Species abundance distributions over time

Transcription

1 Ecology Letters, (2007) 10: doi: /j x IDEA AND PERSPECTIVE Species abundance distributions over time Anne E. Magurran Gatty Marine Laboratory, University of St Andrews, Fife KY16 8LB, UK Correspondence: Abstract It has been known for 50 years that the time period over which data are collected affects the shape of empirical species abundance distributions. However, despite a recent resurgence of interest in characterizing and explaining these patterns the temporal component of species abundance distributions has been largely ignored. I argue that it is essential to take account of time, and not only because sampling duration can have a profound influence on the perceived shape of the distribution. Partitions of species abundance distributions based on temporal occurrence in the record will facilitate tests of both biological and neutral models and may lead to a better understanding of rarity. These temporal partitions also have interesting, but as yet barely explored, parallels with spatial ones such as the core-satellite division. Moreover, changes in abundance distributions across all three of Preston s temporal scales (sampling time, ecological time and evolutionary time) present rich opportunities for ecological research. Keywords Species abundance distributions, Preston, frequent and occasional species, core-satellite species, veil line, sampling. Ecology Letters (2007) 10: Frank Preston is best remembered for his pioneering work on species abundance distributions, particularly the log normal model (Preston 1948). However, he also argued (Preston 1960) that similar processes, such as species turnover, succession and habitat change, underpin the accumulation of species over space and time. With a few notable exceptions (e.g. Rosenzweig 1995, 1998), these ideas were neglected until very recently. An increased appreciation of the dynamical aspects of ecological communities, combined with the need to devise effective conservation policies, has reawakened interest in Preston s proposition. It transpires that species area and species time curves are indeed closely related. Moreover, there is considerable similarity in the form of the species time relationship across assemblages (White et al. 2006). Species abundance distributions are currently the focus of intense research activity, and there is an ongoing debate about the best way of modelling empirical patterns (e.g. Hubbell 2001; McGill 2003a,b; Volkov et al. 2003; Connolly et al. 2005; Dornelas et al. 2006; McGill et al. 2006). Despite this, and in contrast to the advances in the species area, species time debate, the temporal component of species abundance distributions has received little attention. Here I argue that analyses of species abundance distributions must take account of time. Not only are there are close parallels with some familiar spatial patterns but it also seems likely that a temporal perspective will lead to a better understanding of the processes that underlie species abundance distributions. SPECIES RICHNESS OVER SPACE AND TIME Preston (1960) predicted that the number of species recorded at a given locality will increase over time because of events operating at three temporal scales: sampling effects, ecological change such as succession, and evolutionary change including speciation. Although other researchers (e.g. Fisher et al. 1943) had previously noted the accumulation of species over time, Preston (1960) was, as far as I am aware, the first person to assert that species area and species time curves are equivalent. Adler & Lauenroth (2003) used long-term data from grassland communities to test Preston s prediction. As they anticipated, new species were added to the species list more slowly when larger areas were surveyed. Evidence for a general species time area relationship was provided by Adler et al. (2005) who showed, for eight independent assemblages, that recorded species richness is a function of both sampling duration and sampling area. A negative time area interaction was detected in all cases. In other words, the slope of the species time curve was lower when larger areas were sampled and the slope of the species area curve was lower when sampling occurred over longer periods. Adler et al. (2005) also found that the scales at which species area and species time

2 348 A. E. Magurran Idea and Perspective plots were equivalent differed amongst taxa. For example, it is necessary to sample an area of c. 4m 2 of Californian intertidal invertebrates every year to detect the same level of temporal turnover, in species composition, as there would be for spatial turnover if multiple plots of this size were sampled within a single year. In contrast, the spatial scale at which turnover over time and space in Arizona rodents balance out is three orders of magnitude higher. As Adler & Lauenroth (2003, p. 754) rightly point out: ÔTo be meaningful, any estimate of species richness must clearly specify the area and (my italics) the time period of samplingõ. SPECIES ABUNDANCES OVER SPACE AND TIME As the foregoing discussion shows, the relationship between species area and species time curves is now well, if belatedly, established. Surprisingly, given that Preston first articulated these ideas in the context of species abundances with particular emphasis on the log normal model there is still relatively little appreciation of the fact that the shape of the abundance distribution is also affected by temporal factors. This omission is all the more curious as Preston was not the only pioneer of species abundance distributions to comment on the parallel influences of space and time. Williams (1953), for example, noted that the shape of the abundance distribution of moths collected in a light trap changes with sampling duration. Samples compiled over short periods have a pronounced log series quality with a dominant singleton class. As sampling duration increases, the mode of the distribution moves to the right, and it becomes increasingly