Journal of Applied Ecology 2015, 52, 110–118
doi: 10.1111/1365-2664.12366
Estimating the effect of plantations on pine invasions in protected areas: a case study from South Africa Matthew M. McConnachie1*, Brian W. van Wilgen1, David M. Richardson1, Paul J. Ferraro2 and Aurelia T. Forsyth3 1
Centre for Invasion Biology, Department of Botany & Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa; 2Department of Economics, Andrew Young School of Policy Studies, Georgia State University, P.O. Box 3992, Atlanta, GA 30302-3992, USA; and 3CapeNature, Scientific Services, Private Bag X5014, Stellenbosch 7599, South Africa
Summary 1. Protected areas (PAs) are a key intervention for conserving biodiversity and ecosystem ser-
vices. A major challenge for PAs is the control of invasive non-native plants that spread into PAs from surrounding sources such as forestry plantations. The links between invasions and different source populations are poorly understood, making it difficult to apportion responsibilities for control costs. 2. We estimated the effect of plantations of invasive pines (Pinus species) on the spread of these trees into PAs in South Africa’s Cape Floristic Region (CFR). We assessed the effect of (i) distance from plantation, (ii) plantation and PA orientation in relation to the prevailing wind and (iii) the proportionate size of the surrounding plantation on the abundance of invasions in PAs. We also estimated the historic and potential future costs of controlling invasions in the PAs. 3. PA management units within 3 km of a plantation had over double the pine cover of control units >6 km from plantations (612% vs. 239%). We attributed 51% of the pine invasions in PAs to plantations. 4. Neither the proportional size of the plantations nor their orientation relative to prevailing winds had a detectable effect on the cover of invasive pines. 5. Costs of controlling invasive pines in the study area totalled 98 million Rand (10 Rand = c. 1 US$ in 2013) between 2001 and 2012. It could cost between 273 and 916 million Rand (all future costs expressed in 2013 Rand), and take between 34 and 113 years, to clear the remaining invasive pines in the study area, depending on rates of spread and the cost–effectiveness of control. 6. Policy implications. Estimating the contribution of an invasion source, in this case forestry plantations, requires estimating an unobservable counterfactual outcome: the invasions that would have occurred in the absence of the plantations. We have made a first step towards doing this by using empirical approaches that vary in the strictness of their assumptions, along with robustness tests to that assess the plausibility of these assumptions. Our study provides the starting point for estimating the contribution of plantation forestry to protected area invasions in the Cape Floristic Region of South Africa. Key-words: biological invasions, causal inference, counterfactual, evidence-based, forestry, impact evaluation, matching, tree invasions Introduction Protected areas (PAs) are one of the leading interventions for conserving biodiversity and ecosystem services, but formal protection alone does not guarantee conservation *Correspondence author. E-mail:
[email protected]
success (Miteva, Pattanayak & Ferraro 2012). PAs increasingly exist in a matrix of intensive human usage, and many types of human activities spill over PA boundaries to threaten biodiversity and ecosystem services. One such threat is invasive non-native plant species (Foxcroft et al. 2013). Managing plant invasions that originate from forestry plantations, agricultural crops,
© 2014 The Authors. Journal of Applied Ecology © 2014 British Ecological Society
Protected areas and pine plantation invasions shelter-belts and ornamental plants outside the PA boundaries is a major challenge and results in substantial costs (Foxcroft et al. 2013). Previous studies of plant invasions in PAs have adopted an exploratory approach by listing and ranking factors correlated with invasions (such as biophysical conditions, climate, land-use types and human population density) (Rouget et al. 2003b; Spear et al. 2013). Such studies are useful for identifying correlates of invasion but are less useful at attributing invasions to specific sources. To make claims of attribution, it is necessary to systematically account for confounding factors that affect both the placement of sources and the invasibility of nearby PAs (Ferraro & Hanauer 2014). An important step in this accounting process is the assessment of covariate overlap and balance between treatment and control units before and after adjusting for confounding factors. Failure to do so can result in estimates based on model extrapolation beyond the support of the