Lines in the sand

Dr. Suzanne Prober, Prof Richard Hobbs, Prof Hugh Possingham and I have recently had a paper entitled ‘Lines in the sand: quantifying the cumulative development footprint in the world’s largest remaining temperate woodland‘ published in the journal Landscape Ecology. You can view the article online here or contact me to send you a pdf and/or any appendices.

In the paper, we quantify cumulative anthropogenic development footprints for the Great Western Woodland and expose the large proportion of this that is made up of unmapped linear infrastructure. We highlight the crucial importance of explicitly accounting for the ecological impacts of linear infrastructure in impact evaluations – impacts that typically pass under the radar of impact evaluations.

We also present an analysis of key drivers of development footprint extent, both at the regional and landscape-patch levels, and provide key insights, such as the mitigative effect of pastoralism on development footprints in mining landscapes, and investigate the implications for edge effects. Our approach and methodology provide information and insights that are useful for cumulative and strategic impact assessment as well as landscape planning and conservation, and can be applied to other relatively intact landscapes worldwide.

Figure 3 JPEG way.png
Fig. 3 Contribution of different anthropogenic disturbance types to total direct development footprint, with some examples. a) Contribution of different types of infrastructure to total footprint. b) An example of ‘hub’ infrastructure: an abandoned gold mine. c) Aerial view showing both hub and linear infrastructure of a mine and associated exploration grids. d) Aerial view of exploration grids passing through shrubland and woodland vegetation, the white dots are drill pads. e) A mapped track leading to Helena-Aurora Range, one of the banded ironstone formations where mining is currently proposed. The track was probably initially built for mineral exploration purposes and is now used by miners, conservation agencies, and tourists. f) A ground-truthed unmapped track with abandoned exploration drilling sample bags to the left. An abandoned hydrocarbon drum was found further along this track.

Posted in blog, research, sustaining ecology | Tagged , , , , , , , , | Leave a comment

Overview of Keren’s PhD research

Enigmatic impacts of mining and linear infrastructure development in Australia’s Great Western Woodlands

Background and aims

Extensive developments, such as mining, and oil and gas extraction, in relatively intact landscapes, can have numerous ecological impacts that affect the ecosystems within which they are situated. These impacts are often poorly understood and even more poorly accounted for in impact evaluations, biodiversity offsets, and regional conservation or land-use plans that are intended to mitigate impacts and protect the ecological assets and natural values of the landscapes in question.

This research is directed at improving our ability to conceptualise and account for these ‘enigmatic’ impacts in order to improve the decisions that we make in planning, approving, managing and offsetting extensive developments in relatively intact landscapes, with a focus on mitigating the ecological effects of mining and exploration Western Australia’s Great Western Woodland.

The Great Western Woodlands

The Great Western Woodlands is an internationally significant area of great biological richness, owing partly to its 250 million year continuous biological heritage, its location at the interzone between the moist southwest corner of Australia and the arid interior, and its relative intactness. At 16 million hectares, the Great Western Woodlands is larger than England and represents the largest remaining temperate woodland on earth. The region comprises a mosaic of woodland; shrubland; mallee; casuarina and melaleuca thickets; rocky outcrops; halophytic vegetation; salt lakes; and banded ironstone formations, and is the driest place in which such tall woodlands grow. The area is home to almost one third of Australia’s eucalypt taxa and well over 3000 flowering plant species (more than twice the number that occur in the whole of the UK), as well as many species that occur nowhere else in the world.

The Great Western Woodlands is also a very rich and productive mineral province, with 134 operating mines and 119,303 ‘abandoned mines’ registered within its boundaries, as well as more than 5000 current mineral tenements covering more than 60% of the region. Mining in the region mainly targets gold, nickel and iron ore, but commercial quantities of silver, copper, cobalt, gypsum, salt, and construction materials are also extracted.

The Great Western Woodlands

This research consisted of:

  1. A review of ecological impacts that are frequently overlooked in impact evaluations but that continue to cause ecological loss and degradation, with a proposed framework for conceptualising and addressing these issues published in the highly-ranked journal Trends in Ecology and Evolution.
  2. A spatial analysis of ground disturbance in the Great Western Woodlands using GIS and remote sensing to identify and characterise cumulative impacts and areas within disturbance buffers, as well as associations between disturbed areas and disturbance types, land tenure, tenement history, and selected environmental values.  A key outcome of this work has been the identification of roads, tracks and other linear infrastructure corridors as major components of the disturbance regime, despite their impacts being particularly poorly understood (the following sections go some way toward addressing this knowledge gap).
  3. An observational field investigation using motion-sensor cameras and spoor (scats, prints, etc.) surveys to understand the effects of roads and tracks on predator activity within relatively intact landscapes.
  4. A survey of ephemeral drainage lines, erosional features, and water pooling features, and their association with linear infrastructure corridors to characterise and quantify the type and extent of impacts of linear infrastructure on water movement across landscapes.

Prudent strategic assessment and comprehensive mitigation that accounts for all impacts — even enigmatic ones — could provide improved environmental and land-use planning outcomes while potentially benefiting development proponents by providing greater upfront guidance and certainty of access to specified areas, and enhancing their ‘social licence to operate’.

This research contributes to efforts to conserve in perpetuity a relatively intact ecosystem that dates back to Gondwanan times and is internationally significant for its biodiversity and wilderness values. It is also an area cherished by its traditional owners in the cultural and spiritual connections they have with the land, and by many others who prize the region and its unique landscape.

This PhD project was supervised by Professor Richard Hobbs (University of Western Australia), Dr Suzanne Prober (CSIRO), Professor Hugh Possingham (University of Queensland), Dr Leonie Valentine and Dr Kerrie Wilson (University of Queensland), and has been supported by the UWA Gledden Postgraduate Research Scholarship, the Australian Research Council’s Centre of Excellence for Environmental Decisions, the National Environmental Research Program Environmental Decisions Hub, and The Wilderness Society. It runs in association with the Terrestrial Environmental Research Network’s Great Western Woodlands Supersite and the work of GondwanaLink and Pew Trusts.

