物种分布建模示例简介

对物种的地理分布进行建模是保护生物学中的一个重要问题。在此示例中，根据过去的观察结果和14个环境变量，我们对两个南美哺乳动物的地理分布进行了建模。由于只有正例没有负例(没有不成功的观察结果)，不方便做具有显示正负例的有监督机器学习，因此我们将此问题转化为密度估计问题，并使用由package basemap绘制出了南美的海岸线和国界。

示例中使用的2个物种是：

“Microryzomys minutus”，也称为“森林小稻鼠”，一种啮齿动物，生活在秘鲁、哥伦比亚、厄瓜多尔、秘鲁和委内瑞拉。

参考文献

“Maximum entropy modeling of species geographic distributions”(物种地理分布的最大熵建模)S.J. Phillips，R.P.Anderson，R.E.Schapire-Ecological Modelling，190：231-259，2006。

代码实现[Python]

# -*- coding: utf-8 -*-

# Authors: Peter Prettenhofer

# Jake Vanderplas

#

# License: BSD 3 clause

from time import time

import numpy as np

import matplotlib.pyplot as plt

from sklearn.datasets.base import Bunch

from sklearn.datasets import fetch_species_distributions

from sklearn.datasets.species_distributions import construct_grids

from sklearn import svm, metrics

# 如果basemap可用，则使用basemap绘制边界；否则自行处理实现。

try:

from mpl_toolkits.basemap import Basemap

basemap = True

except ImportError:

basemap = False

print(__doc__)

# 创建物种种群：基于物种名从训练集和测试集提取信息

def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):

"""Create a bunch with information about a particular organism

This will use the test/train record arrays to extract the

data specific to the given species name.

"""

bunch = Bunch(name=' '.join(species_name.split("_")[:2]))

species_name = species_name.encode('ascii')

points = dict(test=test, train=train)

for label, pts in points.items():

# choose points associated with the desired species

pts = pts[pts['species'] == species_name]

bunch['pts_%s' % label] = pts

# determine coverage values for each of the training & testing points

ix = np.searchsorted(xgrid, pts['dd long'])

iy = np.searchsorted(ygrid, pts['dd lat'])

bunch['cov_%s' % label] = coverages[:, -iy, ix].T

return bunch

# 绘制种群分布

def plot_species_distribution(species=("bradypus_variegatus_0",

"microryzomys_minutus_0")):

"""

Plot the species distribution.

"""

if len(species) > 2:

print("Note: when more than two species are provided,"

" only the first two will be used")

t0 = time()

# Load the compressed data

data = fetch_species_distributions()

# Set up the data grid

xgrid, ygrid = construct_grids(data)

# The grid in x,y coordinates

X, Y = np.meshgrid(xgrid, ygrid[::-1])

# create a bunch for each species

BV_bunch = create_species_bunch(species[0],

data.train, data.test,

data.coverages, xgrid, ygrid)

MM_bunch = create_species_bunch(species[1],

data.train, data.test,

data.coverages, xgrid, ygrid)

# background points (grid coordinates) for evaluation

np.random.seed(13)

background_points = np.c_[np.random.randint(low=0, high=data.Ny,

size=10000),

np.random.randint(low=0, high=data.Nx,

size=10000)].T

# We'll make use of the fact that coverages[6] has measurements at all

# land points. This will help us decide between land and water.

land_reference = data.coverages[6]

# Fit, predict, and plot for each species.

for i, species in enumerate([BV_bunch, MM_bunch]):

print("_" * 80)

print("Modeling distribution of species '%s'" % species.name)

# Standardize features

mean = species.cov_train.mean(axis=0)

std = species.cov_train.std(axis=0)

train_cover_std = (species.cov_train - mean) / std

# Fit OneClassSVM

print(" - fit OneClassSVM ... ", end='')

clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.5)

clf.fit(train_cover_std)

print("done.")

# Plot map of South America

plt.subplot(1, 2, i + 1)

if basemap:

print(" - plot coastlines using basemap")

m = Basemap(projection='cyl', llcrnrlat=Y.min(),

urcrnrlat=Y.max(), llcrnrlon=X.min(),

urcrnrlon=X.max(), resolution='c')

m.drawcoastlines()

m.drawcountries()

else:

print(" - plot coastlines from coverage")

plt.contour(X, Y, land_reference,

levels=[-9998], colors="k",

linestyles="solid")

plt.xticks([])

plt.yticks([])

print(" - predict species distribution")

# Predict species distribution using the training data

Z = np.ones((data.Ny, data.Nx), dtype=np.float64)

# We'll predict only for the land points.

idx = np.where(land_reference > -9999)

coverages_land = data.coverages[:, idx[0], idx[1]].T

pred = clf.decision_function((coverages_land - mean) / std)

Z *= pred.min()

Z[idx[0], idx[1]] = pred

levels = np.linspace(Z.min(), Z.max(), 25)

Z[land_reference == -9999] = -9999

# plot contours of the prediction

plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)

plt.colorbar(format='%.2f')

# scatter training/testing points

plt.scatter(species.pts_train['dd long'], species.pts_train['dd lat'],

s=2 ** 2, c='black',

marker='^', label='train')

plt.scatter(species.pts_test['dd long'], species.pts_test['dd lat'],

s=2 ** 2, c='black',

marker='x', label='test')

plt.legend()

plt.title(species.name)

plt.axis('equal')

# Compute AUC with regards to background points

pred_background = Z[background_points[0], background_points[1]]

pred_test = clf.decision_function((species.cov_test - mean) / std)

scores = np.r_[pred_test, pred_background]

y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]

fpr, tpr, thresholds = metrics.roc_curve(y, scores)

roc_auc = metrics.auc(fpr, tpr)

plt.text(-35, -70, "AUC: %.3f" % roc_auc, ha="right")

print("\n Area under the ROC curve : %f" % roc_auc)

print("\ntime elapsed: %.2fs" % (time() - t0))

plot_species_distribution()

plt.show()

代码执行

代码运行时间大约:0分20.185秒。

运行代码输出的文本内容如下，分别给出了2个物种的OneClassSVM建模AUC结果。

________________________________________________________________________________

Modeling distribution of species 'bradypus variegatus'

- fit OneClassSVM ... done.

- plot coastlines from coverage

- predict species distribution

Area under the ROC curve : 0.868443

________________________________________________________________________________

Modeling distribution of species 'microryzomys minutus'

- fit OneClassSVM ... done.

- plot coastlines from coverage

- predict species distribution

Area under the ROC curve : 0.993919

time elapsed: 20.18s

运行代码输出的图片内容如下，图中2个物种的分布密度情况一目了然。

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