data (Ho et al. 2007). This type of accounting is missing in the invasive species literature (Gurevitch & Padilla 2004) and in the environmental science literature more broadly (Ferraro & Pattanayak 2006; Ferraro 2009). Conflict of interests exist and are becoming more complex and acrimonious in many areas where the cultivation of non-native plants adjoins PAs (van Wilgen & Richardson 2014). In the absence of policy-relevant evidence, such conflicts of interest are unlikely to be satisfactorily resolved (van Wilgen & Richardson 2014). Here, we estimate the effect of the spread of invasive pines (Pinus species) from plantations into PAs in South Africa’s Cape Floristic Region (CFR). The conflict of interest between PAs and neighbouring pine plantations in South Africa has been well documented (Kruger 1977; van Wilgen & Richardson 2012, 2014). Pine plantations represent a large portion of the global commercial forestry sector based on non-native trees and provide important socio-economic benefits. In South Africa, commercial forestry provides direct employment to over 62 700 people and exports goods worth 95 billion Rand annually (c. 950 million US$, Forestry South Africa 2011). Many of the life-history traits that make pines important forestry trees also make them invasive, and potentially damaging (Prochesß et al. 2012). Most pines have wind-dispersed seeds that enable them to colonize new areas outside the forestry estate (Richardson & Brown 1986; Rouget et al. 2001, Coutts et al. 2011). Legal and policy frameworks for dealing with such ‘pollution’ have yet to be formalized. Within the context of South Africa’s CFR, three major sources of uncertainty and disagreement complicate the establishment and regulation of pine pollution targets (Fig. 1). First, there is uncertainty over the contribution of current pine plantations to pine invasions in PAs, relative to other invasion sources such as shelter-belts in agricultural areas, abandoned pine plantations and pines grown for ornamental or amenity purposes in both urban
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Fig. 1. A hypothetical example of trade-offs adapted from Gren (2008) faced by policymakers in deciding on the socially optimal target for reducing the spread of pine trees from pine plantations. The curves TC and TB indicate total (social and private) costs and benefits, from reductions in the spread of pine plantation pollution (see boxes below the x-axis for examples of reduction options). N‘ = no reduction in pine pollution; N* = pine pollution reduction which gives the maximum net social benefits as TB* TC*. N^ = pine pollution reduction when TB and TC are equal. Examples of TB = avoided loss to biodiversity, water resources and ecotourism. Examples of TC = private profits, employment, contribution to economic growth, carbon sequestration and aesthetic values.
and rural areas (horizontal axis in Fig. 1). Secondly, there is uncertainty about the effectiveness and cost of reducing the risk of spread and clearing established invasions (curve TC in Fig. 1). Examples of proposed risk-mitigation measures include not locating plantations adjacent to, or upwind of invasible habitats (Ledgard & Langer 1999). Life-history factors can be managed by selecting different species, making use of sterile cultivars, using biological control agents to reduce seed production (Hoffmann, Moran & van Wilgen 2011), or clearing invading pines before they mature (Coutts et al. 2011). In addition to the direct costs of risk-mitigation measures, there is uncertainty over whether these measures would reduce plantation productivity and how this would affect socioeconomic opportunity costs. Thirdly, there is uncertainty over the benefits (retention of streamflow volumes and biodiversity) of reducing the spread of new invasions into PAs from pine plantations (curve TB in Fig. 1). This uncertainty has led to an impasse between foresters and conservationists (Bennett & Kruger 2013) over an acceptable target level for mitigating spread risk and how it should be regulated. To begin to address the above knowledge gaps, our study aimed to (i) estimate the effect that distance from pine plantation has on the abundance of pine invasions in Pas, (ii) estimate the effect of a PA being located downwind of a plantation, (iii) estimate the effect of the proportionate size of the surrounding plantation area (a proxy for propagule pressure) on the abundance of invasions and (iv) estimate the costs of controlling pine
© 2014 The Authors. Journal of Applied Ecology © 2014 British Ecological Society, Journal of Applied Ecology, 52, 110–118
112 M. M. McConnachie et al. invasions in the PAs and the proportion of this cost that should be attributed to pine plantations.