log normal in character [see, for example, Fig. 2.4 in Hubbell (2001), Fig in Magurran (2004), Connolly et al. (2005) and the discussion in McGill (2003a)]. This pattern is consistent with Preston s Ôveil lineõ hypothesis (Preston 1948; Williams 1964) which states that the true abundance distribution of an assemblage, the log normal, will be progressively revealed (unveiled) as sampling becomes more complete. One possible source of confusion is that Preston did not distinguish between temporal and spatial influences on the veil line. In practice the increase in the extent of a survey in space is often accompanied by an increase in the time period over which data are collected. Williams (1964, p. 30) recognized this potential confound in his comment ÔThe best test [of veil line theory] would be a number of samples taken simultaneously from a large uniform population, which could be combined into groups of varying sizeõ. In what follows I first characterize temporal influences on species abundance distributions. I then draw parallels with other well-known patterns in ecology, particularly the core satellite partition (sometimes also described as the distribution abundance curve). Next, I ask how sampling over time can affect the shape of the distribution. Finally, I consider different time scales, and their implications for species abundance distributions. DISSECTING OUT THE TEMPORAL COMPONENT OF SPECIES ABUNDANCE DISTRIBUTIONS When Magurran & Henderson (2003) started to look at the relative abundances of species in one particularly wellcensused assemblage, they soon realized that the temporal behaviour of species, in terms of their permanence in the record, affected their signature on the species abundance distribution. The assemblage in question was the estuarine fish of the UK s Bristol Channel. It had been sampled monthly for 21 years the sampling device in this instance being the cooling water filter screens belonging to the Hinkley Point nuclear power station. They found that infrequent species ones that appeared only occasionally in the record were typically rare when they did occur. Frequent species, the fish that were recorded year after year, were often common. Their assemblage was easily divisible into these two categories the frequent (or core) species followed a log normal distribution whereas the abundance distribution of the infrequent (or occasional) ones resembled the log series of Fisher et al. (1943). The two sets of species had different ecological characteristics. Core species were adapted to life in estuarine environments whereas occasional species were associated with other habitats such as deep water or rocky shores. Ulrich & Ollik (2004) also divided a community, this time forest Hymenoptera, into frequent and occasional species. As with the Bristol Channel fish, the log normal distribution provided a good description of the frequent species. In contrast to Magurran & Henderson s findings, the selfsimilar (Harte et al. 1999), or fractal (Mouilliot et al. 2000), model provided a better fit of infrequent species than the log series model did. The zero-sum model (Hubbell 2001) did not adequately explain either distribution. Log normal and fractal abundance distributions for core and occasional species, respectively, were also observed for ground beetles on small lake islands in Poland (Ulrich & Zalewski 2006). These species differed in their spatial distribution and body size ratios providing support for the hypothesis that biological factors underpin the relative abundance of the core species whereas random dispersal is important in structuring the occasional species. These two sets of studies, on fish and insects, reinforce the idea that an assemblage consists of core and occasional species. At the same time the work highlights how little is known about the abundances of rare species. The left, or rare, end of the distribution is often the focus of interest as this is where the differences between models are usually

3 Idea and Perspective Species abundance distributions over time 349 revealed. The log normal and log series distributions, for example, diverge in the low abundance classes. This is the part of the curve where the imprint of immigration, or turnover, is most evident. (It is also the portion of the distribution most affected by sampling a point I revisit below.) Partitioning an assemblage into core and occasional components means that predictions can be made about the abundances of rare species, and insights gained into the underlying processes. First, we might expect the relative importance of the occasional species component of the distribution to be related to the opportunity for dispersal (see also Holloway 1977; Southwood 1996; Magurran & Henderson 2003; Mouquet & Loreau 2003). Occasional species are likely to be more prominent in communities composed of taxa that readily disperse (generalist or mobile species) than for those that do not (specialist or static ones). Similarly, the balance between occasional and core species might be expected to differ between habitats where dispersal is easy (such as interconnected wetland systems) and those where it is difficult (for instance isolated lakes). In fact these distinctions are already implicitly made by researchers who prune vagrant or tourist species before analysing species abundance distributions (Nee et al. 1991; Gaston 1996). There are also resonances here with Hubbell s (2001) dispersal parameter m. Second, it should be possible to make a priori predictions, based on taxon and habitat type, about the relative importance of neutral or biological mechanisms in accounting for the rare species abundance distribution. A comparative test of this hypothesis would be fascinating. Biological processes might be expected to be less important where there is more dispersal (Chave 2004; but see also Gray et al. 2005). Hubbell s (2001) neutral model is a logical null model for use in such investigations. A partition based on frequency of occurrence in the record has a further