- Research article
- Open Access
The effects of spatial and temporal heterogeneity on the population dynamics of four animal species in a Danish landscape
© Sibly et al; licensee BioMed Central Ltd. 2009
Received: 10 February 2009
Accepted: 23 June 2009
Published: 23 June 2009
Variation in carrying capacity and population return rates is generally ignored in traditional studies of population dynamics. Variation is hard to study in the field because of difficulties controlling the environment in order to obtain statistical replicates, and because of the scale and expense of experimenting on populations. There may also be ethical issues. To circumvent these problems we used detailed simulations of the simultaneous behaviours of interacting animals in an accurate facsimile of a real Danish landscape. The models incorporate as much as possible of the behaviour and ecology of skylarks Alauda arvensis, voles Microtus agrestis, a ground beetle Bembidion lampros and a linyphiid spider Erigone atra. This allows us to quantify and evaluate the importance of spatial and temporal heterogeneity on the population dynamics of the four species.
Both spatial and temporal heterogeneity affected the relationship between population growth rate and population density in all four species. Spatial heterogeneity accounted for 23–30% of the variance in population growth rate after accounting for the effects of density, reflecting big differences in local carrying capacity associated with the landscape features important to individual species. Temporal heterogeneity accounted for 3–13% of the variance in vole, skylark and spider, but 43% in beetles. The associated temporal variation in carrying capacity would be problematic in traditional analyses of density dependence. Return rates were less than one in all species and essentially invariant in skylarks, spiders and beetles. Return rates varied over the landscape in voles, being slower where there were larger fluctuations in local population sizes.
Our analyses estimated the traditional parameters of carrying capacities and return rates, but these are now seen as varying continuously over the landscape depending on habitat quality and the mechanisms of density dependence. The importance of our results lies in our demonstration that the effects of spatial and temporal heterogeneity must be accounted for if we are to have accurate predictive models for use in management and conservation. This is an area which until now has lacked an adequate theoretical framework and methodology.
Population ecology takes the population as the unit of study, identifies factors responsible for population growth or decline, and quantifies their effects. Variations in the circumstances of individuals in time and space (heterogeneity) are generally ignored. However real landscapes rarely approximate to homogeneity, and spatial and temporal heterogeneity are the norm in the fragmented landscapes of the natural world. Thus it is important to know whether and how spatial and temporal heterogeneity affects population dynamics.
Population dynamics often begins by analysing the relationship between a population's density and its growth rate [1, 2]. Population growth rate, pgr hereafter, is defined as the per capita growth rate of the population. The relationship between pgr and the natural logarithm of density determines whether a population will return to equilibrium after a disturbance, and the slope of the relationship determines how fast any such return will be. The negative of the slope is referred to as return rate  or as the strength of density dependence (e.g. ), and is sometimes estimated from the first coefficient in an autoregression analysis (e.g. [5, 6]. In discrete generation models a return rate of one per unit time means that a population returns to equilibrium after perturbation in a single time unit in the absence of further perturbations . Positive return rates less than two indicate population stability, and return rates less than one indicate that population density approaches equilibrium smoothly without oscillating (see  for further discussion).
Return rates and carrying capacities are key measures in the analysis of population dynamics. Until recently most studies of population dynamics have assumed that both are constant in space and time, and spatial and temporal heterogeneity has generally been ignored. However heterogeneity can affect vital rates (e.g., [7–9]) and density dependent processes (e.g., [10, 11]), for example return rates have been shown to vary with predator density in a tropical damselfish (Dascyllus fiavicaudus) . Since heterogeneity is widespread in real landscapes, it is important to know whether and how spatial and temporal heterogeneity affect local carrying capacities and return rates.
