Local factors mediate the response of biodiversity to land use on two African mountains

TLDR: It is demonstrated that local factors mediate biodiversity responses to land use and cautions against applying biodiversity models to local contexts without prior knowledge of which factors are locally relevant.

Abstract: Land-use change is the single biggest driver of biodiversity loss in the tropics. Biodiversity models can be useful tools to inform policymakers and conservationists of the likely response of species to anthropogenic pressures, including land-use change. However, such models generalize biodiversity responses across wide areas and many taxa, potentially missing important characteristics of particular sites or clades. Comparisons of biodiversity models with independently collected field data can help us understand the local factors that mediate broad-scale responses. We collected independent bird occurrence and abundance data along two elevational transects in Mount Kilimanjaro, Tanzania and the Taita Hills, Kenya. We estimated the local response to land use and compared our estimates with modelled local responses based on a large database of many different taxa across Africa. To identify the local factors mediating responses to land use, we compared environmental and species assemblage information between sites in the independent and African-wide datasets. Bird species richness and abundance responses to land use in the independent data followed similar trends as suggested by the African-wide biodiversity model, however the land-use classification was too coarse to capture fully the variability introduced by local agricultural management practices. A comparison of assemblage characteristics showed that the sites on Kilimanjaro and the Taita Hills had higher proportions of forest specialists in croplands compared to the Africa-wide average. Local human population density, forest cover and vegetation greenness also differed significantly between the independent and Africa-wide datasets. Biodiversity models including those variables performed better, particularly in croplands, but still could not accurately predict the magnitude of local species responses to most land uses, probably because local features of the land management are still missed. Overall, our study demonstrates that local factors mediate biodiversity responses to land use and cautions against applying biodiversity models to local contexts without prior knowledge of which factors are locally relevant.

Figures

Figure 4: Difference between the relative observed biodiversity values at the field sites, and predicted biodiversity values from the best-selected model and a land-use-only model (see Table 1). Predicted values were obtained by

Figure 5: Average assemblage structure in terms of bird species’ characteristics, for the broad-scale (‘Africa-wide’) and the independent (‘Taita’ and ‘Kilimanjaro’) datasets, in different land uses. Range size was measured as the average

Table 1: Best-fit model from among those using all possible combinations of explanatory variables for the broad-scale dataset. Pseudo-R² values were computed following (Nakagawa and Schielzeth 2013).

Figure 1: Distribution of broad-scale studies across Africa coloured by taxonomic group. Bars show the number of sites per latitude coloured by taxonomic group. Highlighted countries indicate the location of the independent field study

Figure 2: The response of species richness and total abundance to land use, from the Africa-wide model and the independent field data. Land-use categories are primary vegetation (PV), secondary vegetation (SV), plantation forest (PL), cropland (CL) and urban (UR). All coefficients are visualized as proportional difference to primary vegetation

Figure 3: Difference in environmental variables in different land uses, between the broad-scale (‘Africa-wide’) and the independent field data (‘Kilimanjaro’ and ‘Taita’). Boxes show the inter-quartile range, while lines show the full range of the data (or 1.5 times the upper and lower quartiles if less extreme). Abbreviations as in Figure 2.

Full text

Local factors mediate the response of biodiversity to land use
on two African mountains
Article (Accepted Version)
http://sro.sussex.ac.uk
Jung, M, Hill, S L L, Platts, P J, Marchant, R, Siebert, S, Fournier, A, Munyekenye, F B, Purvis, A,
Burgess, N D and Newbold, T (2016) Local factors mediate the response of biodiversity to land
use on two African mountains. Animal Conservation, 20 (4). pp. 370-381. ISSN 1367-9430
This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/64763/
This document is made available in accordance with publisher policies and may differ from the
published version or from the version of record. If you wish to cite this item you are advised to
consult the publisher’s version. Please see the URL above for details on accessing the published
version.
Copyright and reuse:
Sussex Research Online is a digital repository of the research output of the University.
Copyright and all moral rights to the version of the paper presented here belong to the individual
author(s) and/or other copyright owners. To the extent reasonable and practicable, the material
made available in SRO has been checked for eligibility before being made available.
Copies of full text items generally can be reproduced, displayed or performed and given to third
parties in any format or medium for personal research or study, educational, or not-for-profit
purposes without prior permission or charge, provided that the authors, title and full bibliographic
details are credited, a hyperlink and/or URL is given for the original metadata page and the
content is not changed in any way.