Keren’s full PhD thesis is available at:

 See my publications for papers and datasets that have emerged from this research.
Posted in blog | Leave a comment

Being quoted

I just came across an interesting book recently published by Random House in the UK called Linescapes: Remapping and Reconnecting Britain’s Fragmented Wildlife, by Hugh Warwick. In the book, the author discussed some of my research, published a few years ago in an article called ‘Under the radar: mitigating enigmatic ecological impacts‘ (contact me if you’d like a copy).

I really like the discussion, even though he’s misquoted me a little (still, the point I was making is unchanged). What a nice feeling to see my research quoted as an example of ‘recent thinking’ and incorporated into the global discourse in such an obviously well-researched and thoughtful book. I hope to have more of my reasearch on the ecological implications of the lines that we draw through wild landscapes published soon too!

Click on this link to see the discussion:


Here is the book’s blurb: It is rare to find a landscape untouched by our lines – the hedges, walls, ditches and dykes built to enclose and separate; and the green lanes, roads, canals, railways and power lines, designed to connect. This vast network of lines has transformed our landscape.
In Linescapes, Hugh Warwick unravels the far-reaching ecological consequences of the lines we have drawn: as our lives and our land were being fenced in and threaded together, so wildlife habitats have been cut into ever smaller, and increasingly unviable, fragments.
I’d love to hear any comments from anyone who has read it!

Posted in blog | Tagged , , , | Leave a comment

Jungkajungka Woodlands Festival

jungkajungka_poster_final.jpgDon’t miss being a part of the inaugural Jungkajungka Woodlands Festival, held over Easter in Norseman, Western Australia—the Heart of the Great Western Woodlands. This event is organised by the Wilderness Society in collaboration with the Shire of Dundas, GondwanaLink, and with support from a number of other organisations.

This is Ngadju country and the festival organisers acknowledge the traditional owners of this part of the Great Western Woodlands and thank them for co-hosting the festival.

I’ll be talking as part of the presentations on Saturday afternoon on Woodlands Knowledge.

For more information, including how to register (for free), go to:

or click here to download the full program.

Posted in blog | Tagged , , , , , , , | Leave a comment

The Great Western Woodlands: a biological wonderland, a poem, a movement

I’m pleased to share that I have had one of my poems used as the voiceover for the Wilderness Society’s Great Western Woodlands campaign that they launched last month. The poem is called Biological Cornucopia and is one of a suite of  poems that I wrote about the enigmatic ecological impacts of mining and associated linear infrastructure development in the Great Western Woodlands, the subject of the PhD that I completed last year.

My PhD was focused on the conservation of large, relatively intact landscapes in the face of widespread development such as resource extraction: a challenge of global conservation significance. In particular, ‘enigmatic’ ecological impacts that commonly evade consideration in conservation strategies invariably pervade such landscapes. Keren investigated the significance and ecological implications of linear infrastructure (e.g. roads, tracks and railways) largely associated with mining activity in the largest and most intact remaining temperate woodland on earth. Keren discovered significant effects on attributes of key ecosystem processes, including predator activity and water movement and recommends ways in which these impacts could be ameliorated.

Great cudos to Amy Matheson for excellent editing, and to the amazong team at the Wilderness Society for their great work on the campaign.

If you’re inspired to experience the Great Western Woodlands, consider joining the inaugural Jungka Jungka Woodlands Festival to be held in Norseman in April.

Posted in blog | Leave a comment

The Great Western Woodlands Campaign Launch: 3rd February 2017


All are invited to join the Wilderness Society for a special celebration of the Great Western Woodlands (GWW), one of West Australia’s most significant natural spaces. They’ll be launching a new campaign for the Great Western Woodlands and showcasing the new GWW website and video.

The GWW is the largest unfragmented woodland left on earth. It is vast, beautiful and unprotected. The GWW has remained home to a remarkable richness and diversity of plant and animal life. With over 3,300 flowering plant species, there are more native plants in the Great Western Woodlands than in the whole of Canada! This is a great opportunity to learn more about the GWW, why it’s under threat, and how you can help preserve it for all Australians.

This event will also exhibit work from local artists with a focus on natural spaces and the Australian outback including some  stunning photos of the GWW. I’ll be giving a presentation about the incredible environmental values of the GWW and my research in it, and also presenting a poem that I wrote about my experience doing field research in the GWW and reflections on the magnificent Helena Aurora Range and the proposal to mine it.

RSVP by Wednesday the 1st of February and…


Friday the 3rd of February 2017.
6.30 – 8.30pm.
North Perth Town Hall.
26 View St, North Perth WA 6006.

Food and drinks provided.

RSVP at:

Posted in blog | Leave a comment

In defense of science

As a government scientist in Australia, I stand in solidarity with those around the world who are restricted from enabling informed decisions

Being A Better Scientist

I (Pleuni Pennings) endorse the following, which was drafted by Graham Coop (UC Davis), Michael Eisen (UC Berkeley) and Molly Przeworski (Columbia):

We are deeply concerned by the Trump administration’s move to gag scientists working at various governmental agencies. The US government employs scientists working on medicine, public health, agriculture, energy, space, clean water and air, weather, the climate and many other important areas. Their job is to produce data to inform decisions by policymakers, businesses and individuals. We are all best served by allowing these scientists to discuss their findings openly and without the intrusion of politics. Any attack on their ability to do so is an attack on our ability to make informed decisions as individuals, as communities and as a nation.

If you are a government scientist who is blocked from discussing their work, we will share it on your behalf, publicly or with the appropriate recipients…

View original post 6 more words

Posted in blog | Leave a comment

Mixed-model analysis of a percentage outcome using logit transformation in R

Warning: this post is terribly technical. Readers not inclined towards statistical analysis and R coding may wish to skip this one!