Several approaches to incorporating heterogeneity into population dynamic analyses have been taken. The simplest ignores landscape structure and studies resemblances between the autocorrelation coefficients of spatially separated populations. Using this method return rates have been shown to decrease with latitude in grouse populations in North America , in caribou and reindeer in Greenland, Finland and Russia , and in voles (Microtus arvalis) in Fennoscandia and eastern Europe [5, 15] but cf. . Return rates of large herbivore populations are increased by temporal heterogeneity in weather, but decreased by spatial heterogeneity in resources in the Rocky Mountains, USA . However return rates of red kangaroos (Macropus rufus) did not vary among pastoral zones in South Australia . At the other end of the spectrum landscape ecology provides more realistic treatments of the effects of heterogeneous landscapes on the animals that live there, but has so far little considered their population dynamics. However some progress has been made identifying landscape features that predict species presence, persistence and dispersal ; using analytic spatially explicit models to determine population spread rates  and growth rates ; and using our system to study how landscape framentation affects predator-vole dynamics .
There is therefore a need for population dynamics theory that effectively incorporates realistic effects of spatial and temporal heterogeneity. Here we use agent-based models (ABMs) to explore the mechanics and dynamics of four ecologically-contrasting species in a heterogeneous Danish landscape. Spatial variation in local carrying capacity is expected because habitats vary across landscapes (e.g., ), but return rates are expected to be invariant unless the mechanisms of density dependence vary. These predictions are largely supported.
In this paper population density is described by loge(N t ), where N t is the number of adult females in a specified area in year t; pgr is estimated as loge(Nt+1/N t ); return rate as the negative of the slope of the relationship between density and pgr; i.e. as – [dpgr/dlogeN t ] K ≡ - [N t dpgr/dN t ] K , where local carrying capacity, K, is defined as population size in a specified area when pgr = 0. In practice the specified areas are 500 × 500 m grid squares as described below.
These simulation tools gave us an opportunity to carry out experiments on dispersing animals with complex life histories within Danish landscapes. Simulations were initiated with individuals distributed in the landscape at random, their number being close to the overall carrying capacity of the landscape, and run for 200 years repeating a real 10 year weather data sequence 20 times. This was to allow for the analysis of temporal variation using 'weather years' as explanatory variables in the GLM models described below.
A GLM population model
Summary ANOVA tables for each species for GLMs regressing pgr against population density (log scale), weather years and squares and their interactions.
Figure. 3 shows the effects of variation between grid squares, denoted by colours, for a randomly chosen weather year. Density had a linear effect on pgr for each grid square, with similar slopes within species except the vole, where it appears squares showing larger population fluctuations had shallower slopes (this is discussed further below). The lines intersect pgr = 0 at carrying capacity, and these equilibrium densities varied widely between squares in each species, as expected since habitats varied across the landscape (Figure. 1, left-hand panel). The proportion of variance in pgr explained by grid square was similar for all four species (0.23 – 0.30, Table 1).
Return rates, y-1, for each species obtained as minus the regression coefficient for density.
Local carrying capacities of the grid squares are shown in maps in Figure. 1.
Population characteristics are emergent properties of collections of individuals in ABMs as in the real world . It follows that even if individuals are completely deterministic, it is not necessarily true that population characteristics are directly related to environmental characteristics such as weather year or habitat quality in simple one-to-one relationships. In particular, none of the population characteristics revealed in the present analyses were known in advance. Instead they emerged from characteristics of landscapes, weather and individual behaviour reported in field studies and programmed into the ABMs. Thus the revealed population characteristics require explanations. Ideally these will be in terms of the properties of individuals in simple mechanistic relationships, but there is no guarantee such relationships exist. Below, we suggest mechanisms in terms of known and programmed behavioural ecology, and we back up our suggestions where possible by simulation experiments  and further analysis of simulation data. However proof of the validity of our conjectures requires in some cases additional experimentation beyond the scope of the present paper.
There was considerable spatial variation in local carrying capacity (Figure. 1) as expected because habitat quality varied across landscapes, and variation between 500 × 500-m squares accounted for 23% – 30% of the variance in pgr in the general linear model (Table 1). It can be seen that carrying capacities broadly reflect the main features of the landscape shown in Figure. 1. Thus the primary habitats of the skylarks and spiders were the arable fields which occur in the triangular regions at lower right and top left, but there the voles and beetles were scarce. These distributions result from the known behavioural ecology of each species. Skylarks nest in arable fields because they prefer open and accessible low vegetation, and the modelled spiders are an opportunistic species specializing in disturbed habitats like arable fields. Voles prefer the permanent grass cover often found beside roadside verges and other linear features. The beetles prefer pasture and arable fields, but their numbers suffer in arable fields because of disturbance during farming operation .
