Skip to main content

Table 4 Estimates of spatial autocorrelation and the appropriate spatial detrending model for all 15 forest types

From: Large-scale forest inventories of the United States and China reveal positive effects of biodiversity on productivity

Forest type

Spatial autocorrelation estimates

Detrending model

Local spatial autocorrelation1

Range of spatial autocorrelation (km)2

Parameters3

n

Pinyon/juniper

0.33

1,302.7

All

16,709

Douglas-fir

0.45

260.2

All

7,077

Oak/pine

0.21

490.0

All

19,023

Oak/gum/cypress

0.16

597.4

All

28,431

Elm/ash/cottonwood

0.51

617.5

All

11,742

Aspen/birch

0.13

536.6

All

46,411

Southern pine

0.19

617.0

All

102,844

Oak/hickory

0.14

524.0

x, y, x 2, y 2

141,062

Maple/beech/birch

NS

NS

NS

47,350

Tropical and exotic hardwoods

0.35

404.6

All

408

Northern pines

0.08

219.7

x, y, x 2, y 2

9,151

Spruce/fir and exotic softwoods

NS

NS

NS

18,761

Western conifers

0.39

973.2

x, xy, y 2

21,792

Western hardwoods

0.65

1,167.8

x, x 2, y 2

1,924

Western oak

0.37

990.7

x, y, xy, y 2

3,207

  1. 1Empirical mean (from 200 bootstrapped simulations) of the estimate of local autocorrelation as the distance between sampling locations approaches 0
  2. 2Mean distance (from 200 bootstrapped simulations) of the lag distance at which the estimate of local autocorrelation = 0
  3. 3Full model parameters = x, y, xy, x 2, and y 2
  4. NS not significant