I was recently contacted by a biologist in the UK regarding one of the analyses that I did in my PhD. This researcher was trying to model percentage data with mixed effects models and was struggling to find examples of where this had been done before to base his analysis on, except for the analysis that I performed to identify drivers of disturbance at a patch level in the Great Western Woodlands. I remember that at the time, I really struggled to figure out the best way to perform the analysis, it was so complicated and there were issues that made it different from all the other examples I was trying to go off. I remember assuming that it was probably really straightforward and that everyone else does these sorts of analyses all the time and that I was a bit slow for not being able to figure it out straight away… so it is somewhat gratifying to hear that it’s not a common problem that biologists solve, and to have someone else reaching out to me for my experience on it!

I’ve decided to write up my response as a blog post, in case anyone else is interested… and to remind myself of what happened there! Comments and suggestions for improvement are invited!

In general terms, the analysis is aimed at understanding what factors explain how much anthropogenic disturbance (e.g. land clearing) will occur within the landscapes of the Great Western Woodlands, where there is an abundance of mining activity and pastoralism within an otherwise relatively undisturbed region.

More specifically, the analysis relates to predicting the percentage of area disturbed (the ‘development footprint’) at a patch level (this was the smaller of two scales of analysis used in the investigation, and the only one to be discussed here), by a set of potential predictor variables. The predictor variables included both categorical and continuous variables. Categorical variables were: pastoral tenure, greenstone lithology, conservation estate, ironstone formation, schedule 1 area clearing restrictions, environmentally sensitive area designation, vegetation formation, type of tenements (exploration/prospecting tenement, mining and related activities tenement, none), primary target commodity (gold, nickel, iron-ore, other), and sample area (n=24). The numerical variables were: number of mining projects, number of dead tenements, sum of duration of all live and dead tenements, distance to wheatbelt, and distance to nearest town.

The next image (below) shows an example of these patches and their attribuets.

pic for blog 2.jpg

Example of polygons constructed to assess predictors of development footprint at the patch level

I used ‘nlme’ package in R for the analysis. Early in the data exploration (which I won’t detail here) I realised that the data for the proportional area disturbed was very skewed, and that log or other transformations weren’t enough to achieve a normal-ish distribution. A logistic transformation was the clear solution, given the percentage nature of the data (i.e. that it was bounded by 0 and 1); see the following image with scatter plots and histograms.


However, I couldn’t just use a GLMM with a gaussian distribution and a logit link, as this would have produced the problem that predicated values will be outside the 0-1 range. I had to first do a logistic transformation, model linearly, and then back-transform the modelled values.

The following line of code shows how I logit-transformed the proportion of area disturbed (‘dist_perc’), after adding 0.001 to each value (I had to do this as logit only works on values between 0 and 1, exclusive). I also show how I ‘untranformed’ the data and plotted it to produce a nice, normal, histogram. Note that dp was the dataframe I worked with, stands for ‘disturbance by patch’.

> dp$ldistp = qlogis(dist_perc+0.001)                   # qlogis does a logit transformation on (a set of) values.

> plot(dp$ldistp)                                         # scatter plot looks much better

> range(dp$ldistp)

> hist(dp$ldistp)                                         # Looks good.

> dp$s.dist = scale(dp$ldistp)                   # s.dist is the logit-transformed, scaled disturbed percentage

> back_transformed = (plogis(dp$ldistp))-0.001           # plogis ‘undoes’ the logit transformation.


After completion of the data exploration and dealing with other variables that needed to be transformed, confirming no outliers, dealing with collinearity etc and scaling issues, I modelled the transformed variable using maximum likelihood and performed a manual backward-selection and a few other checks to reach the final model, which I then modelled using REML.

I have included the whole script here. It includes the data exploration stages, as well as a plethora of comments, and also a few tries of alternative analysis techniques using lmer, lmerTest and lme4 (and modelling with a logit-link before I figured out that this wasn’t a useful approach). I take no responsibility for errors! Check line 621 for the final model, followed by back-transformations of the outcomes.

Posted in blog | Tagged , , | 1 Comment

R Script for spatial analysis disturbance by patch – for reference in blog post on mixed-model analysis with logistic transformation

Note: this is a post to hold the R script that is referred to in this post. It’s even more awfully technical than the post that refers to it; readers not interested or familiar with R code or this type of analysis may want to skip it!

You’d probably want to copy this all in to an R script (and perhaps view in r studio) to make sense of it.

# Disturbance by patch to determine landscape predictors of disturbance —-

# Housekeeping —-
setwd(“C:/Users/ke/Dropbox/PhD/Chapter 3 – spatial analysis/R”)
library(lme4); library(ggplot2); library(plyr); library(lattice); library(car); require(lmerTest); library(nlme)

# Start of data exploration —-
sum( # no missing values

# 4.2 looking for outliers
vars1 “greenstone” , “esa” , “cons_estate” , “iron_formation”)
Mydotplot(dp[, vars1])
vars2 “mining_activity”,”commodity”,”wheatbelt_dist”,”town_dist”,”patch_area”,”disturbed.area”, “dist_perc”)
Mydotplot(dp[, vars2])
vars3 Mydotplot(dp[, vars3])
# no outliers once the tenement years issue was fixed (in raw data), but some vars need transformation

# Transforming variables —-

# mini-tutorial on how scaling works:
x s.x s.x # the resultant scaled variable stored the scaling attributes: the centre (mean) and scale (multiplier)
attr(s.x,”scaled:center”) # 5.5; extracting the centre, the mean of the original vector, from which each value was subtracted to do the scaling
attr(s.x,”scaled:scale”) # 3.02765; extracting the scale (the multiplier)
x.recreated = s.x*attr(s.x,’scaled:scale’) + attr(s.x,’scaled:center’) # to unscale just multiply the scaled vector by the ‘scale’ and add the ‘centre’

# transforming disturbance percentage:
hist(dp$dist_perc) # too skewed
dist_perc1 = dist_perc + 1
dp$logdisp = log(dist_perc1)
plot(dp$polyID~dp$logdisp) # still too squashed. try log10 transformation?
dp$logdisp = log10(dist_perc1)
plot(dp$polyID~dp$logdisp) # still too skewed. Let’s try a logit transformation:
qlogis(0.9) # Getting familiar w logit transformations… this returns 2.197. qlogis does a logit transformation on (a set of) values.
plogis(2.197225) # returns 0.9. plogis ‘undoes’ the logit transformation.
dp$ldistp = (qlogis(dp$dist_perc+0.001)) # ldistp is logit transformation of the percent disturbed plus 0.001
plot(dp$ldistp) # Looks good.
dp$s.dist = scale(dp$ldistp) # s.dist is the logit-transformed, scaled disturbed percentage
# OK I need to either do a logistic regression here, or simply logit-transform the response before modelling linearly
# doing a logistic transformation and then modelling linearly – advised by Michael Renton