Although carrying capacities varied, there were no differences between squares in the other key parameter, the return rate, except in the vole. This is shown by the parallelism of the pgr-density relationships, return rate being by definition the negative of the slope of this relationship. Invariance of return rates has also been reported in red kangaroos (Macropus rufus) in the pastoral zones in South Australia . Invariance of return rates suggests, but does not prove, that the mechanisms of density dependence are the same in all squares.
Average return rates are shown in Table 2, and are less than one y-1 for all four species, indicating that a return to carrying capacity after disturbance takes more than a year. These estimates are likely overestimates because our method of estimation of return rates has an intrinsic bias towards one , so the true values may be lower than shown in Table 2. Return rates less than one indicate that populations are stable and show no tendency to oscillate about their equilibrium values [3, 35].
The importance of our results lies in our quantification of the effects of spatial and temporal heterogeneity on the population dynamics of the four study species. The magnitude of these effects has implications for how we understand and predict population dynamics in reality. The effects of spatial and temporal heterogeneity must be accounted for if we are to have accurate predictive models for use in management and conservation. The size of the temporal effects shown here also has implications for how we evaluate long term temporal change (e.g. through climate change), since where there are large temporal variations, longterm changes will not be discernible quickly.
The effects of spatial heterogeneity were here shown by a significant square-density interaction, seen here in the vole (Table 1). Such interaction terms should be included in autoregression analyses of spatially separated populations, where return rate is given by the first autoregressive coefficient [5, 6], but this has not been attempted to our knowledge except in 's study of kangaroos. It is not possible to include all the interaction terms included here in analyses of real spatially separated populations, because there would not be enough degrees of freedom. In our simulations we overcame this by replicating the weather years. Nevertheless the interactions that were most important here could be included, these would be density*year and density*square. Together with the main effects of year and square this would allow analysis of whether both carrying capacities and return rates vary in space and time. This provides a method of answering the question as to whether there is spatial and temporal heterogeneity in the population's dynamics.
How general are our conclusions? There are several limitations to our study. The study species were selected because they represent different functional groups, and each has qualities that make them representative of other species, but more would certainly be better. Similarly other landscapes should be investigated. For example the landscapes experienced by northern Scandinavian voles are more homogeneous than those modelled here, so spatial heterogeneity should there affect population dynamics less. The effects of weather year are likely qualitatively robust, though quantitatively effects depend on the actual weather experienced. Lastly, we have not investigated the effects of population sizes in earlier years, i.e., Nt-1, Nt-2, but these would allow the identification of population cycles found in, e.g., some vole species [37, 38].
It may be questioned as to whether we are here studying reality, or just very complex models. It is important to stress that our ABMs are the best available representations of life in the study site, which is why we chose to work with them. So, the emergent population properties of the ABMs should provide the most accurate characterisation of the real populations that is currently possible. Accuracy is not however guaranteed and checking – and correction – will probably be needed over the foreseeable future. The detailed nature of ABM predictions allows many checks, and such testing is ongoing. Related to this, it may seem surprising that not all emergent population properties of the ABMs are fully understood. Obtaining these mechanistic explanations is part of our research programme, however this can be laborious in ABMs as in reality, and success is not certain, as explained at the start of this Discussion.
Here we have used previously published detailed ABMs to gain new conceptual insights into how populations behave in landscapes that vary geographically in realistic fashion. The results of much field work are encapsulated within each ABM but their population properties were not known in advance. The present study obtains population insights from known behavioural observations. The importance of our results lies in our demonstration that the effects of spatial and temporal heterogeneity must be accounted for if we are to have accurate predictive models for use in management and conservation. This is an area which until now has lacked an adequate theoretical framework and methodology [19, 23, 39].
All authors are members of the Centre for Integrated Population Ecology (CIPE), which is supported by the Danish Natural Sciences Research Council. This paper benefited from discussions with additional members of the CIPE team, Jim Hone and members of QBAS at the University of Reading.
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