Journal of Applied Ecology 2012, 49, 581–590
doi: 10.1111/j.1365-2664.2012.02138.x
Population density but not stability can be predicted from species distribution models Tom H. Oliver1*, Simon Gillings2, Marco Girardello1, Giovanni Rapacciuolo1, Tom M. Brereton3, Gavin M. Siriwardena2, David B. Roy1, Richard Pywell1 and Robert J. Fuller2 1
Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK; 2British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2PU, UK; and 3Butterfly Conservation, Manor Yard, East Lulworth, Wareham, Dorset BH20 5QP, UK
Summary 1. Species distribution models (SDMs) are increasingly used in applied conservation biology, yet the predictive ability of these models is often tested only on detection ⁄ non-detection data. The probability of long-term population persistence, however, depends not only upon patch occupancy but upon more fundamental population parameters such as mean population density and stability over time. 2. Here, we test estimated probability of occurrence scores generated from SDMs built using species occupancy data against independent empirical data on population density and stability for 20 bird and butterfly species across 1941 sites over 15 years. We devised a measure of population stability over time which was independent of mean density and time-series duration, yet positively correlated with risk of local extinction. This may be a useful surrogate measure of population persistence for use in applied conservation. 3. We found that probability of occurrence scores were significantly positively correlated with mean population density for both butterflies and birds. In contrast, probability of occurrence scores were at best weakly positively correlated with population stability. Referring to established ecological theory, we discuss why SDMs may be appropriate for predicting population density but not stability. 4. Synthesis and applications. Species distribution models are often constructed using species occupancy data because, for the majority of species and regions, these are the best data available. The models are then often used for projecting species’ distributions in the future and identifying areas where management could be targeted to improve species’ prospects. However, our results suggest that an overreliance on these SDMs may result in an exclusive focus on landscape management approaches that promote patch occupancy and density, but may overlook features important for long-term population persistence such as population stability. Other landscape metrics that take into account habitat heterogeneity or configuration may be required to predict population stability. To understand species persistence under rapid environmental change, count data from standardised monitoring schemes are an invaluable resource. These data provide additional insights into the factors affecting species’ extinction risks, which cannot easily be inferred from species’ occupancy data. Key-words: bioclimatic niche models, butterfly and bird population trends, detection ⁄ nondetection data, extinction risk, population dynamics, population monitoring data, population persistence, presence ⁄ absence data
Introduction Identifying species ‘niches’, the environmental conditions in which they are found, and using these to model future changes *Correspondence author. E-mail:
[email protected]
in populations is an approach that is increasingly used to inform conservation policy (Vos et al. 2008). To understand how species are likely to respond to environmental change, correlative statistical models based on empirical data on species distributions have been developed to predict which areas will be climatically suitable for species according to future
2012 The Authors. Journal of Applied Ecology 2012 British Ecological Society
582 T. H. Oliver et al. environmental scenarios (‘species distribution models’ or ‘bioclimate’ models; Guisan & Zimmermann 2000). The aim of all these modelling endeavours is to identify (i) species at high risk from environmental change and (ii) areas that are particularly valuable to species under altered climates, that is, areas of current range that will remain climatically suitable or new areas that will benefit from improvement to enable the species to more fully realise their potential ranges. The standard procedure for species distribution modelling is that statistical correlations are identified between species detection ⁄ non-detection (‘presence-absence data’ where detection is imperfect; MacKenzie et al. 2006) and various land cover and climate explanatory variables, including complex interactions between explanatory variables. A number of methods exist to build these models. For example, regression models, such as generalised additive models (GAM), and machine-learning techniques, such as artificial neural networks (ANN), random forests (RF) and maximum entropy (MAXENT) are all techniques that perform well in controlled comparisons. Next, after building the statistical model, predictions are made of the probability of species occurrence given the specific combination of explanatory variable values at each location. These predictions are validated using the test data set to give a final measure of the goodness-of-fit of the model (e.g. AUC scores, Kappa statistic). Models with sufficient amount and quality of input data generally perform well, although if results are extrapolated outside the spatial or temporal range of the training data then there are caveats regarding species dispersal limitations, local adaptation, biotic interactions etc. (Sobero´n & Nakamura 2009). To date, species distribution models (SDMs) have mostly been