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Abstract Estimating the strength of interactions among species in natural communities has always been a challenge for empirical ecologists. Sessile organisms, like plants or corals, often occur in metacommunities where they compete only with their immediate neighbours but disperse propagules over a wider area. To estimate the strength of competitive interactions, ecologists often count abundances in cells on a spatial grid for at least two time‐points. This data is then analysed using regression, by modelling the change in population size as a function of local densities, using cells as independent data‐points: a technique known as space‐for‐time substitution. These analyses generate estimates of competition coefficients; however, the method ignores dispersal among cells. To determine the impact of ignoring dispersal, we derived the bias that would arise when we apply regression methods to a metacommunity in which a fraction of seeds disperse beyond their natal cells but this dispersal is ignored in the model fitting process. We present results from a range of population models that make different assumptions about the nature of competition and assess the performance of our bias formulae by analysing data from simulated metacommunities. We reveal that: estimates of competition coefficients are biased when dispersal is not properly accounted for; and the resulting bias is often correlated with abundance, with rare species suffering the greatest overestimation. We also provide a standardized metric of competition that allows the bias to be calculated for a broad range of other population models. Our study suggests that regression methods that ignore dispersal produce biased estimates of competition coefficients when using space‐for‐time substitution. Our analytical bias formula allows empirical ecologists to potentially correct for biases, but it requires either tailored experiments in controlled conditions or an estimate of the average dispersal rate in a natural community, so may be challenging to apply to real datasets.

Estimating the strength of interactions among species in natural communities has always been a challenge for empirical ecologists. Sessile organisms, like plants or corals, often occur in metacommunities where they compete only with their immediate neighbours but disperse propagules over a wider area. To estimate the strength of competitive interactions, ecologists often count abundances in cells on a spatial grid for at least two time‐points. This data is then analysed using regression, by modelling the change in population size as a function of local densities, using cells as independent data‐points: a technique known as space‐for‐time substitution. These analyses generate estimates of competition coefficients; however, the method ignores dispersal among cells.To determine the impact of ignoring dispersal, we derived the bias that would arise when we apply regression methods to a metacommunity in which a fraction of seeds disperse beyond their natal cells but this dispersal is ignored in the model fitting process. We present results from a range of population models that make different assumptions about the nature of competition and assess the performance of our bias formulae by analysing data from simulated metacommunities.We reveal that: estimates of competition coefficients are biased when dispersal is not properly accounted for; and the resulting bias is often correlated with abundance, with rare species suffering the greatest overestimation. We also provide a standardized metric of competition that allows the bias to be calculated for a broad range of other population models.Our study suggests that regression methods that ignore dispersal produce biased estimates of competition coefficients when using space‐for‐time substitution. Our analytical bias formula allows empirical ecologists to potentially correct for biases, but it requires either tailored experiments in controlled conditions or an estimate of the average dispersal rate in a natural community, so may be challenging to apply to real datasets. [ABSTRACT FROM AUTHOR]

Abstract: In this review, we compiled published results on biological interactions at different spatial scales in the Monte desert of Argentina and identified gaps in current knowledge. We presented evidence of competitive and facilitative plant–plant conspecific and heterospecific interactions, and plant–soil–microbes interactions in relation to the abiotic environment at the fine patch-scale. We also showed evidence of animal–animal interactions and plant–animal interactions at the community scale through study cases involving both native and introduced herbivores. Moreover, we identified bottom-up and top-down forces governing the interactions between granivores (birds, ants, and small mammals) and seed availability/production at the community scale. At the landscape scale, we discussed feedbacks between domestic grazers and the spatial patterns of resources and their interrelationships with processes occurring at other scales. We concluded that research has steadily increased during the last 6 years but knowledge on biological interactions in the Monte desert is still scarce, particularly at a landscape scale. [Copyright &y& Elsevier]