# transforming and scaling ten_yrs:
range(ten_yrs) # the 177 is a valid value.
plot(ten_yrs) # needs some transformation
dp$lntyrs = log((ten_yrs+1))
plot(dp$lntyrs) # yep the natural log looks good.
dp$s.lntyrs = scale(dp$lntyrs, center = TRUE, scale = TRUE)

# transforming patch area:
dp$patchkm2 = dp$patch_area/1000000 # make kms instead of m2
dp$ln.pkm = log(dp$patchkm2) # natural log of patch area
hist(dp$ln.pkm) # centred and normalish

# transforming minedex_count:
plot(log(minedex_count+1)) # somewhat better
logmx = log(minedex_count+1)
dp$ = scale(logmx)

# transforming distance to wheatbelt:
dp$wbkm = wheatbelt_dist/1000
dp$s.wb = scale(dp$wbkm)

# transforming distance to town:
dp$tnkm = town_dist/1000
dp$ = scale(dp$tnkm)

# not much can be done with the commodity variable:
summary(dp$commodity) # not sure if having the “no commodity stated” value is useful…
# dp$commodity[dp$commodity==”no commodity stated”] <- “none” # creates NAs

# adding second-order polynomials of the numerical variables
dp$tenyrs2 = dp$s.lntyrs^2
dp$mx2 = dp$^2
dp$wb2 = dp$s.wb^2
dp$tn2 = dp$^2

# combining greenstone and ironstone lithology data into one categorical variable
dp$lithology = ifelse(greenstone == “greenstone”,”greenstone”,”other.lithology”)
dp$lithology = ifelse(iron_formation == “iron-form”,”iron.formation”,dp$lithology)
table(dp$lithology) # clever duck

# Looking for collinearity —-
pairs(dp[,vars1], lower.panel = panel.cor)
#i.e. SA number, SA_mines and category all related – use SA_mines
pairs(dp[,vars2], lower.panel = panel.cor)
miningvars = c(“SA_mines”,”greenstone”,”iron_formation”,”minedex_count”,”ten_yrs”,”mining_activity”,”commodity”)
pairs(dp[,miningvars], lower.panel = panel.cor)
# also strong collinearity between minedex count and dt-count, mined and
# note mining activity replaces expl_ten and mined.
# low 0.4 collinearity between minedex count and ten_yrs, and 0.5 between SA_mines and mining activity

summary(dp$dist_perc ~ dp$veg_form)

dpvars = c(“pastoral”,”greenstone”,”esa”,”cons_estate”,”iron_formation”,”sched_1″,”veg_form”,
Mydotplot(dp[, dpvars])
pairs(dp[,dpvars], lower.panel = panel.cor)
# ok, have dealt with collinearity. There’s nothing above 0.5, and even the 0.5 is not important

# looking at possible X Y relationships —-

# first the numeric variables: plot Y against the Xes and calculate pearson coefficients:
dpnumvars = c(“”,”s.lntyrs”,”s.wb”,””,”s.dist”)
pairs(dp[,dpnumvars], lower.panel = panel.cor)
# there appears to be a positive relationship between the area disturbed and both the
# number of mining projects and the total duration of tenements in an area.
# No relation with distance from wheatbelt, and slight negative relationship with distance from town,
# which makes sense – areas near towns are more likely to get more disturbed and to stop tracks from growing over etc.

plot(dp$dist_perc~dp$lntyrs) #remember that dp$lntyrs = log((ten_yrs+1))

#then categorical variables:
boxplot(dp$s.dist~dp$pastoral) # no real effect of pastoral activity
boxplot(dp$s.dist~dp$greenstone) # more disturbance on greenstone
boxplot(dp$s.dist~dp$esa) # more disturbance on ESAs
boxplot(dp$s.dist~dp$cons_estate) # greatest disturbance on former leasehold then class As!
# UCL has same mean distrubance as gazetted conservation estate; though higher variance.
boxplot(dp$s.dist~dp$iron_formation) # more disturbance on BIFs
boxplot(dp$s.dist~dp$sched_1) # more disturbance on schedule 1 areas
boxplot(dp$s.dist~dp$veg_form) # less disturbance in bare areas and succulent steppe
boxplot(dp$s.dist~dp$mining_activity) # more disturbance where there’s been mining tenement.
# strangely similar between exploration and no mining tenement.
boxplot(dp$s.dist~dp$commodity) # most disturbance around gold mining projects, maybe not significant

# Looking for interactions —-

coplot(s.dist ~ | factor(commodity), data = dp)
# R displays from lower left, across, to upper right.

coplot(s.dist ~ | factor(commodity), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })
# there could be an interaction between the commodity and the number of mining projects

coplot(s.dist ~ s.lntyrs | factor(pastoral), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })
# There could be an interaction between tenement years and pastoral, same as found in regional-scale analysis.

coplot(s.dist ~ factor(greenstone) | factor(pastoral), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })
# maybe nothing between greenstone and pastoral

coplot(s.dist ~ factor(veg_form) | factor(greenstone), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })
# maybe nothing between greenstone and pastoral
coplot(s.dist ~ | factor(veg_form), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })
# well there doesn’t seem to be much mallee near towns; looks like there could be an interaction

coplot(s.dist ~ | factor(esa), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })
# definitely looks like an interaction between esas and dist to town

coplot(s.dist ~ s.wb | factor(esa), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })
# same again – interaction between esas and distance to wheatbelt, although esas tend to be much closer to wheatbelt than non-esas.