Materials and methods STUDY AREA
Our study area included all PAs managed by CapeNature, the conservation agency in South Africa’s Western Cape Province (Fig. 2). The area covers c. 74% of the 161 million ha PA network in the province, which in turn contains a large part of remaining untransformed land in the CFR, a global biodiversity hotspot. The predominant vegetation type is fynbos shrubland associated with nutrient-poor, sandy soils. Fynbos is fireprone and vulnerable to invasion by non-native trees, even in the absence of human disturbance (van Wilgen 2013). Vegetation dominated by invasive trees consumes more water than uninvaded fynbos, impacting negatively on water resources (Le Maitre et al. 1996). To address this issue, the government established the ‘Working for Water’ programme in 1995, this initiative employs poor people to control invasive plants in natural and semi-natural ecosystems, including major invasions in PAs (van Wilgen, Le Maitre & Cowling 1998). Pines were introduced to the Western Cape in the mid-seventeenth century, but large-scale plantations were only established in the twentieth century (van Wilgen & Richardson 2012). Most pine plantation areas were originally owned and managed by government, but are now leased to privately owned companies. Most pine plantations in the Western Cape adjoin PAs along the Cape Fold Belt mountains where the bulk of PAs in the province are located (Rouget, Richardson & Cowling 2003a); 98% of pine plantations are located within 1 km of PAs.
DATA SOURCES
Our analysis was based on estimates of the cover of the five dominant species of invasive trees and shrubs (which included pines
and other species) across CapeNature PA management units (n = 4940). In some cases, pines were identified to species, but in others were combined into a single estimate for ‘pines’. We therefore did not differentiate between pine species in our analyses. The management units had an average size of 242 ha and a total area of 119 million hectares. Invasive non-native plant cover was estimated and verified in the field by reserve managers in 2013 as part of their annual monitoring programmes. We excluded management units that had been previously covered by pine plantations and subsequently cleared, or were missing estimates of invasive plant cover (n = 160). We used national land-cover data layers for the years 1994 and 2000 (Fairbanks et al. 2000) to identify pine plantations and cross-checked the accuracy of these data with Google Earth satellite imagery (following procedures set out by Visser et al. 2014). To identify plantations that had been removed prior to 1994, we used aerial photographs from 1980 to 1990 (supplied by the Surveyor General, South Africa); these were included in our analyses.
STUDY DESIGN
Each management unit was placed into one of three categories (within 3 km; between 3 and 6 km, and >6 km), based on the shortest distance between the management unit and the plantation. Management units that fell into two or more categories were included only once in the category closest to the plantation. The selected distance ranges were based on observations of pine spread in South Africa (Richardson & Brown 1986; Higgins & Richardson 1999) and New Zealand (Coutts et al. 2011). Our outcome measure was the percentage cover of invading pines in a management unit. We were interested in measuring four average treatment effects on the treated management units: (i) the expected difference between the observed pine cover on management units 6 km from pine plantations (the latter being the control or counterfactual state); (ii) the expected difference between pine cover on units (ii) and (iii) < (iv).
EFFECT ESTIMATES
Fig. 2. Location of pine plantations (black) and protected area management units under the jurisdiction of CapeNature (grey) in South Africa’s Western Cape Province. The inset map shows how management units were selected based on their proximity to pine plantations (black spotted polygon).
To estimate the counterfactual pine cover for the above analyses (i.e. cover that would have existed in the absence of the plantation sources), we used a combination of interval and point-estimate approaches that differed in the strictness of their assumptions and how they weighted treatment and control units. We began with a partial identification approach developed by Manski (2003) that estimates the bounds within which impact estimates can occur. Without any assumptions, we knew that the counterfactual pine cover on each treated unit could be no lower than zero and no higher than 100%. The difference between the
© 2014 The Authors. Journal of Applied Ecology © 2014 British Ecological Society, Journal of Applied Ecology, 52, 110–118