Local factors mediate the response of biodiversity to land use on two African mountains
1
Martin Jung¹
1
, Samantha Hill²,
8
, Philip J. Platts
3
, Rob Marchant
4
, Stefan Siebert
5
, Anne Fournier
6
, Fred B.
2
Munyekenye
7
, Andy Purvis
8, 9
, Neil D. Burgess¹ ² and Tim Newbold²
,10
3
1. Center for Macroecology, Climate and Evolution, the Natural History Museum of Denmark, Copenhagen,
4
Denmark
5
2. United Nations Environment Programme World Conservation Monitoring Centre, Cambridge, CB3 0DL,
6
U.K
7
3. Department of Biology, University of York, York, YO10 5DD, U.K
8
4. York Institute for Tropical Ecosystems (KITE), Environment Department, University of York, York, YO10
9
5DD, U.K
10
5. Unit of Environmental Sciences and Management, North-West University, Private Bag X6001,
11
Potchefstroom, 2520, South Africa
12
6. IRD - Institut de recherche pour le développement, Research Unit 208 PALOC (IRD MNHN), Paris,
13
France
14
7. Nature Kenya, Museum Hill, P.O Box 44486 GPO, 00100 Nairobi, Kenya.
15
8. Department of Life Sciences, Natural History Museum, London SW7 5BD, U.K
16
9. Department of Life Sciences, Imperial College London, Silwood Park, Ascot SL5 7PY, U.K.
17
10. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment,
18
University College London, Gower Street, London WC1E 6BT, U.K.
19
Abstract
20
Land-use change is the single biggest driver of biodiversity loss in the tropics. Broad-scale biodiversity models
21
can be useful tools to inform policy-makers and conservationists of the likely response of species to
22
anthropogenic pressures, including land-use change. However, such models generalize biodiversity responses
23
across wide areas and many taxa, potentially missing important characteristics of particular sites or clades.
24
Comparisons of broad-scale models with independently collected field data can help us understand the local
25
factors that mediate broad-scale responses.
26
We collected bird occurrence and abundance data along two elevational transects in Mount Kilimanjaro,
27
Tanzania and the Taita Hills, Kenya. We estimated the local response to land use and compared our estimates
28
with modelled responses based on a broad-scale, but fine-resolution, database of many different taxa across
29
Africa. To identify the local factors mediating responses to land use, we compared environmental and species
30
assemblage information between sites in the local and broad-scale data sets.
31
Bird species richness and abundance responses to land use in the independent data followed similar
32
trends as suggested by the broad-scale model, but the broad-scale land-use classification was too coarse to
33
capture fully the variability introduced by local agricultural management practices. A comparison of assemblage
34
1
Current address: School of Life Science, University of Sussex, Brighton, United Kingdom, BN1 9RH: m.jung@sus-
sex.ac.uk

characteristics showed that the sites on Kilimanjaro and the Taita Hills had higher proportions of forest specialists
35
in croplands compared to the Africa-wide average. Local human population density, forest cover and vegetation
36
greenness also differed significantly between the local and broad-scale datasets. Broad-scale models including
37
those variables performed better, but still could not accurately predict the magnitude of local species responses
38
to most land uses, probably because local features of the land management are still missed.
39
Overall, our study demonstrates that local factors mediate biodiversity responses to land use and
40
cautions against applying broad-scale models to local contexts without prior knowledge of which broad-scale
41
factors are locally relevant.
42
Keywords:
43
Modelling; Birds; Eastern Arc Mountains; Homegardens; Kilimanjaro; PREDICTS; Taita Hills;
44

