Strength Of Density-dependent Mortality Research Articles

PREDATION, HABITAT COMPLEXITY, AND VARIATION IN DENSITY-DEPENDENT MORTALITY OF TEMPERATE REEF FISHES

Density dependence in demographic rates can strongly affect the dynamics of populations. However, the mechanisms generating density dependence (e.g., predation) are also dynamic processes and may be influenced by local conditions. Understanding the manner in which local habitat features affect the occurrence and/or strength of density dependence will increase our understanding of population dynamics in heterogeneous environments. In this study I conducted two separate field experiments to investigate how local predator density and habitat complexity affect the occurrence and form of density-dependent mortality of juvenile rockfishes (Sebastes spp.). I also used yearly censuses of rockfish populations on nearshore reefs throughout central California to evaluate mortality of juvenile rockfish at large spatial scales. Manipulations of predators (juvenile bocaccio, S. paucispinus) and prey (kelp, gopher, and black-and-yellow [KGB] rockfish, Sebastes spp.) demonstrated that increasing the density of predators altered their functional response and thus altered patterns of density dependence in mortality of their prey. At low densities of predators, the number of prey consumed per predator was a decelerating function, and mortality of prey was inversely density dependent. However, at high densities of predators, the number of prey killed per predator became an accelerating response, and prey mortality was directly density dependent. Results of field experiments and large-scale surveys both indicated that the strength of density-dependent mortality may also be affected by the structural complexity of the habitat. In small-scale field experiments, increased habitat complexity increased the strength of density-dependent mortality. However, at large scales, increasing complexity resulted in a decrease in the strength of density dependence. I suggest that these differences resulted from scale-dependent changes in the predatory response that generated mortality. Whether increased habitat complexity leads to an increase or a decrease in the strength of density-dependent mortality may depend on how specific predatory responses (e.g., functional or aggregative) are altered by habitat complexity. Overall, the findings of this study suggest that rates of demographic density dependence and the resulting dynamics of local populations may largely depend upon attributes of the local habitat.

CRYPTIC DENSITY DEPENDENCE: EFFECTS OF COVARIATION BETWEEN DENSITY AND SITE QUALITY IN REEF FISH

The importance and strength of density dependence continues to engender debate because of its central importance to population dynamics and regulation. Here, we show how confounding effects of site quality can mask strong effects of density dependence. In particular, we explore spatiotemporal variation and covariation among (1) densities of newly settled coral reef fish (Thalassoma hardwicke), (2) environmental characteristics, and (3) the strength of density-dependent mortality. Environmental features of patch reefs were spatially and temporally variable and influenced density-dependent survival. Higher-quality sites (i.e., reefs possessing features that yield greater numbers of recruits at any given settlement level) received greater settlement, and this relationship masked the operation of density dependence when variation in quality among sites (or times) was not distinguished (a common approach in many observational studies of density dependence). Our work illustrates how spatiotemporal covariation in settlement density and site quality can obscure patterns of density dependence at larger scales, contributing to a phenomenon we call “cryptic density dependence.” Acknowledging patterns and consequences of covariance may alter the way we study population dynamics, especially of marine organisms, where the link between processes that affect settlement and post-settlement survival remains relatively poorly understood. Corresponding Editor: J. R. Bence.

Modelling the population consequences of age‐ and sex‐related differences in winter mortality in the oystercatcher, Haematopus ostralegus

A modelling approach is used to explore the effect of age and sex differences in oystercatcher (Haematopus ostralegus) winter mortality on population size, population structure and the population response to habitat loss or change. Increasing the mortality of first and second year birds reduced population size, but had very little effect on the proportion of the population that were adults. Increasing female mortality reduced population size and resulted in a male‐biased population. A sex bias amongst birds of breeding age meant that there were fewer potential breeding pairs for a given population size, reducing the size of the breeding population and the breeding output. Increasing the mortality of one sex relative to the other reduced population size, even when mean adult mortality rates remained unchanged. Increasing the strength of density‐dependent mortality in young birds caused a greater reduction in population size as habitat was lost. Increasing the strength of female density‐dependent mortality had the same effect, even though male density‐dependent mortality had been correspondingly reduced. Increasing density‐independent or density‐dependent winter mortality in one sex relative to another also exaggerated the disproportional effect of winter habitat loss on separate breeding subpopulations using the same overwintering area. These results suggest that any study of population dynamics should be aware of both age and sex differences in mortality. Conservationists should be particularly aware of any age or sex differences in diet or habitat use that may result in a differential response to environmental change.

