Tips： 默认情况下会删除重复项（同一网格单元中同一物种的多个记录），可以通过设置取消
1、导入Samples（csv格式）、Environmental layers（asc格式/也可以直接选择包含asc文件的文件夹） 2、勾选
3、设置结果存储路径-Output directory 4、Setting 中 Random test percentage 设置为25，即随机留出25%的样本用于检验 5、单击Run
<randomly set aside 25% of the sample records for testing>
<The default output is logistic, which is the easiest to conceptualize: it gives an estimate between 0 and 1 of probability of presence.>
< It starts at 0 and increases towards an asymptote during the run. During this process, Maxent is generating a probability distribution over pixels in the grid, starting from the uniform distribution and repeatedly improving the fit to the data. The gain is defined as the average log probability of the presence samples, minus a constant that makes the uniform distribution have zero gain. At the end of the run, the gain indicates how closely the model is concentrated around the presence samples; for example, if the gain is 2, it means that the average likelihood of the presence samples is exp(2) ≈ 7.4 times higher than that of a random background pixel.>
这个图暂时不会解释，感觉是描述训练集与测试集模型的错误率，但x/y轴的含义都看不明白。
测试数据与训练数据不独立时，会出现test omission line 远低于 predicted omission line 的情况
<In some situations, the test omission line lies well below the predicted omission line: a common reason is that the test and training data are not independent, for example if they derive from the same spatially autocorrelated presence data.>
<If you use the same data for training and for testing then the red and blue lines will be identical. If you split your data into two partitions, one for training and one for testing it is normal for the red (training) line to show a higher AUC than the blue (testing) line.> <It is important to note that AUC values tend to be higher for species with narrow ranges, relative to the study area described by the environmental data. This does not necessarily mean that the models are better; instead this behavior is an artifact of the AUC statistic.>
解释时会用到的变量：
pre6190_ann 年降水量pre6190_l10 10月降水量pre6190_l1 1月平均降水量
这张图我得再啃啃其他文章再来补
<Each step of the Maxent algorithm increases the gain of the model by modifying the coefficient for a single feature; the program assigns the increase in the gain to the environmental variable(s) that the feature depends on. Converting to percentages at the end of the training process.>
<In our Bradypus example, annual precipitation is highly correlated with October and July precipitation. Although the above table shows that Maxent used the October precipitation variable more than any other, and hardly used annual precipitation at all, this does not necessarily imply that October precipitation is far more important to the species than annual precipitation.>
Tips: 如果环境变量是相关的，边际响应曲线可能是错误的。例如，如果两个密切相关的变量的响应曲线是接近相反的，那么对于大多数像素来说，两个变量的联合效应可能很小。
<Note that if the environmental variables are correlated, as they are here, the marginal response curves can be misleading.For example, if two closely correlated variables have response curves that are near opposites of each other, then for most pixels, the combined effect of the two variables may be small.we see that predicted suitability is negatively correlated with annual precipitation (pre6190_ann), if all other variables are held fixed. In other words, once the effect of all the other variables has already been accounted for, the marginal effect of increasing annual precipitation is to decrease predicted suitability. However, annual precipitation is highly correlated with the monthly precipitation variables, so in reality we cannot easily hold the monthly values fixed while varying the annual value.
以下每条曲线是通过只使用相应的变量而不考虑其他变量的模型生成的。
<each curve is made by generating a model using only the corresponding variable, disregarding all other variables>
<fitting so close to the training data that the model doesn’t generalize well to independent test data>