coplot(s.dist ~ factor(veg_form) | factor(esa), data = dp,
panel = function(x, y, …) {
tmp abline(tmp)
points(x, y) })

coplot(s.dist ~ factor(mining_activity) | factor(veg_form), data = dp )
# most of the mining and exploration appears to be in woodland

# Fitting and selecting models —-

# 4.7.1 using gaussian family and logit link with lme4
# glmer example: model1 dp1 = glmer(dist_perc ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
data=dp, family = gaussian (link = ‘logit’))

E1 F1 plot(x = F1,
y = E1,
xlab = “Fitted values”,
ylab = “Residuals”,
main = “Homogeneity?”)
abline(h = 0, v = 0, lty = 2)

# 4.7.2 Fitting a linear mixed model with a logit-transformed response in lmerTest
dp2 = lmer(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
(1|SA_number), REML=TRUE,
# dp$commodity[$commodity)] <- “none”

# compare with model that doesn’t have random effects to see if they are important
dp2.gls = gls(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+


E2 F2 plot(x = F2,
y = E2,
xlab = “Fitted values”,
ylab = “Residuals”,
main = “Homogeneity?”)
abline(h = 0, v = 0, lty = 2)

dp3 = lmer(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
data=dp, REML = FALSE)

dp4 = lmer(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
data=dp, REML = FALSE)

dp5 = lmer(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
data=dp, REML = FALSE)

dp6 = lmer(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
data=dp, REML = FALSE)

# 4.7.3 Fitting a linear mixed model with a logit-transformed response in nlme

# first check the significance of the random effect structure:
dp1 = lme(s.dist ~ factor(pastoral)+factor(iron_formation)+factor(greenstone)+factor(esa)+factor(cons_estate)+
factor(sched_1)+factor(veg_form) +factor(commodity)+factor(mining_activity)+,
random = ~1|SA_number,method = “REML”,

# compare with model that doesn’t have random effects to see if they are important
dp1.gls = gls(s.dist ~ factor(pastoral) +factor(iron_formation) +factor(greenstone) +factor(esa) +
factor(cons_estate) +factor(sched_1) +factor(veg_form) +factor(commodity) +
factor(mining_activity) +s.lntyrs +s.wb +tenyrs2 +mx2 +wb2 +tn2 +ln.pkm,

# The p-value of the Maximum Likelihood ratio test comparison between the mixed and fixed methods
# (both calculated using REML, which allows us to apply the likelihood ratio test) is 0.0154,
# indicating that the random factor is significant.

# check the model:
E2 F2 op MyYlab <- “Residuals”
plot(x = F2, y = E2, xlab = “Fitted values”, ylab = MyYlab, main=”Residuals versus fitted”)
boxplot(E2 ~ pastoral, data = dp,
main = “Pastoral tenure”, ylab = MyYlab)
boxplot(E2 ~ greenstone, data = dp,
main = “Greenstone lithology”, ylab = MyYlab)
plot(x = dp$s.lntyrs, y = E2, ylab = MyYlab,
main = “Tenement duration”, xlab = “scaled log of total tenement duration”)
#haven’t checked against all variables but there’s too many, moving on for now and will check when model is smaller.

# Next, I’m going to fit the ‘beyond optimal’ model with ML, then reduce it manually
# then validate it,
dp2 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# remove tn2
dp3 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# remove mx2
dp4 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,
anova(dp4,dp3) #yes, wasn’t significant

# remove wb2
dp5 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

#remove tenyrs2
dp6 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# remove
dp7 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# remove schedule 1 areas
dp8 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# remove iron_form
dp9 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# remove either distance to wheatbelt or mining activity = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,
data=dp) = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,
anova(dp9, # p = 0.1054
anova(dp9, # p = 0.2048

# so remove mining activity

dp10 = lme(s.dist ~ factor(pastoral)+factor(greenstone)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# not significant are: greenstone, cons estate, wheatbelt. try remove each, one at a time.

anova(dp10, # p = 0.0539
anova(dp10, # p < 0.0001
anova(dp10, # p = 0.0395

# got to remove greenstone
dp11 = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# pastoral is significant
# esa is significant
# cons estate need to be checked but is probably highly significant
# veg form looks significant
# commodity looks significant
# tenement years is highly significant
# s.wb – what is that still doing here? the p values from the model are so different from anova comparing models! = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# yep get rid of wheatbelt
dp12 = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,

# I think we’re there so just double checking the significance of every variable: = lme(s.dist ~ factor(esa)+factor(cons_estate)+factor(veg_form)+factor(commodity),
random = ~1|SA_number,method = “ML”, data=dp) = lme(s.dist ~ factor(pastoral)+factor(cons_estate)+factor(veg_form)+factor(commodity),
random = ~1|SA_number,method = “ML”, data=dp) = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(veg_form)+factor(commodity),
random = ~1|SA_number,method = “ML”, data=dp) = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(commodity),
random = ~1|SA_number,method = “ML”, data=dp) = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(veg_form),
random = ~1|SA_number,method = “ML”, data=dp) = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(veg_form)+factor(commodity),
random = ~1|SA_number,method = “ML”, data=dp) = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(veg_form)+factor(commodity)+s.lntyrs+ln.pkm,
random = ~1|SA_number,method = “ML”, data=dp) = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(veg_form)+factor(commodity),
random = ~1|SA_number,method = “ML”, data=dp)

anova(dp12, # p = 0.0046
anova(dp12, # p = 0.0001
anova(dp12, # p = 0.0001
anova(dp12, # p = 0.0206
anova(dp12, # p = 0.0001
anova(dp12, # p = 0.0001
anova(dp12, # p = 0.000001
anova(dp12, # p = 0.0348
# all significant, and lower AIC values!

# what if i try to reintroduce greenstone? = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(greenstone)+
random = ~1|SA_number,method = “ML”,
# greenstone is marginally significant with p = 0.0562 and a slightly lower AIC value (but within 2 AIC). Leave out.

