporate density dependence in combination with key environmental factors for robust prediction of Cx. pipiens population expansion and WNV transmission risk. We hypothesize that detailed analysis of the determinants of mosquito vector growth rate as conducted here can help identify when and where an increase in vector population size and associated WNV transmission risk should be expected.

How entomological studies can help the control of mosquito-borne diseases: a five-years experience in north-eastern Italy

North-eastern Italy is particularly suitable for mosquito survival, due to its climate, landscape and abundance of wild/domestic animals. After the emergence of West Nile Virus (WNV) in 2008, entomological studies were implemented. Here we describe how entomological data were managed to optimize surveillance and control of mosquito-borne pathogens. CDC-CO2 traps were used (May-October, 2009-2013). In one site, captures with CDC and gravid traps were organized every 2hrs for 24hrs. In other three sites pre- and post-disinfestation captures were done. The mosquitoes were screened by RT-PCR for Flaviviridae and a sub-sample for Bunyaviridae. Host preference of Culex pipiens was assessed by PCR blood meal analysis. Mapping, modelling and spatial analyses were done using entomological data to identify correlations with climate, landscape, animal and human infections. More than 700,000 mosquitoes were collected, with Cx.pipiens the most abundant (80%) and the only vector of WNV and USUV. Tahyna virus was isolated once in Ochlerotatus caspius. Cx.pipiens fed preferentially on birds (76%), mainly blackbird, sparrow, magpie and collared dove, but not on humans. Diel activity showed that Cx.pipiens changed its host searching activity according to the season and defined the favorite period for oviposition. The success of disinfestation measures in reducing Cx.pipiens density varied according to the methods used. The contribution of density-dependence in regulating vector population growth resulted greater than any environmental factor on its own. Overall the most significant predictors of Cx.pipiens dynamics included length of daylight, population density and temperature in the 15 days prior to sampling. Precipitation, number of rainy days and humidity had limited importance. Linear models detected significant relationships between WNV in humans and mosquitoes. Spatial analysis detected clusters of WNV occurrences for all the hosts, identifying an area where to focus surveillance and promptly detect WNV re-activation. In long term studies the mosquito species composition, seasonality, distribution, abundance and pathogen rate of infections were assessed. The blood-meal analyses indicated possible bird targets for surveillance. The control of the efficacy of disinfestations highlighted the need for harmonic guidelines. Modelling indicated the need to incorporate density dependence in combination with key environmental factors for robust prediction of Cx.pipiens population expansion, helping to identify when and where an increase in vector population size and WNV transmission risk should be expected. The results stressed also the necessity to improve the density and the frequency of mosquito captures.

Incorporating density dependence in pup production in a stock assessment of NE Atlantic spurdog Squalus acanthias

Abstract De Oliveira, J. A. A., Ellis, J. R., and Dobby, H. 2013. Incorporating density dependence in pup production in a stock assessment of NE Atlantic spurdog Squalus acanthias. – ICES Journal of Marine Science, 70: . An age- and sex-structured stock assessment model for Northeast Atlantic spurdog Squalus acanthias is presented that includes length-based processes, such as maturation, pup production, growth, and gear selectivity, with a length-at-age relationship to convert length to age. It relates pup production functionally to numbers of pregnant females, allowing for density-dependent effects. The model was fitted to a combined Scottish groundfish survey biomass index, to proportion-by-length category data from both trawl surveys and commercial catch sampling from target and non-target fisheries, and to fecundity data. The model was run from 1905 to better reflect virgin conditions and to allow early fecundity data to be fitted in order to estimate the extent of density dependence in pup production. The model estimated 2010 population levels to be about 23% relative to 1955 and 19% relative to 1905. Results confirm that the stock is depleted, but not to the extent estimated in a previous assessment. Current estimates of depletion would support an IUCN listing of “Endangered”, but not “Critically Endangered”. Model projections showed that a TAC of 1422 t (the last non-zero TAC) would allow future population growth.