benefit. Niche-based models describe the manner in which resources are apportioned amongst species (Tokeshi 1993, 1996; Magurran 2004). The success of each species in capturing resources is assumed to be reflected in its relative abundance (but see Thibault et al. 2004). Typically one evaluates these models by comparing observed species abundances with predicted ones. It seems logical to focus on the core species on the basis that this is where niche-apportionment is most likely to be happening. This tallies with the advice (e.g. Tokeshi 1993; Magurran 2004) to ignore rare species when testing niche-based models. Interestingly, the log normal distribution is not usually regarded as a niche-apportionment model (but see Sugihara 1980), yet seems to be the most appropriate descriptor of those species whose abundances may depend on the outcome of niche competition. Thus far I have assumed that an assemblage is easily divisible into two components and that there are relatively few species of intermediate abundance. Time will tell whether this type of dichotomous classification has broad application or whether it represents an over-simplified view of community structure. Moreover, there are other ways of dividing assemblages. Ugland & Gray (1982) and Gray (1987) earlier proposed that log normal distributions are composed of three (or more) symmetrical distributions of very common, moderately common and rare species. The distributions are predicted to move apart under disturbance or enrichment and to merge to form a single distribution where there is no disturbance. However, Gray et al. (2005) also point out that the right-hand distribution consisting of the extremely abundant species is usually quite small and difficult to distinguish statistically. Indeed they argue that a two-group model common and rare species is the most parsimonious way to deal with empirical species abundance distributions. They provide a useful technique for separating these distributions in the absence of a time series. Once again the common species are best described by a (symmetrical) log normal. Gray et al. (2005) contend that the rare species also follow a log normal pattern, albeit a truncated one. Of course truncated log normal and log series distributions show considerable overlap and are difficult to separate (Magurran 2004). It is clear that much remains to be learnt about the distribution of rare species. PARALLELS BETWEEN TEMPORAL AND SPATIAL PATTERNS OF ABUNDANCE AND OCCURRENCE Only a handful of studies examine species abundances over time, and just a few of these relate abundance to species frequency. In contrast, spatial patterns of abundance and occurrence have been extensively investigated. However, there are striking parallels in the relationship between occurrence and abundance over both time and space. This suggests that the patterns are interrelated. Williams (1964, p. 279) has a short, but fascinating section in Patterns in the balance of nature entitled: ÔThe relation between the abundance of animals and their regularity of appearance at different timesõ. His data on winter bird counts (see Fig. 1a,b) are similar to those of our Bristol Channel fish in that species that occur frequently are also abundant. In addition (Fig. 1c), there are more species that are either persistent (occurring on most sampling occasions) or infrequent (occurring in just a few sampling occasions) than those with intermediate occurrence patterns. Williams draws an intriguing parallel with Raunkaier s (1934) classification of plant species based on the number of quadrats they occur in. Once again there is a twin-peaked distribution of species one cluster of occasional taxa and another of frequent ones (Fig. 1d,e). And in both cases infrequent species tend to have low abundance when they do occur. Williams shows how Raunkaier s spatial occurrence distribution is related to the diversity (measured as Fisher s alpha)

4 350 A. E. Magurran Idea and Perspective (a) (b) (c) (d) (e) Figure 1 There are interesting parallels in occurrence and abundance patterns over time and space. (a) Abundance of species in relation to their frequency of occurrence (over 26 counts). The graph shows the abundance of bird species in winter counts, near London, between 1931 and 1937 data from Table 138 in Williams (1964). (b) Rank abundance graph of the same data. Species that occur 13 times in the record are denoted by an open symbol, those 12 times by a closed symbol. Species that occur infrequently have the lowest abundances. The split point was chosen by selecting the midpoint of the time series. As with the Bristol Channel fish time series analysed by Magurran & Henderson (2003), other split points near the median occurrence point would give broadly similar results. (c) Frequencies of species in this data set are shown in relation to the number of occasions on which they were recorded. This graph uses the plotting method developed by Raunkaier (1934) to illustrate the distribution of species amongst quadrats. The two-peaked, or ÔJÕ curve, that is produced is similar to those often seen when spatial data are analysed. Examples of spatial patterns are provided in (d) and (e). (d) The percentage of species in each of Raunkaier s five quadrat groups given either more small quadrats or fewer larger quadrats, and low diversity (Fisher s alpha ¼ 3.94). (e) As (d) but with higher diversity (alpha ¼ 5.50). Both graphs are based on Table 33 in Williams (1964). Two peaks are apparent in all cases though their relative magnitude is dependent on both quadrat size and community diversity. of the assemblage (Fig. 1d,e). In the same way I would expect the degree of bimodality in a temporal distribution to vary with factors such as the size of the assemblage, the length of the time series and the immigration rate. (The practical implications of these observations, in the context of sampling, are addressed in Sampling issues.) Hanski s (1982) core satellite hypothesis is a more recent, and much better known approach to analysing the distribution patterns of species. This metapopulation model predicts a bimodal pattern of patch occupancy by species. Core species are widespread and abundant, while satellite species are restricted in their distribution and are rare