#what about interaction between mining and pastoralism?
dp13 = lme(s.dist ~ factor(pastoral)+factor(esa)+factor(cons_estate)+
random = ~1|SA_number,method = “ML”,
# interaction not significant

# I’ll also just try to use non-scaled versions of the variables for ease of interpretation if possible:
dp14 = lme(ldistp ~ factor(pastoral)+factor(esa)+factor(cons_estate)+
factor(veg_form) +factor(commodity) +lntyrs +tnkm +ln.pkm,
random = ~1|SA_number,method = “ML”,
# excellent! unscaled it is…

# what about removing commodity as it’s not helpful as is, and seeing if this
# and allows for reinstatement of greenstone too, and removal of patch area:
dp15 = lme(ldistp ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(veg_form)+
lntyrs+tnkm+factor(greenstone),random = ~1|SA_number,method = “ML”,
#yes, lets just recheck the significance of all variables: = update(dp15, .~. – factor(pastoral)) = update(dp15, .~. – factor(esa)) = update(dp15, .~. – factor(cons_estate)) = update(dp15, .~. – factor(veg_form)) = update(dp15, .~. – factor(greenstone)) = update(dp15, .~. – lntyrs) = update(dp15, .~. – tnkm)
dp15.w.pkm = lme(ldistp ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(veg_form)+lntyrs+
tnkm+ln.pkm+factor(greenstone)+ln.pkm,random = ~1|SA_number,method = “ML”,data=dp)

anova(dp15, # p = 0.0031
anova(dp15, # p = 0.0004
anova(dp15, # p < 0.0001
anova(dp15, # p = 0.005
anova(dp15, # p = 0.006
anova(dp15, # p = 0.0001
anova(dp15, # p = 0.0002
anova(dp15,dp15.w.pkm) # p = 0.778 # excluding patch area is justified.
# otherwise all significant, and lower AIC values!

# Final dp model, using REML and with unscaled response variable
finaldp = lme(ldistp ~ factor(pastoral)+factor(esa)+factor(cons_estate)+factor(veg_form)+
factor(greenstone)+lntyrs+tnkm,random = ~1|SA_number,method = “REML”, data=dp)

# Validate model
E1 F1

par(mfrow = c(1, 1))
plot(x = F1,
y = E1,
xlab = “Fitted values”,
ylab = “Residuals”,
main = “Homogeneity?”)
abline(h = 0, v = 0, lty = 2)
# according to Zuur 2009, looks ok
boxplot(E1 ~ factor(dp$greenstone)) # compare variance across factor levels
boxplot(E1 ~ factor(dp$esa))
boxplot(E1 ~ factor(dp$cons_estate))
boxplot(E1 ~ factor(dp$veg_form))
boxplot(E1 ~ factor(dp$commodity))
boxplot(E1 ~ factor(dp$mining_activity))
tapply(E1, INDEX = dp$pastoral, FUN = var)
tapply(E1, INDEX = dp$cons_estate, FUN = var)
# variance varies only by a factor of ~4; Zuur says that it’s a problem if greater than 10. So not a problem.
# could consider removing the outlier

# test for independence:
plot(x = dp$patch_area,
y = E1,
xlab = “patch area”,
ylab = “Residuals”)
abline(h = 0, lty = 2)

cor.test(E1, dp$patch_area) # not significant

# i.e. no clusters above or below; looks ok.


hist(E1, main = “Normality”, breaks=10)
#Or qq-plot
# i.e. not so normal
hist(dp$s.dist ) # very skewed no worries

#But plot residuals also versus covariates NOT in the model!
boxplot(E1 ~ s.wb) ; abline(h = 0, lty = 2) # confirms no effect of distance to wheatbelt
boxplot(E1 ~ ; abline(h = 0, lty = 2) # no residual effect of distance to town

# Final model and sketch with predictions —-

finaldp = lme(ldistp ~ factor(pastoral) +factor(esa) +factor(cons_estate) +factor(veg_form) +
factor(greenstone)+lntyrs+tnkm,random = ~1|SA_number,method = “REML”, data=dp)

# I tried many different ways to predict from the model using a newdata dataframe but couldn’t get it to work.
# here i use the values i calculated manually in excel using the model outputs:

# remember how to un-logit the response variable:
plogis(2.197225) # returns 0.9. plogis undoes the logit transformation.
ldistp = (qlogis(dist_perc+0.001))

# predicting effect of pastoral status:
pastoral.pred.logit = data.frame(pastoral = c(“pastoral”,”pastoral”,”pastoral”,”not.pastoral”,”not.pastoral”,”not.pastoral”),
logit.percent.disturbed = c(-5.48660829,
pastoral.pred.logit$percent.disturbed = plogis(pastoral.pred.logit$logit.percent.disturbed)-0.001
pastoral.pred2 fit = (c(0.0031247841,0.0060574270)*100))
pastoral.pred2$se.low = (c((0.003124784-0.002423696),(0.006057427-0.004860812))*100)
pastoral.pred2$se.high = (c((0.003968722-0.003124784),(0.007496270-0.006057427))*100)
k k + geom_pointrange(size=1,aes(colour=Pastoral.status)) + theme_bw()+ylab(“Percent of area disturbed”)
ggsave(“dp figure 1 – pastoral status.png”, width=4, height=3, dpi=300)

# using intervals function to calculate predictions and 95% intervals:
conf.ints = intervals(finaldp)
# predicting effect of pastoral status:
pastoral.pred1 = data.frame(pastoral = c(“pastoral”,”pastoral”,”pastoral”,”not.pastoral”,”not.pastoral”,”not.pastoral”),
logit.percent.disturbed = c(-5.40035707,
pastoral.pred1$percent.disturbed = plogis(pastoral.pred1$logit.percent.disturbed)-0.001
fit1=percent.disturbed[1];low1 = percent.disturbed[3];high1=percent.disturbed[2]
fit2=percent.disturbed[4];low2 = percent.disturbed[6];high2=percent.disturbed[5]
pastoral.pred2 fit = c(fit1,fit2))
pastoral.pred2$se.low = c((fit1-low1),(fit2-low2))
pastoral.pred2$se.high = c((high1-fit1),(high2-fit2))

k k + geom_pointrange(size=1,aes(colour=Pastoral.status)) + theme_bw()+ylab(“Percent of area disturbed”)
ggsave(“dp figure 1 – pastoral status(full uncertainty).png”, width=4, height=3, dpi=300)