PREDATOR RESPONSES, PREY REFUGES, AND DENSITY-DEPENDENT MORTALITY OF A MARINE FISH

Detection of density dependence in animal populations is a primary goal of population ecology, and the processes causing density dependence play a major role in population regulation. Predation can strongly regulate populations by populational and behavioral responses of predators to their prey. Here I evaluate the existence and strength of density-dependent mortality in local populations of a reef fish, the kelp perch (Bra- chyistius frenatus), caused by its predator, the kelp bass (Paralabrax clathratus). Specifi- cally, I examine both the functional response and a potential aggregative response by kelp bass as mechanisms underlying predator-induced density-dependent mortality. I calculated the per capita mortality of kelp perch as a result of the functional response of its predator by subjecting a range of densities of kelp perch to a low, constant number of kelp bass and different amounts of giant kelp (Macrocystis pyrifera) as habitat structure and a potential prey refuge. The potential for an aggregative response by kelp bass was determined by exposing densities of kelp perch to natural levels of kelp bass in the field. Per capita mortality of kelp perch in the laboratory was inversely density dependent to density independent with increasing habitat structure. By contrast, per capita mortality in the field was strongly density dependent, with evidence for an aggregative response by kelp bass. Furthermore, emigration and other nonpredatory losses of kelp perch from field plots were negligible, indicating that the product of the functional and aggregative responses by kelp bass induced density-dependent mortality in kelp perch. My results indicated that increasing habitat structural complexity at low densities of kelp perch, coupled with a strong aggregative response by kelp bass, was responsible for the observed pattern of density-dependent mortality. In addition, the distribution and relative abundances of kelp perch and kelp bass at larger spatial scales (entire reefs) were consistent with predation as an important process structuring local populations of kelp perch. The contrasting patterns of short-term per capita mortality found here underscore the need to evaluate both the functional and aggregative responses of predators to their prey. Ultimately, the pattern of mortality expressed will be determined by the relative strengths of these behavioral re- sponses and the degree to which habitat structure provides an effective prey refuge.

New insights into testing for density dependence.

The reasons why tests for density dependence often differ in their results for a particular time-series were investigated using modelled time-series of 20 generations in lenght. The test of Pollard et al. (1987) is the most reliable; it had the greatest power with the three forms of density dependent data investigated (mean detection rates of 50.8-61.1%) and was least influenced by the form of the density dependence in time-series. Bulmer's first test (Bulmer 1975) had slightly lower power (mean detection rates of 27.4-56.8%) and was more affected by the form of density dependence present in the data. The mean power of the other tests was lower and detection rates were more variable. Rates were 24.6-46.2% for regression of k-value on abundance, 6.4-32.6% for regression of k-value on logarithmic abundance and 0.2-13.7% for Bulmer's second test (Bulmer 1975). Bulmer's second test is not useful because of low power. For one method, regression of k-value on abundance. density dependence was detected in 19.9% of timeseries generated using a random-walk model. For regression of k-value on logarithmically-transformed abundance the equivalent figure was 18.3% of series. These rates of spurious detection were significantly (P<0.001) greater than the generally accepted 5% level of type 1 errors and so these methods are not suitable for the analysis of time-series data for density dependence. Levels of spurious detection (from random-walk data) were around the 5% level and hence were acceptable for Bulmer's first test, Bulmer's second test, and the tests of Pollard et al. (1987), Reddinguis and den Boer (1989) and Crowley (1992). For all tests, except Bulmer's second test, the rate of detection and the amount of autocorrelation in time-series were negatively correlated. The degree of autocorrelation accounted for as much as 59.5-77.9% of the deviance in logit proportion detection for regression of k-value on abundance, Bulmer's first test, and the tests of Pollard et al. and Reddingius and den Boer. For regression of k-value on abundance this relationship accounted for less of the deviance (29.4%). Independent effects of density dependence were largely absent. It is concluded that these are tests of autocorrelation, not density dependence (or limitation). Autocorrelation was found to become positive (which is similar to values from random-walk data) as the intrinsic growth rate became either small or large. As the strength of density dependence (in the discrete exponential logistic equation) is dependent on the product of the intrinsic growth rate and the density dependent parameter α it is unclear whether this is because of variation in the strength of density dependent mortality or reproduction per se. However, small values of the intrinsic grwoth rate cause the amount of variation in the data to become small, which might hinder detection of density dependence, and large values of the intrinsic growth rate are coincident with determinstic chaos which hinders detection. The user of these tests for density dependence should be aware of their potential weakness when variation within time-series is small (which itself is difficult to judge) or if the intrinsic growth rate is large so that chaotic dynamics might result. Power and levels of variability in rates of detection using Reddingius and den Boer's test were intermediate between those of the test of Pollard et al. and Bulmer's first test. This, combined with the strong relationship between rates of detection of limitation and the value of the autocorrelation coefficient, make testing for limitation similar to testing for density dependence. Crowley's test of attraction gave the widest range of mean detection rates from density dependent data of all the tests (20.4-60.6%). The relative rates of detection for the three forms of density dependent data were opposite to those found for Bulmer's first test and the test of Pollard et al. I conclude that testing for attraction is a complementary concept to testing for density dependence. As dynamics represented in time-series generated using a stochastic form of the exponential logistic equation became chaotic, Bulmer's first test, the test of Pollard et al. and regression of k on abundance failed to detect density dependence reliably. Conversely, Crowley's test was capable of detecting attraction with a power between 96 and 100% with time-series containing both stochastically and deterministically chaotic dynamics. This difference from other tests is in agreement with the lower influence of autocorrelation.