5 Idea and Perspective Species abundance distributions over time 351 (Hanski & Gyllenberg 1993). Time, along with spatial scale and habitat heterogeneity is thought to influence the shape of species occupancy distributions (McGeoch & Gaston 2002). Gibson et al. (2005), for example, noted temporal shifts in the level of bimodality of species occupancy in an old-field secondary succession. However, there has been little discussion, from a metapopulation perspective, of temporal core satellite species occurrence patterns (but see Guo et al. 2000). This is a topic that clearly warrants further research. It could just be a coincidence that similar bimodal distributions of occurrence are found in time and space and that in both cases the core species are abundant, and the occasional (or satellite) species rare. It is also possible that the same processes underpin spatial and temporal species distributions. Hanski & Gyllenberg (1997) argued that two common patterns in ecology the species area curve and the distribution abundance curve (that is the core satellite pattern) are predicted by the same model. This model blends two formerly competing explanations for species distributions, the habitat heterogeneity and the extinction colonization hypotheses. Given the compelling evidence for equivalence in species area and species time curves it would be interesting to ask whether Hanski & Gyllenberg s model can replicate temporal patterns as well as spatial ones. In the same vein there has been progress towards a common explanation for species area relationships and species abundance distributions (Harte et al. 1999; Pueyo 2006). Can this approach be extended to link the species time relationship and species abundance distribution? SAMPLING ISSUES Although Preston, Williams and their contemporaries were aware that sampling was important, the extent to which sampling artefacts can account for apparently pervasive patterns has been underappreciated. One example of this is Ôlog-left-skewÕ in unveiled species abundance distributions. Log-left-skew means that there are more rare species than would be expected for a symmetrical log normal. MacArthur (see Hutchinson 1967) first drew attention to this phenomenon, but it was Nee et al. (1991) who set down the challenge of left-skewness as something that species abundance distributions must account for. Candidate explanations include Sugihara s niche subdivision model (Sugihara 1980; Nee et al. 1991), along with the zero-sum neutral (Hubbell 2001) and self-similarity (Harte et al. 1999; see also Pueyo 2006) models. Southwood (1996) commented on the fact that a high number of vagrant species can modify the shape of a log normal distribution so that leftskew is more apparent. Dividing an assemblage into frequent and infrequent components is further way of explaining negative skew (Magurran & Henderson 2003). McGill (2003a) has convincingly demonstrated that logleft-skew can be an artefact of sampling. He used Monte Carlo simulations to examine the consequences of repeatedly taking small samples from an unskewed assemblage. This simulates what happens when the same site is sampled repeatedly over time, or when multiple small samples are taken at the same time. McGill found that left-skewness becomes increasingly apparent as more samples are added and attributes this result to autocorrelation between the repeated samples. Because species with low abundance are found in just a small fraction of samples their relative abundance decreases as sample size increases, creating the impression that there is an excess of very rare species, and hence left-skew. As McGill pointed out, left-skew is most noticeable in species abundance distributions based on long time series. For the same reason I would expect the separation between the core and occasional species in the Bristol Channel fish, and in other communities, to become more pronounced as sampling continues. Moreover, recognition that there is autocorrelation during sampling does not invalidate the observation that there are distinct biological groupings of species in an assemblage. We were fortunate to have enough biological information about our estuarine fish species to show that core and occasional species have different ecological requirements. In other less well characterized assemblages it would be interesting follow Ulrich & Zalewski s (2006) example and test for ecological differences (e.g. body size, thermal ecology, feeding specializations) between species assigned to core and occasional categories based on their temporal occurrence in the record. This knowledge could be used to make predictions about shifts in the abundance patterns of individual species, such as a switch from being an occasional to being a core species, attendant on extrinsic events, for instance climate change (Magurran & Henderson 2003) or intrinsic events such as succession (Gibson et al. 2005). The methods used to quantify species abundances can vary considerably, even within a single study. For example, the British breeding birds data set, which is widely used in tests of species abundance distributions, is a mixture of counts for rarer species and estimates of abundance for common ones (Williamson & Gaston 2005). Connolly et al. (2005) found that the conclusions drawn about species abundance distributions depend on the way abundance is measured. Log normal distributions of fish and corals were unveiled at different scales when biomass and numerical abundance measures were used. In the same way, the prominence, and shape, of the core species distribution, relative to the occasional one, will be influenced by the type, or types, of abundance measure adopted. Binning method (Gray et al. 2006) will also affect conclusions about the contributions of core and occasional species.