Density-and-temperature-dependent volume translation for the SRK EOS: 2. Mixtures

A density-and-temperature-dependent volume translation function, originally developed for pure component, cubic volumetric equations of states (EOSs) has been extended to mixtures and applied to the Soave–Redlich–Kwong cubic EOS. This density modified translation (DMT) function has been used to estimate mixture properties for a set of 18 representative fluids: water, carbon dioxide, ammonia, hydrogen sulfide, tetrafluoromethane, acetone, hydrogen, nitrogen, oxygen, argon, and the normal alkanes through octane. Accuracy in mixture molar volumes is substantially improved with respect to the accuracy achievable though consistent translation functions independent of density, but bubble and dew point pressures remain largely unaffected by the DMT function. Incorporating density dependence into the DMT function does affect estimated mixture equilibrium properties, but not in a manner that provides systematic improvement. Application of the DMT function specifically for estimating mixture densities provides the greatest benefit.

A Conceptional Analysis of Dynamics and Production in Bioeconomic Models

Harvesting functions and stock dynamic equations in age‐structured bioeconomic models are generalized in order to incorporate density dependence. Using this generalization, anything from completely uniformly distributed fish to extreme schooling can be analyzed. The classical Beverton–Holt model comes out as a special case of the generalized model. The generalization can be applied both for simulation as well as for optimization purposes given appropriate software. Conceptual analysis indicates that pulse fishing seems to become less and less economically profitable as we move from uniformly distributed fish to schooling species. This has important implications for how fish stocks ought to be managed.

Incorporating density dependence into the directed‐dispersal hypothesis

The directed-dispersal (DrD) hypothesis, one of the main explanations for the adaptive value of seed dispersal, asserts that enhanced (nonrandom) arrival to favorable establishment sites is advantageous for plant fitness. However, as anticipated by the ideal free distribution theory, enhanced seed deposition may impair site suitability by increasing density-dependent mortality, thus negating the advantage postulated by the DrD hypothesis. Although the role of density effects is thoroughly discussed in the seed-dispersal literature, this DrD paradox remains largely overlooked. The paradox, however, may be particularly pronounced in animal-mediated dispersal systems, in which DrD is relatively common, because animals tend to generate local seed aggregations due to their nonrandom movements. To investigate possible solutions to the DrD paradox, we first introduce a simple analytical model that calculates the optimal DrD level at which seed arrival to favorable establishment sites yields maximal fitness gain in comparison to a null model of random arrival. This model predicts intermediate optimal DrD levels that correspond to various attributes of the plants, the dispersers, and the habitat. We then use a simulation model to explore the temporal dynamics of the invasion process of the DrD strategy in a randomly dispersed population, and the resistance of a DrD population against invasion of other dispersal strategies. This model demonstrates that some properties of the invasion process (e.g., mutant persistence ratio in the population and generations until initial establishment) are facilitated by high DrD levels, and not by intermediate levels as expected from the analytical model. These results highlight the need to revise the DrD hypothesis to include the countering effects of density-dependent mortality inherently imposed by enhanced arrival of seeds to specific sites. We illustrate how the revised hypothesis can elucidate previous results from empirical studies reporting little or no support for the DrD hypothesis, and we suggest its incorporation in designing empirical studies of plant recruitment and in management practices.

Stock–Recruitment Dynamics and the Maximum Population Growth Rate of the Barndoor Skate on Georges Bank

AbstractIn 1998, the barndoor skateDipturus laeviswas reported to have been locally extirpated in parts of its northern range and to be potentially on the brink of extinction. Managers were faced with assessing the species with virtually no information other than a limited number of individuals observed in annual groundfish surveys. Since that time, a number of the primary life history parameters have been estimated, but the population dynamics of the species remain largely unexplored. In this study, we use information from the National Marine Fisheries Service (NMFS) annual groundfish surveys to investigate two critical components of barndoor skate population dynamics: the relationship of recruitment to spawner abundance and the maximum population growth rate. A strong stock–recruitment relationship was found in the fall survey data, suggesting that recruitment is closely tied to spawner abundance. The Ricker and Beverton–Holt stock–recruit models were fitted to the survey data, and estimates of the slope at the origin was generated. These parameters provided an estimate of the maximum annual reproductive rate, which was then converted to an estimate of the instantaneous maximum population growth rate of 0.37–0.38 per year. A second analysis was also conducted using a Leslie matrix and data from the NMFS survey. Observed rates of population change were used to estimate early life history parameters and incorporate density dependence into the density‐independent framework of a Leslie matrix demographic model. From this method, the instantaneous maximum population growth for the barndoor skate was estimated to be 0.36–0.48 per year. Our results suggest that the species is more resilient to fishing pressure than previously believed and is capable of growing at an instantaneous rate in excess of 35% at low population sizes.