# predicting effect of esa status:
esa.pred1 = data.frame(esa = c(“esa”,”esa”,”esa”,”not.esa”,”not.esa”,”not.esa”),
logit.percent.disturbed = c(-3.83612729,
esa.pred1$percent.disturbed = (plogis(esa.pred1$logit.percent.disturbed)-0.001)*100
fit1=percent.disturbed[1];low1 = percent.disturbed[3];high1=percent.disturbed[2]
fit2=percent.disturbed[4];low2 = percent.disturbed[6];high2=percent.disturbed[5]
esa.pred2 fit = c(fit1,fit2))
esa.pred2$se.low = c((fit1-low1),(fit2-low2))
esa.pred2$se.high = c((high1-fit1),(high2-fit2))
k k + geom_pointrange(size=1,aes(colour=Clearing.regs)) + theme_bw()+ylab(“Percent of area disturbed”)
ggsave(“dp figure 2 – esa.png”, width=4, height=3, dpi=300)

# predicting effect of conservation estate:
cons.pred1 = data.frame(cons = c(rep(c(“class-A”,”ex.leasehold”,”gazetted”,”not-cons”,”unofficial”),each=3)),
logit.percent.disturbed = c(-5.70267029, -4.91130149, -6.49403909,
-4.34291529, -3.60996819, -5.07586239,
-6.59664929, -5.88199979, -7.31129879,
-4.94659229, -4.30305129, -5.59013329,
-5.45534029, -4.75924019, -6.15144039))
cons.pred1$percent.disturbed = plogis(cons.pred1$logit.percent.disturbed)-0.001

fit1= percent.disturbed[1]; low1 = percent.disturbed[3]; high1=percent.disturbed[2]
fit2= percent.disturbed[4]; low2 = percent.disturbed[6]; high2=percent.disturbed[5]
fit3= percent.disturbed[7]; low3 = percent.disturbed[9]; high3=percent.disturbed[8]
fit4= percent.disturbed[10];low4 = percent.disturbed[12];high4=percent.disturbed[11]
fit5= percent.disturbed[13];low5 = percent.disturbed[15];high5=percent.disturbed[14]

cons.pred2 fit = c(fit1,fit2,fit3,fit4,fit5))
cons.pred2$se.low = c((fit1-low1),(fit2-low2),(fit3-low3),(fit4-low4),(fit5-low5))
cons.pred2$se.high = c((high1-fit1),(high2-fit2),(high3-fit3),(high4-fit4),(high5-fit5))
k k + geom_pointrange(size=1,aes(colour=Conservation.tenure)) + theme_bw()+ylab(“Percent of area disturbed”)
ggsave(“dp figure 3 – cons.png”, width=6, height=3, dpi=300) # note colouring is different to combined plot (r2 code) but content is same

# predicting effect of vegetation form:
veg.pred1 = data.frame(veg = c(rep(c(“bare”,”broombush”,”mallee”,”mulga”,”shrubland”,”succulent”,”woodland”),each=3)),
logit.percent.disturbed = c(-4.56203229, -5.35340109, -3.77066349,
-4.12225829, -4.35071239, -3.89380419,
-4.33060829, -4.71315739, -3.94805919,
-3.40862529, -3.71443229, -3.10281829,
-4.10232729, -4.37808419, -3.82657039,
-4.00632829, -4.30158499, -3.71107159,
-3.88034929, -4.08146089, -3.67923769))
veg.pred1$percent.disturbed = plogis(veg.pred1$logit.percent.disturbed)-0.001
fit1=percent.disturbed[1];low1 = percent.disturbed[2];high1=percent.disturbed[3]
fit2=percent.disturbed[4];low2 = percent.disturbed[5];high2=percent.disturbed[6]
fit3=percent.disturbed[7];low3 = percent.disturbed[8];high3=percent.disturbed[9]
fit4=percent.disturbed[10];low4 = percent.disturbed[11];high4=percent.disturbed[12]
fit5=percent.disturbed[13];low5 = percent.disturbed[14];high5=percent.disturbed[15]
fit6=percent.disturbed[16];low6 = percent.disturbed[17];high6=percent.disturbed[18]
fit7=percent.disturbed[19];low7 = percent.disturbed[20];high7=percent.disturbed[21]
veg.pred2 fit = c(fit1,fit2,fit3,fit4,fit5,fit6,fit7))
veg.pred2$se.low = c((fit1-low1),(fit2-low2),(fit3-low3),(fit4-low4),(fit5-low5),(fit6-low6),(fit7-low7))
veg.pred2$se.high = c((high1-fit1),(high2-fit2),(high3-fit3),(high4-fit4),(high5-fit5),(high6-fit6),(high7-fit7))
k k + geom_pointrange(size=1,aes(colour=Vegetation.form)) + theme_bw()+ylab(“Percent of area disturbed”)+xlab(“Vegetation formation”)
ggsave(“dp figure 4 – veg.png”, width=6, height=3, dpi=300)

summary(veg.pred$percent.disturbed~ veg.pred$veg)

# predicting effect of greenstone lithology:
green.pred1 = data.frame(green = c(rep(c(“greenstone”,”not_greenstone”),each=3)),
logit.percent.disturbed = c(-4.55671829, -5.34808709, -3.76534949,
-4.94659229, -5.09247909, -4.80070549))
green.pred1$percent.disturbed = plogis(green.pred1$logit.percent.disturbed)-0.001
fit1=percent.disturbed[1];low1 = percent.disturbed[2];high1=percent.disturbed[3]
fit2=percent.disturbed[4];low2 = percent.disturbed[5];high2=percent.disturbed[6]
green.pred2 fit = c(fit1,fit2))
green.pred2$se.low = c((fit1-low1),(fit2-low2))
green.pred2$se.high = c((high1-fit1),(high2-fit2))
k k + geom_pointrange(size=1,aes(colour=Lithology)) + theme_bw()+ylab(“Percent of area disturbed”)+xlab(“Lithology”)
ggsave(“dp figure 5 – greenstone.png”, width=4, height=3, dpi=300)

