Understanding the interannual effect of various environmental factors on biodiversity distribution is fundamental for developing biological monitoring tools. The interannual variability of environmental factors on presettlement fish assemblages (PFAs) has been so far under investigated, especially in Madagascar. Numerous explanatory variables including local hydro-dynamic conditions recorded during the sampling night, characteristics of the benthic substrate and remotely sensed oceanic conditions (RSOC) were used to explain the spatio-temporal variability of PFAs in southwestern Madagascar. Gradient forest analyses were used to hierarchically classify the effect of these explanatory variables on the PFAs for two sites and during two different recruitment seasons. RSOC variables appeared to better explain the PFAs than the local variable and the characteristics of the benthic substrate. The PFAs caught in water masses with coastal characteristics were better explained than those with open water characteristics. This spatial variability is hypothesised to be linked to differences in feeding conditions among water masses. The gradient forest analyses also highlighted the complexity of predicting PFAs as the species for which abundances were better explained by RSOC variables varied between years. This interannual variability was mainly explained by the interannual variation of chlorophyll a (Chl a) concentration, wind and surface current, with better prediction obtained during the year with high Chl a values associated with high averaged sea surface temperature. These findings suggest the importance of forecasting Chl a concentrations, taking into account the impact of tropical storms and climate variability in order to predict PFAs in the future.

中文翻译：

---

遥感海洋状况解释了预设热带热带鱼类种群的空间和年际变化

了解各种环境因素对生物多样性分布的年际影响是开发生物监测工具的基础。迄今为止，尚未调查环境因子在预设鱼类组合（PFA）上的年际变化，特别是在马达加斯加。大量采样变量包括采样夜记录的局部水动力条件，底栖基质特征和遥感海洋条件（RSOC）被用来解释马达加斯加西南部PFA的时空变化。使用梯度森林分析对这些解释变量对两个地点和两个不同招聘季节的PFA的影响进行分级分类。RSOC变量似乎比局部变量和底层基质的特征更好地解释了PFA。在具有沿海特征的水团中捕获的PFA比具有开放水特征的PFA更好。假设该空间变异性与水团之间的进食条件差异有关。梯度森林分析还强调了预测PFA的复杂性，因为通过RSOC变量随年份变化，可以更好地解释其丰度。这种年际变化主要是由叶绿素的年际变化解释的 假设该空间变异性与水团之间的进食条件差异有关。梯度森林分析还强调了预测PFA的复杂性，因为通过RSOC变量随年份变化，可以更好地解释其丰度。这种年际变化主要是由叶绿素的年际变化解释的 假设该空间变异性与水团之间的进食条件差异有关。梯度森林分析还强调了预测PFA的复杂性，因为通过RSOC变量随年份变化，可以更好地解释其丰度。这种年际变化主要是由叶绿素的年际变化解释的a（Chl a）浓度，风和地表水流，在这一年中，与高平均海面温度相关的高Chl a值可获得更好的预测。这些发现表明，考虑到热带风暴的影响和气候变异性，为了预测未来的PFA，预测Chl a浓度的重要性。