6 352 A. E. Magurran Idea and Perspective TIME SCALES AND SPECIES ABUNDANCE DISTRIBUTIONS As noted earlier, Preston identified three time scales relevant to the study of species abundance distributions. These are sampling duration, that is the period over which data are collected, ecological time, during which the composition and possibly the shape of the species abundance distribution will change as a result of succession, immigration and other dynamic processes, and evolutionary time when new forms arise through speciation or are removed by extinction. Much of the discussion about species abundance distributions relates to the first two time scales. In practice they are not always readily separable. Sampling programmes attempt to accurately characterize the underlying abundance distribution. Sampling over a longer period helps achieve this goal as it allows the investigator to include taxa that are temporarily absent from the assemblage, such as migrant or seedbank species, or ones that were not detected during the initial survey. However, species turnover is continuous, even in communities that are not undergoing directional change. It follows that the total number of species recorded at a given locality will rise with time. This is the process that Preston invoked when he made predictions about species time curves. There is a fine line between sampling thoroughly enough to uncover the true abundance distribution and sampling in a way that will pick up the inevitable temporal changes in the assemblage. Abrupt changes in structure do not necessarily indicate the latter. McGill (2003a) cautioned that marked shifts in the degree of log-left-skew can occur simply because accumulated samples are not independent. Autocorrelation must be excluded as an explanation before ecology can be invoked. Equally, small changes over short periods can be part of a long-term trend. Thibault et al. (2004) characterized the rank abundance and rank energy distributions of a desert rodent community over 25 years. Both distributions were found to change directionally with time. These changes were not driven by richness, which remained constant during the study period, but could be linked to variation in habitat characteristics. Year by year differences in abundance and energy distributions were small; their contribution to the overall changes only became apparent when the full data set was analysed. It is also important to remember that the two methods of tracking species abundances over time provide different perspectives on the underlying abundance distribution. Accumulating species, and their abundances, over successive sampling events (Magurran & Henderson 2003) can be used to distinguish core and occasional species. Examining the species abundance distribution at discrete intervals along a time series (Thibault et al. 2004) will reveal changes in structure and composition which can then be characterized as either directional related, for example to succession (e.g. Bazzaz 1975) or anthropogenic change (e.g. Gray 1987; Southwood et al. 2003; Bhat & Magurran 2006), or nondirectional, reflecting baseline turnover in the community. Finally, although Preston recognized that species abundance distributions can be assessed across sampling, ecological and evolutionary time, a comprehensive analysis of the changes in the size, shape and composition of communities associated with each of these time scales is still awaited. CONCLUSIONS This article is essentially a plea for more consideration of temporal factors when investigating species abundance distributions. I have highlighted some of the many topics that warrant further investigation. In particular, I have stressed the observation that assemblages can be partitioned into common and rare components based on their temporal occurrence in the record, and drawn parallels with spatial partitions. It will be very interesting to see whether the hypothesis that an assemblage can be dissected into core and occasional species groups is supported by other data sets and whether there is, as suggested above, a link with core satellite occurrence patterns. I also believe that recognition of different subsets of species within a species abundance distribution will permit more meaningful tests of species abundance models. 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