constructed using categorical detection ⁄ non-detection data and have thus only been able to project likely changes in species distributions in terms of occupancy. However, it is population parameters, such as density and stability that are most useful in assessing the conservation status of species. Although a number of studies have recently used estimates of population abundance as input in SDMs (Shoo, Williams & Hero 2005; Randin et al. 2009; Wilson et al. 2010; Huntley et al. 2011; Kulhanek, Leung & Ricciardi 2011; Renwick et al. 2011; Tucker, Rebelo & Manne 2011), it is clear that, in most cases, only occupancy data are available. Whether the outputs of SDMs using occupancy data can adequately predict population parameters, such as density and stability over time, has rarely been tested. Links have been long been drawn between occupancy and abundance across species, with species’ total range size often positively related to local abundance (Hanski 1982; Brown 1984; Gaston et al. 2000). At the interspecific level, projected reductions in the total area of suitable climate space have been found to be correlated with species’ population declines (Green et al. 2008; Gregory et al. 2009). However, intraspecific relationships between climatic suitability and population density are less well studied. Of the few studies that consider whether SDMs can predict population density, results are equivocal, with poor correlations in many cases (Pearce & Ferrier 2001; Nielsen et al. 2005; Elmendorf & Moore 2008; Jime´nez-
Valverde et al. 2009; but see VanDerWal et al. 2009). With multiple visits to the same sampling sites, species’ detectability and population densities can be inferred with some success (Royle & Nichols 2003; MacKenzie et al. 2006); yet SDMs rarely build in probabilities of detection, probably because repeat visits to the same site are rare with ad-hoc collected distribution data. Moreover, the ability of SDMs to predict other aspects of population persistence has also been neglected. A few case studies have experimentally tested climatic suitability from SDMs by assessing establishment success or reproductive output (Wright et al. 2006; Elmendorf & Moore 2008; Willis et al. 2009). If SDMs are to be used in applied conservation (e.g. for prioritising conservation actions in different areas), then we need more confidence that projected probability of occurrence surfaces are associated with fundamentally important measures of population persistence, rather than simply species’ presence or absence. Two correlates of population persistence are mean density and inter-annual variability. Larger populations are known to suffer from lower extinction risk, whilst smaller populations are more vulnerable to both demographic and environmental stochasticity driving them to local extinction (Pimm, Jones & Diamond 1988). In addition, there is theoretical and empirical evidence that populations that are more variable over time also suffer greater extinction risk (Pimm, Jones & Diamond 1988; Lande 1993; Inchausti & Halley 2003). Hence, the IUCN Red List criteria for classifying species’ extinction risk include both small population size and the observation of extreme population fluctuations (Mace et al. 2008). Even for true metapopulations where the occupancy of individual patches is transient, the persistence of the overall metapopulation depends on adequate local densities being maintained and will also be facilitated by increased longevity of populations in individual patches (Hanski 1999). Therefore, understanding the landscape and climatic factors that promote population density and stability is crucial for species conservation. With this in mind, we test whether a number of SDMs that are widely used in conservation literature can be used as a tool to predict population density and stability. If they can, these modelling frameworks may be used to prioritise areas for conservation based on their ability to host persistent populations of species. If they cannot, the conservation value of the priority areas identified by these models, as they stand, must be questioned. In this study, we analyse population time series for 20 species of butterflies and birds across 1941 locations in Britain over a 15-year period. For each time series, we calculate mean density and a measure of population stability that is independent of density, yet still associated with extinction risk. These population parameters are then related to probability of occurrence scores generated from four commonly used SDMs built using independent species detection ⁄ non-detection data. In an ideal world, where data availability is not an issue, we might have land cover and climate data that were a perfect temporal match with our species distribution data. However, we use the best input data sources currently available and have selected
2012 The Authors. Journal of Applied Ecology 2012 British Ecological Society, Journal of Applied Ecology, 49, 581–590
Predicting density and stability from SDMs 583 commonly used modelling frameworks that perform well under standard tests. Hence, we take a pragmatic approach by asking: With the best data currently available to build SDMs, are these models fit for purpose in terms of predicting the areas where populations have the highest densities and most stable population dynamics? Therefore, a poor fit between the SDM probability of occurrence scores and these independent population parameters does not necessarily mean that SDMs will always fail to predict population persistence accurately, only that, given the input data and the modelling frameworks, predictions in this certain case are inadequate.
