Ecol Evol 2025 Jun 18;156:e71603. doi: 10.1002/ece3.71603.

Using the Size Structure of Populations to Infer Range Dynamics and the Frequency of Recruitment.

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Climate change is causing shifts in the distributional ranges of species. Often, it is hard to evidence that a range shift has happened; however, because we lack time-series data to track the distributional change of species. In Australia, Centrostephanus rodgersii (the Long-spined urchin) underwent a well-studied range extension southward to Tasmania, where it caused an ecosystem phase shift by overgrazing kelp. Centrostephanus rodgersii is also found in north-eastern New Zealand (NENZ), where it could have similar impacts. Unfortunately, there are no time-series data from which to infer population or recruitment dynamics of C. rodgersii in NENZ. To address this knowledge gap, we analysed the size structure (the mean and standard deviation of urchin sizes) of C. rodgersii populations across its geographic range using a Bayesian modelling approach. We expected that older populations with frequent recruitment would have larger mean sizes and larger standard deviations in sizes than more recently formed populations with less recruitment. We tested our model using a simulation study and population size-structure data from across C. rodgersii's extended range in Tasmania. We found a poleward decrease in mean sizes, consistent with the well-known range extension. When applied to NENZ, we recovered a poleward decrease in mean sizes across northern populations, suggesting a recent sequential, southward colonisation in this part of the species range. Our model also showed a poleward increase in the standard deviation of sizes across NENZ, suggesting a poleward increase in the frequency of recruitment. In contrast, the southern populations of Tasmania had smaller values indicative of more limited recruitment at the leading range edge. Our study demonstrates that size-structure data can be a valuable resource in understanding range dynamics and recruitment in the absence of time-series data.

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