Incorporating density dependence into the oviposition preference–offspring performance hypothesis

1. Although theory predicts a positive relationship between oviposition preferences and the developmental performance of offspring, the strength of this relationship may depend not only on breeding site quality, but also on the complex interactions between environmental heterogeneity and density-dependent processes. Environmental heterogeneity may not only alter the strength of density dependence, but may also fundamentally alter density-dependent relationships and the preference-performance relationship. 2. Here I present results from a series of field experiments testing the effects of environmental heterogeneity and density-dependent feedback on offspring performance in tree-hole mosquitoes. Specifically, I asked: (i) how do oviposition activity, patterns of colonization and larval density differ among habitats and among oviposition sites with different resources; and (ii) how is performance influenced by the density of conspecifics, the type of resource in the oviposition site, and the type of habitat in which the oviposition site is located? 3. Performance did not differ among habitats at low offspring densities, but was higher in deciduous forest habitats than in evergreen forest habitats at high densities. Oviposition activity and larval densities were also higher in deciduous forests, suggesting a weak preference for these habitats. 4. The observed divergence of fitness among habitats with increasing density may select for consistent but weak preferences for deciduous habitats if regional abundances vary temporally. This would generate a negative preference-performance relationship when population densities are low, but a positive relationship when population densities are high. 5. This study demonstrates that failure to recognize that fitness differences among habitats may themselves be density-dependent may bias our assumptions about the ecological and evolutionary processes determining oviposition preferences in natural systems.

Approaches for testing herbivore effects on plant population dynamics

Summary As plant invasions pose one of the greatest threats to biodiversity, it is critical to improve both our understanding of invasiveness and strategies for control. Much research into plant invasions and their management, including biological control, assumes strong demographic effects by natural enemies, including herbivores. However, the importance of natural enemies in the regulation of plant populations remains controversial: some ecologists contend that they rarely affect plant populations, and others that they can strongly limit plant population sizes. We briefly review the conflicting views and suggest that new approaches to gather and analyse data are needed before the effects of natural enemies on plant populations can be fully characterized. We outline experimental and analytical approaches that incorporate density dependence into population models and thus provide a more complete test of the long‐term effects of natural enemies on plant populations. We also introduce new methods for obtaining stochastic estimates of equilibrium density, which will provide a key test of enemy effects on plant population size. Synthesis and applications. Designing effective strategies for invasive plant management requires information about the factors that limit plant population size. Together, the experiments and analyses we describe measure more clearly how natural enemies influence plant population dynamics. They will provide an important tool in evaluating the role of enemy release in plant invasions and for predicting the potential success of biological control. Such information should help to prioritize strategies that are most likely to control invasive plants effectively and will contribute to risk assessment when considering the release of non‐native natural enemies as biological control agents.

Accounting for Management Costs in Sensitivity Analyses of Matrix Population Models

Traditional sensitivity and elasticity analyses of matrix population models have been used to inform management decisions, but they ignore the economic costs of manipulating vital rates. For example, the growth rate of a population is often most sensitive to changes in adult survival rate, but this does not mean that increasing that rate is the best option for managing the population because it may be much more expensive than other options. To explore how managers should optimize their manipulation of vital rates, we incorporated the cost of changing those rates into matrix population models. We derived analytic expressions for locations in parameter space where managers should shift between management of fecundity and survival, for the balance between fecundity and survival management at those boundaries, and for the allocation of management resources to sustain that optimal balance. For simple matrices, the optimal budget allocation can often be expressed as simple functions of vital rates and the relative costs of changing them. We applied our method to management of the Helmeted Honeyeater (Lichenostomus melanops cassidix; an endangered Australian bird) and the koala (Phascolarctos cinereus) as examples. Our method showed that cost-efficient management of the Helmeted Honeyeater should focus on increasing fecundity via nest protection, whereas optimal koala management should focus on manipulating both fecundity and survival simultaneously. These findings are contrary to the cost-negligent recommendations of elasticity analysis, which would suggest focusing on managing survival in both cases. A further investigation of Helmeted Honeyeater management options, based on an individual-based model incorporating density dependence, spatial structure, and environmental stochasticity, confirmed that fecundity management was the most cost-effective strategy. Our results demonstrate that decisions that ignore economic factors will reduce management efficiency.