45
Introduction
46
Humanity drives global biodiversity decline in many different ways (Butchart et al. 2010). Among the different
47
pressures, anthropogenic land-use change has been shown to have the most severe impact on terrestrial biodiversity (Foley
48
et al. 2005; Jetz et al. 2007; Gibson et al. 2011). A change in land use might greatly reduce the amount or quality of habitat
49
available to species, or contribute to landscape fragmentation resulting in declining species abundance and/or local
50
extinctions (Brooks et al. 2002). Therefore it is of particular interest to understand how assemblages of species respond
51
to land use, and if they can persist in a human-modified landscape (Gardner et al. 2007). Broad-scale statistical models
52
are increasingly employed to predict the response of species assemblages to land use (Loh et al. 2005; Scholes and Biggs
53
2005; Alkemade et al. 2009; Newbold et al. 2014a; Newbold et al. 2015). Such models can be based on data from many
54
different taxonomic groups, and can inform policy-makers about biodiversity trends and influence ongoing international
55
debates about relevant mitigation schemes (Pereira et al. 2010; Leadley et al. 2014; CBD 2014). However, in generalising
56
across a broad area, such models likely miss local factors that mediate species’ response to land use.
57
Most broad-scale models employ a coarse land-use classification scheme (eg. Scholes and Biggs 2005; Alkemade
58
et al. 2009; Newbold et al. 2014a; Newbold et al. 2015) that cannot capture the full variability of local land-use systems,
59
often missing important land-use categories such as agroforestry (Scholes and Biggs 2005; Newbold et al. 2015). Others
60
ignore the differential responses of taxonomic groups (Alkemade et al. 2009), which can be important (e.g., Gibson et al.
61
2011; Murphy and Romanuk 2014; Newbold et al. 2014a). Some broad-scale models of local species richness and
62
abundance have found environmental variables such as land-use intensity, human population density and metrics derived
63
from vegetation-greenness data to be influential (Newbold et al. 2014a; De Palma et al. 2015). It is however unclear if
64
the inclusion of these variables is relevant in understanding how the local environment mediates biodiversity responses
65
to land use. Similarly it has been shown that functional characteristics can help explain species’ varying responses to land
66
use on a broad scale (Owens and Bennett 2000; Flynn et al. 2009; Newbold et al. 2013; De Palma et al. 2015), but to our
67
knowledge no previous studies have evaluated whether those responses are consistent in a local context. Comparing
68
estimates derived from broad-scale models with local independent data, where the detailed environmental conditions are
69
known and taken into account, could help to identify some of the important local factors that mediate biodiversity
70
responses to land use and ultimately provide insight on how to improve the applicability of broad-scale models.
71
Addressing the question of how biodiversity responds to land use is especially important in sub-Saharan Africa,
72
where the congruent and patchy distribution of both biodiversity and human population leads to a high risk of biodiversity
73
loss (Balmford et al. 2001; Burgess et al. 2007a; Pfeifer et al. 2012). In this study we investigated biodiversity responses
74

