The ability to predict changes in the abundances of the species in ecological communities is essential for sustainable management, biodiversity conservation, and community restoration.

We propose a framework to predict such changes. We test our method, which uses the linear Lotka-Volterra equations (LLVE) as well as other empirical predictors (linear least squares regression, quadratic extrapolation, simple exponential smoothing), against the measured abundances of trees from the long-term 50-ha plot on Barro Colorado Island (BCI), along eight censuses.

To obtain the parameters of the LLVE -the intrinsic growth rate r and the carrying capacity K of each species and the interspecific interaction matrix A- we first estimate A through the Maximum Entropy (MaxEnt) method. Next, using A as input, we fit r and K. Then, feeding the LLVE with these parameters, we obtain predicted species trajectories along censuses. Since for this particular community the interspecific interaction coefficients are much smaller than the intraspecific ones, keeping only intraspecific competition is enough to predict the evolution of the abundances of several tree species, i.e. the LLVE reduce to a set of uncoupled logistic equations. However, this simplification is not a requirement of the method. We define P-values to establish when the predicted trajectory for a species is statistically significant; this is crucial in determining the set of species over which a particular predictor can be meaningfully applied.

To illustrate a possible application of the method, we present our predictions for the abundances of tree species for the currently underway BCI 2020 census, which provide warnings regarding species that are likely to experience important population loss.

预测生态社区中物种丰富度变化的能力对于可持续管理，生物多样性保护和社区恢复至关重要。

我们提出了一个框架来预测这种变化。我们测试了我们的方法，该方法使用线性Lotka-Volterra方程（LLVE）以及其他经验预测变量（线性最小二乘回归，二次外推，简单指数平滑）针对长期50公顷的树木丰度进行了测量。沿八次人口普查在Barro科罗拉多岛（BCI）上进行绘制。

为了获得LLVE -the固有生长速率的参数- [R和承载能力ķ每个物种的和种间相互作用矩阵甲-我们首先估计阿通过最大熵（最大墒）方法。接下来，使用A作为输入，我们拟合r和K。然后，用这些参数输入LLVE，我们沿着人口普查获得了预测的物种轨迹。由于对于该特定群落，种间相互作用系数比种内相互作用系数小得多，因此仅保持种内竞争就足以预测几种树种的丰度演化，即LLVE简化为一组未耦合的逻辑方程。但是，这种简化不是该方法的要求。我们定义P值来确定物种的预测轨迹在统计上是否显着。这对于确定可以有意义地应用特定预测变量的物种集至关重要。

为了说明该方法的可能应用，我们提出了对当前正在进行的BCI 2020人口普查的树木物种丰富度的预测，这些预测提供了有关可能经历重要种群丧失的物种的警告。