The effect of density on the properties of short chain fluids

We incorporate density dependence into continuum Born-Green-Yvon (BGY) theory through calculation of the end-to-end intramolecular correlation function. Whereas in previous studies we had only performed this calculation for the case of an isolated (zero-density) square-well chain of m segments (3</=m</=7), here we consider this single chain to have been placed in a square-well monomeric fluid of variable density. We find that the results obtained by this more sophisticated approach are in good agreement with the predictions of both other theories and simulation concerning the structural properties of short chains. Using a homologous series of n-alkanes as a test case, we also conclude that BGY theory, with the current modifications, is capable of describing fluid properties for heptane (n-C7) through nonadecane (n-C19).

Modeling the Population Dynamics of &lt;I&gt;Culex quinquefasciatus&lt;/I&gt; (Diptera: Culicidae), along an Elevational Gradient in Hawaii

We present a population model to understand the effects of temperature and rainfall on the population dynamics of the southern house mosquito, Culex quinquefasciatus Say, along an elevational gradient in Hawaii. We use a novel approach to model the effects of temperature on population growth by dynamically incorporating developmental rate into the transition matrix, by using physiological ages of immatures instead of chronological age or stages. We also model the effects of rainfall on survival of immatures as the cumulative number of days below a certain rain threshold. Finally, we incorporate density dependence into the model as competition between immatures within breeding sites. Our model predicts the upper altitudinal distributions of Cx. quinquefasciatus on the Big Island of Hawaii for self-sustaining mosquito and migrating summer sink populations at 1,475 and 1,715 m above sea level, respectively. Our model predicts that mosquitoes at lower elevations can grow under a broader range of rainfall parameters than middle and high elevation populations. Density dependence in conjunction with the seasonal forcing imposed by temperature and rain creates cycles in the dynamics of the population that peak in the summer and early fall. The model provides a reasonable fit to the available data on mosquito abundance for the east side of Mauna Loa, Hawaii. The predictions of our model indicate the importance of abiotic conditions on mosquito dynamics and have important implications for the management of diseases transmitted by Cx. quinquefasciatus in Hawaii and elsewhere.

THE ROLE OF DENSITY DEPENDENCE IN THE POPULATION DYNAMICS OF A TROPICAL PALM

The role of density dependence in the population dynamics of tropical trees has been a subject of considerable debate. Here, we present data on the demography of the edible palm Euterpe edulis, classified into seven size categories and monitored over three years. On average, each adult palm contributed 98 seedling recruits per year into the population. The pattern of mortality was similar to that of other palms, with mortality being highest among the smallest plants. Those plants with a diameter at soil level >20 mm had an annual mortality <7%. Density dependence was found to act only on the seedling stage of the life cycle. The probability of survival and transition of seedlings to the next size class were affected both by the density of seedlings and the presence of conspecific adults. Matrix modeling indicated that the true finite rate of population increase (λ) was 1.28 and that the observed reverse “J”-shaped size distribution of plants was a consequence of the density dependence operating in the population. Elasticity analysis showed that the survival elements in the matrix contributed most to the value of λ, and that the position of the transition matrix in growth–survival–fecundity (G–L–F) space was influenced by density. The matrix model incorporating density dependence predicted size distributions and densities approximating the maximum observed in the field. Spatial simulations indicated that the predictions from the matrix model relating to the size structure of plants are robust, but that the predictions of densities are sensitive to the precise spatial dynamics of the population.