# predicting effect of tenement years:
range(lntyrs) # 0.000000 to 5.181784
tenyrs.pred = data.frame(lntyrs = seq(from=0, to= 5.181784, by= 0.01))
tenyrs.pred$logit.percent.disturbed = -5.107206754 + 0.440025 *tenyrs.pred$lntyrs
tenyrs.pred$logit.percent.disturbed.upper = -5.107206754 + (0.440025+0.0756683) *tenyrs.pred$lntyrs
tenyrs.pred$logit.percent.disturbed.lower = -5.107206754 + (0.440025-0.0756683) *tenyrs.pred$lntyrs
tenyrs.pred$tenyrs = exp(tenyrs.pred$lntyrs)-1
tenyrs.pred$percent.disturbed = (plogis(tenyrs.pred$logit.percent.disturbed)-0.001)*100
tenyrs.pred$percent.disturbed.upper = (plogis(tenyrs.pred$logit.percent.disturbed.upper)-0.001)*100
tenyrs.pred$percent.disturbed.lower = (plogis(tenyrs.pred$logit.percent.disturbed.lower)-0.001)*100
plot(percent.disturbed~lntyrs, data = tenyrs.pred, xlab = “log of tenement years”, ylab=”Percentage of area disturbed”)

head(tenyrs.pred) # variables of use are: tenyrs,percent.disturbed,percent.disturbed.upper,percent.disturbed.lower

#start of plot
p = ggplot(tenyrs.pred, aes(tenyrs,percent.disturbed))
p + geom_line(colour=I(“red”),size=1.1)+
geom_ribbon(aes(ymin=percent.disturbed.lower, ymax=percent.disturbed.upper),
theme_bw() +
labs(x=”Total tenement duration (years)”,y=”Area disturbed (%)”)
ggsave(“dp figure 6 – tenement years.png”, width=4, height=3, dpi=300)
# end of plot

# predicting effect of distance to town:
range(tnkm) # 0.74 100.00
tnkm.pred = data.frame(tnkm = seq(from=0.74, to= 100, by= 0.1))
tnkm.pred$logit.percent.disturbed = -2.629818536 + -0.019183 *tnkm.pred$tnkm
tnkm.pred$logit.percent.disturbed.upper = -2.629818536 + (-0.019183+0.0051545) *tnkm.pred$tnkm
tnkm.pred$logit.percent.disturbed.lower = -2.629818536 + (-0.019183-0.0051545) *tnkm.pred$tnkm
tnkm.pred$percent.disturbed = (plogis(tnkm.pred$logit.percent.disturbed)-0.001)*100
tnkm.pred$percent.disturbed.upper = (plogis(tnkm.pred$logit.percent.disturbed.upper)-0.001)*100
tnkm.pred$percent.disturbed.lower = (plogis(tnkm.pred$logit.percent.disturbed.lower)-0.001)*100
plot(percent.disturbed~tnkm, data = tnkm.pred, xlab = “Distance to town (km)”, ylab=”Percentage of area disturbed”)
#start of plot
p = ggplot(tnkm.pred, aes(tnkm,percent.disturbed))
p + geom_line(colour=I(“blue”),size=1.1)+
geom_ribbon(aes(ymin=percent.disturbed.lower, ymax=percent.disturbed.upper),
theme_bw() +
labs(x=”Distance from town (kms)”,y=”Area disturbed (%)”)
ggsave(“dp figure 7 – distance from town.png”, width=4, height=3, dpi=300)
# end of plot

# an afterthought: it would be good to combine pastoral and cons_estate as they both refer to tenure.
# I’ve resolved a few conflicts and created a new variable called ‘tenure’; the AIC for this model is slightly better,

dptenure = lme(ldistp ~ factor(tenure) + factor(esa) + factor(veg_form) + factor(greenstone) +
lntyrs + tnkm, random = ~1|SA_number,method = “REML”, data=dp)

# Maybe this would be better but I’d have to do all the predictions over again in excel and that’s too hard
# but for now I’ll have a sneak peak at the result of it:
# Class-A is the default,
# former leasehold has more disturbance
# gazetted cons has less disturbance
# not-cons has more disturbance but less than former leasehold
# pastoral has more disturbance but less than not-cons
# unofficial cons has more than pastoral

# so in order of most to least disturbance:
# 1. former leasehold
# 2. not-cons-estate
# 3. unofficial cons
# 4. pastoral
# 5. Class-A
# 6. gazetted conservation

# It’s wierd, you’d expect pastoral and former-pastoral to match up a bit more.
# ..and perhaps unofficial cons to match up more with gazetted cons.
# it looks as if the process of converting a pastoral lease to a conservation-ex-lease is the most disturbing thing!
# doesn’t really make sense, unless it’s all focued on Credo and they cleared a lot more area?
# I think i need to actually just combine the pastoral and ex-pastoral categories and that might increase the sample size and solve the issue.

Posted in blog | Leave a comment

A reflection

Two quotes from the recent newsletter of DBytes, a Newletter of the Environmental Decisions Group, seem to highlight a manifestation of Dr. Robert Sapolsky’s claim that the one trait that best defines and distinguishes us humans from other species is the ability to hold two contradictory ideas in our head, and yet continue on in the face of it:

“Many species are slipping away before we can even describe them. This IUCN Red List update shows that the scale of the global extinction crisis may be even greater than we thought.”   – Inger Andersen, Director General, IUCN press release

meanwhile, back in Australia…

“By 2019 Federal Environment Department spending will have declined by 38 per cent on 2013 levels while total government spending will have increased by 21.7 per cent. National spending on biodiversity has fallen to the lowest levels in more than a decade – with conservation getting just 5 cents in every 100 dollars spent by the government.”
– Matthew Rose, ACF press release

In the meantime, this recent flier from the Australian Greenspace Alliance reminds us that sustaining the natural environment is not just a ‘giving thing’: it helps to provide us with the things that make us well and happy:


Posted in blog | Leave a comment