Materials and methods DATA COLLATION
The most suitable monitoring and environmental data sets available for Great Britain were collated. We aimed to test the efficacy of SDMs in predicting population density and stability for more than one bird and butterfly species, but testing on all British species was not feasible. We therefore selected 10 birds and 10 butterfly species (Table S1, Supporting information) and used the following criteria: (i) species had a reasonable geographical coverage across Britain providing a large number of monitored sites [mean 648Æ6 ± 136Æ5 (SE) sites per species], (ii) each species group had an equal proportion of species previously classed as habitat ‘specialists’ vs. ‘generalists’(butterflies, Asher et al. 2001; birds: Siriwardena et al. 1998 and authors’ judgement), (iii) each species group comprised species with a range of estimated dispersal capabilities, (iv) the species were generally easily detected during field surveys to reduce the potential for stochasticity in detection to mask population fluctuations. For each species, population time-series data were obtained from the UK Butterfly Monitoring Scheme (UKBMS) and the BTO ⁄ JNCC ⁄ RSPB Breeding Bird Survey (BBS). Full details of each scheme are available elsewhere (Pollard & Yates 1993; Risely et al. 2010), and we briefly summarise them in the Supporting information. For all monitoring sites, we noted the region in which a site occurred using a 50-km British Ordnance Survey grid, which was later used to account for spatial autocorrelation in the data. Species detection ⁄ non-detection data for butterflies were obtained from the Butterflies for the New Millennium (BNM) Atlas, comprising species records georeferenced to the nearest 1-km position on the GB grid between 1995 and 2004 (Asher et al. 2001). Tetrad (2-km grid cell) resolution detection ⁄ non-detection data for birds were obtained from the New Atlas of Breeding Birds in Britain and Ireland (Gibbons, Reid & Chapman 1993) for 1988–1991. Our environmental layers comprised remotely sensed land cover data along with climate data for four key bioclimate variables. For climate data, we used the following variables, known to affect the distribution and physiology of butterfly and bird species: growing day degrees above 5 C (GDD5), mean temperature of the warmest month, mean temperature of the coldest month and ratio of actual to potential evapotranspiration (Roy et al. 2001; Hill et al. 2002; Thuiller, Arau´jo & Lavorel 2004; Robinson, Baillie & Crick 2007). These climate data were obtained from CRU ts2.1 (Mitchell & Jones 2005) and CRU 61-90 (New, Hulme & Jones 1999) data sets and interpolated to a 10-km British Ordnance Survey grid. Climate variables for each 1-km or tetrad grid square were taken from the 10-km grid square in which they were located. The climate data comprised the years 1988, the earliest year that species distribution data were collected, to 2000, the latest year that climate data were available. Bioclimate variables were calcu-
lated annually over the entire period and the long-term average taken. For land cover data, the areas of 13 biotopes (Table S2, Supporting information) inside each 1-km or tetrad grid cell were summarised from the 25-m resolution UK LandCover 2000 map (Fuller et al. 2002).
CALCULATING POPULATION DENSITY AND STABILITY
We used our species monitoring data to calculate the mean density and population stability over time for each species at each site. For each species, we used all UKBMS or BBS sites, which had been monitored for 10 or more years between 1994 and 2008, to minimise variation in the duration of population time series that can affect measures of stability (Arino & Pimm 1995); our time series ranged from 10 to 15 years. We also included only time series that consisted of