to land use in two study areas in east Africa each with different geological, evolutionary and land-use history. We explicitly
75
test if (1) the response of avian diversity to land use is different in those study areas compared to a taxonomically and
76
geographically broad Africa-wide model of local biodiversity responses to land use, (2) investigate potential explanations
77
for any mismatches using remote-sensed data and information on species’ ecological characteristics and threat status, to
78
identify the local factors that mediate the local response of biodiversity to land use; and (3) make recommendations for
79
additional factors to be included in broad-scale biodiversity models and sampling choices for biodiversity surveys.
80
81
Methods
82
Assemblage composition data
83
To generate broad-scale estimates of how local species richness and abundance respond to land use, we used
84
the database of the Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (PREDICTS)
85
project (Hudson et al. 2014; www.predicts.org.uk). We used only the data sources for Africa (extracted 28/07/2014, see
86
Table SI 1) with land use in each site classified as primary vegetation (1285 sites), secondary vegetation (485),
87
plantation forest (441), cropland (612) and urban (33) habitat (see Hudson et al. 2014 for definitions). Additionally, we
88
also used the information on land-use intensity according to the classification developed by the PREDICTS Project,
89
which combines information on management intensity and proportion of each site impacted (SI Table 2; Hudson et al.
90
2014). This classification was used so that different land uses could be compared across the different studies, both in the
91
broad-scale dataset and the independent field data, and necessarily means that some of the variability in land-use
92
systems is omitted.
93
We collected fine-scale field data for birds (herein called ‘independent data’) along two transects on the
94
southern slopes of Mount Kilimanjaro, Tanzania and the Taita Hills, Kenya (Figure 1). Both landscapes are known for
95
their long history of human modification (Conte 2010; Heckmann et al. 2014), while having a contrasting geological
96
age (~ 30 mil. years for Taita compared to ~2 mil. years for Kilimanjaro, see Platts et al. 2011), and each has different
97
sets of endemic species (Hemp 2006a; Burgess et al. 2007b). Data on bird species richness and abundance were
98
collected visually and audibly using standardized 10-minute fixed-time point counts (Bibby et al. 2000), of 50-m radius,
99
along each of the transects. While more accurate estimates of biodiversity can be obtained by taking into account
100
detection probability (Buckland et al. 2008), our sampling methodology was chosen to match the sampling scheme of
101
bird studies in the PREDICTS database. Because detectability is likely to be higher in more open habitats, which are
102
often those with higher human land-use activity, our estimates of the effects of human land use on biodiversity (from
103
both the broad-scale and independent datasets) are likely to be conservative. Point counts (N=147) were located along
104

Frequently asked questions

Q1. What is the reason for the loss of vegetation in the Taita Hills?

The loss of natural vegetation seems to have accelerated in the last century owing to 293 increasing human population density, colonial forestry operations (Brooks et al. 

Q2. How many plantation forests could support conservation efforts?

It has been suggested that 302 plantation forests could support conservation efforts if appropriately managed (Brockerhoff et al. 2008). 

Q3. What was the random-effects structure for the two models?

For both 172 models, the best random-effects structure (lowest AIC) contained a random slope of land use nested within study, and a 173 random intercept for study identity. 

Q4. What is the background to the panels showing the field transects?

The background to the panels showing the field transects is a hillshade modelderived from SRTM 90m indicating the topographic relief of the sites. 

Q5. What is the explanation for the mismatch in biodiversity in 314 plantation forests?

the observed mismatch in biodiversity in 314 croplands can be best explained by the occurrence of low-intensity agroforestry systems (known locally as 315 ‘homegardens’), which were located at higher elevations than more intensively used croplands. 

Q6. Why did the broad-scale model show that responses to 250 land use vary significantly among different studies?

the broad-scale model showed that responses to 250 land use vary substantially among different studies (Figure S1); this heterogeneity is especially apparent in urban sites, 251 perhaps because local factors, such as vegetation greenness and proximity to nearby forests, mediate responses. 

Q7. How did the authors use the database of the Projecting Responses of Ecological Diversity in Changing?

To generate broad-scale estimates of how local species richness and abundance respond to land use, the authors used 84 the database of the Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (PREDICTS) 85 project (Hudson et al. 2014; www.predicts.org.uk). 

Q8. What were the important variables for explaining species richness?

Across all candidate models, land use, 222 land-use intensity, their interaction, and vegetation removal were of the greatest relative importance for explaining 223 abundance and species richness (for each of these variables, summed AIC weights, ∑AICw ≈ 1).