Spatial synchrony in population dynamics of West African fishes: a demonstration of an intraspecific and interspecific Moran effect

Abstract

Summary1. Synchronous fluctuations of the abundance of several populations at a regional scale can be a sign of a climatic effect on their dynamics (Moran effect). However, the interpretation of spatial synchrony is often complicated by the interaction between climatic disturbances and migrations between the studied populations. 2. We addressed this question by studying 24-year time-series of abundance estimates for four populations of four fish species in three different catchment basins in Côte d'Ivoire (West Africa). As localities from two different basins cannot exchange individuals, dispersal cannot be considered a synchronizing factor in this system. 3. A sign test performed on the four site × site correlation matrices rejected the null hypothesis of no spatial synchrony. No relation was detected in relating the level of synchrony in species dynamics ( r = 0·58 on average) to the geographical distances between the four sites (176-367 km). A more detailed analysis taking account of sampling error was carried out between two sites, and confirmed the above results. When corrected for sampling noise, high values of spatial synchrony were obtained ( r = 0·88 on average). 4. Interspecific spatial synchronies ( r = 0·57 on average) were almost as high as intraspecific values, suggesting that the four species are reacting in the same way to the same Moran effect. 5. A good correlation ( r = 0·54) between a regional river discharge index and the total catch of the four species 1 year later was observed, suggesting that hydrological variability is one of the main synchronizing factors. Moreover, as expected on theoretical grounds, spatial synchrony in river discharge ( r = 0·84 and 0·88 on a log scale) is similar to spatial synchrony in population dynamics ( r = 0·88 on average and corrected for sampling error).Key-words : disconnected populations, hydrology, Moran effect, sampling error, within and between species synchrony.

Large-scale climate variability and rodent abundance modulates recruitment rates in Willow Ptarmigan (Lagopus lagopus)

“…There might be several reasons for this: First, this might be related to the scale at which the rodent and NAO MJJ are collected. As for the local climate data, the rodent data were measured at a finer scale than the NAO index, and might then be more vulnerable to demographic stochasticity and sampling variation (Tedesco et al 2004). Strong correlation between rodent trap sites, however, suggests that this is not the case for this data.…”

“…see ''Rodent abundance data'' in the methods) and the average correlation was lower within than between regions. This local variation could arise from variation in local factors such as habitat and predation rates (Tavecchia et al 2008), but the pattern may also be due to the small size of local populations and low sample sizes making them more vulnerable to demographic stochasticity and sampling variation (Tedesco et al 2004).…”

“…Spatial autocorrelation in predation (Ims and Andreassen 2000) or weather (Moran 1953;Grenfell et al 1998;Kvasnes et al 2010) across large areas can potentially force demographic rates of spatially structured populations into synchrony. Although adjacent populations may experience similar weather events, their dynamics can, nonetheless, be out of phase due to differences in local factors such as predator density or habitat (Tavecchia et al 2008), or as an effect of sampling variation or demographic stochasticity, which may be larger on smaller scales due to reduced sample sizes (Tedesco et al 2004;Lande et al 2003).…”

Stage-dependent spatial synchrony revealed for fish populations in the Garonne River (SW France)

“…CattanØo et al (2003), in a study of 57 populations of brown trout (Salmo trutta L.), found a Moran effect on early-life stages due to high flows during emergence periods. Tedesco et al (2004) demonstrated a strong Moran effect in four African riverine fish populations using a 24-year timeseries dataset, which clearly acted through regional hydrological variability. For the same populations, Tedesco and Hugueny (2006) highlighted that spatial synchrony was dependent on life-history strategies.…”

“…The configuration of the sampling team was always the same to minimize inter-annual bias in sampling efficiency. This is of particular importance for synchrony studies, where the focus is made on the temporal variation in population dynamics and sampling noise may blur observed patterns (Tedesco et al, 2004). One person held the fishing apparatus, two people caught the shocked fish with landing nets, and one person collected the fish, putting them in oxygenated plastic tanks on the river bank.…”