Protected areas and pine plantation invasions mean observed pine cover in treated units and these two extreme counterfactual values, respectively, yielded the upper and lower ‘no-assumption’ bounds on the impact of plantations. To further limit the bounds, we made two weak, but plausible, assumptions: monotone treatment response (MTR) and monotone treatment selection (MTS). MTR assumed that the unit-level treatment effect cannot be negative, that is being within closer proximity to pine plantations will not result in lower pine cover. MTS assumes that the treated units will, on average, have counterfactual pine cover outcomes that are at least as great as the cover on untreated units (i.e. positive selection bias). The MTS assumption is plausible given that pine plantations were established in areas where pine growth will be maximized (see pre-matching covariate balance results in the supplementary information). Our point-estimate approach involved pre-processing the data using matching methods followed by regression analysis with the matched data. The goal of matching was to select untreated units that were as similar as possible, on average, to the treated units in terms the covariate variables. The reason for doing this was to reduce model dependence of the regression estimates (see Appendix S1, Supporting information for detailed explanation and Ho et al. 2007). We used a matching method that attempts to maximize covariate balance via a genetic search algorithm (Diamond & Sekhon 2006). We used 1-to-1 matching with replacement. The covariates were confounding variables that could jointly affect pine plantation location and invasive pine cover. The first two covariates related to accessibility and proximity to markets, namely distance from closest town and main road (from the Department of Land Affairs, South Africa), human population within 10 km of protected area management units and distance to cultivated areas. The other covariates related to climatic variables, namely mean annual precipitation and temperature (data from Schulze 2008). Without controlling for these covariates via matching and then regression, our estimates could be biased because on average, pine plantations are more likely to be located close to management units with higher rainfall and temperature, and closer to roads, cultivated areas and human settlements (see unmatched covariate balance in Table S1–S4, Supporting information). Finally, for comparative purposes we used conventional regression analysis without matching. Our second aim was to determine the treatment effect of being located downwind of the prevailing south-easterly wind and within 3 km of a plantation. The control units were selected using the same approaches as above from units also within 3 km of plantations but not downwind of plantations. Our third aim was to measure the effect of the size of surrounding pine plantation area on PA invasions. Management units with more than 10% of a surrounding 3-km buffer area covered by plantations formed our treatment group. We selected control units from areas within 3 km of plantations but with 9 km from plantations. We found no significant difference (see Table S5, Supporting information for results). Finally, we tested whether pine clearing operations over the past 10 years could have biased our estimates because the clearing operations may have focussed on management units closer to plantations. To test for, this we replaced the current 2013 pine cover estimates with estimates of invaded cover prior to clearing, as recorded by Working for Water’s information management data base (Marais & Wannenburgh 2008). All analyses were run in R (R Development Core Team 2013) using the packages ‘MATCHING’ (Sekhon 2011) and ‘RBOUNDS’ (Keele 2011).
PROTECTED AREA CONTROL COST ESTIMATES
We obtained the costs of clearing pines in the study area from Working for Water’s spatial data base (Marais, van Wilgen & Stevens 2004), which has recorded costs since 2001. To account for inflation, we used the consumer price index to inflate all costs to 2013 South African Rand (1 US$ = c. 10 Rand). For predicting the cost of clearing remaining pine invasions in the PAs, we constructed scenarios by varying the number of follow-up clearings required (either 3 or 10), the annual rate of spread within the PAs (either no spread, or increase the area occupied by pine by 1% or 5% annually of the extent of the pine invasion within the PA) and the annual rate of spread into PA management units 6 km from plantations (239%). The cover of invading pines in management units 3–6 km from plantations was 629%, compared to 391% for matched control management units >6 km from plantations. Control units represent the counterfactual cover (the cover that would have established from other sources, had there been no plantations). Therefore for management units within 3 km, or 3–6 km of plantations, 609% [1 (239/612)], or 378% [1 (391/629)], of the pine cover could, respectively, be attributed to plantations. By multiplying these estimates by the equivalent area of closed canopy cover pines within the respective distance ranges ( 3 and 2 > 4 for Table 1). This supports the hypothesis that the higher pine cover can be attributed to the pine plantations. Neither the size of plantations nor their orientation relative to the prevailing winds had a detectable effect on invasions (Table 2). ROBUSTNESS CHECKS
The Rosenbaum sensitivity test values in Tables 1 and 2 indicate how sensitive the treatment effect estimates are to a potential unobserved covariate (values closer to 1 imply high sensitivity to hidden bias) that is related to where pine plantations are established and pine invasions. For example, for the matching-with-regression estimate in Table 1, column 1, if an unobserved covariate caused the odds ratio (gamma value) of protection to differ between treatment and control units by a factor as much as 254, the treatment effect estimate would still remain significant at the P-value level of 010. With a gamma value of 2, the 90% confidence interval would still exclude zero. The 3–6 km treatment effect estimates (Table 1, column 2) are however more sensitive to possible hidden bias. We found no significant difference in non-pine cover for the 6 km
