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Contributed Paper

Use of Modified Threat Reduction Assessments to

Estimate Success of Conservation Measures withinand Adjacent to Kruger National Park, South Africa

BRANDON P. ANTHONY

Environmental Sciences & Policy Department, Central European University, Nador u. 9, Budapest 1051, Hungary,

email [email protected]

Abstract: The importance of biodiversity as natural capital for economic development and sustaining humanwelfare is well documented. Nevertheless, resource degradation rates and persistent deterioration of humanwelfare in developing countries is increasingly worrisome. Developing effective monitoring and evaluation

schemes and measuring biodiversity loss continue to pose unique challenges, particularly when there is a

paucity of historical data. Threat reduction assessment (TRA) has been proposed as a method to measure

conservation success and as a proxy measurement of conservation impact, monitoring threats to resources

rather than changes to biological parameters themselves. This tool is considered a quick, practical alternative

to more cost- and time-intensive approaches, but has inherent weaknesses. I conducted TRAs to evaluate the

effectiveness of Kruger National Park (KNP) and Limpopo Province, South Africa, in mitigating threats to

biodiversity from 1994 to 2004 in 4 geographical areas. I calculated TRA index values in these TRAs by using

the original scoring developed by Margoluis and Salafsky (2001) and a modified scoring system that assigned

negative mitigation values to incorporate new or worsening threats. Threats were standardized to allow

comparisons across the sites. Modified TRA index values were significantly lower than values derived from the

original scoring exercise. Five of the 11 standardized threats were present in all 4 assessment areas, 2 were

restricted to KNP, 2 to Limpopo Province, and 2 only to Malamulele municipality. These results indicate, first,the need to integrate negative mitigation values into TRA scoring. By including negative values, investigators

will be afforded a more accurate picture of biodiversity threats and of temporal and spatial trends across sites.

Where the original TRA scoring was used to measure conservation success, reevaluation of these cases with the

modified scoring is recommended. Second, practitioners must carefully consider the need and consequences

of generalizing threats into generic categories for comparative assessments. Finally, continued refinement of

the methodology and its extension to facilitate the transfer of successful conservation strategies is needed.

Keywords: biodiversity threats, conservation success, Kruger National Park, Limpopo Province, threat reductionassessment

Uso de Evaluaciones de Reduccion de Amenaza Modificadas para Estimar el Exito de Medidas de Conservacion

Dentro de y Alrededor del Parque Nacional Kruger, Africa del Sur

Resumen: La importancia de la biodiversidad como capital natural para el desarrollo economico y sost endel bienestar humano est a bien documentada. Sin embargo, las tasas de degradaci on de los recursos y el

persistente deterioro del bienestar humano en los pa ses en desarrollo es cada vez mas preocupante. El

desarrollo de esquemas efectivos de monitoreo y evaluaci on y la cuantificaci on de la perdida de biodiversidad

continuan presentando retos unicos, particularmente cuando existe escasez de datos hist oricos. La evaluaci on

de la reducci on de riesgos (ERR) ha sido propuesta como un metodo para medir el exito de la conservaci on

y como una medida del impacto de la conservaci on, que monitorea las amenazas a los recursos en lugar de

los cambios en los par ametros biol ogicos mismos. Esta herramienta es considerada una alternativa r apida

y pr actica a metodos mas costosos y tardados, pero tiene debilidades inherentes. Realice ERR para evaluar

Paper submitted September 24, 2007; revised manuscript accepted April 28, 2008.

1497

Conservation Biology, Volume 22, No. 6, 14971505C2008 Society for Conservation Biology

DOI: 10.1111/j.1523-1739.2008.01030.x


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1498 Threat Reduction Assessment

la efectividad del Parque Nacional Kruger (PNK) y la Provincia Limpopo, Africa del Sur, en la mitigaci on de

las amenazas a la biodiversidad de 1994 a 2004 en cuatro areas geogr aficas. Calcul e los valores delndice

de ERR en estas ERR mediante la clasificaci on desarrollada por Margoluis & Salafsky (2001) y un sistema

de clasificaci on modificado que asigno valores de mitigaci on negativos para incorporar nuevas amenazas o

empeor andolas. Las amenazas fueron estandarizadas para permitir comparaciones entre todos los sitios. Los

valores delndice de ERR modificados fueron significativamente menores que los valores derivados del ejerci-

cio de clasificaci on original. Cinco de las 11 amenazas estuvieron presentes en todas las areas de evaluaci on,

dos estuvieron restringidas a PNK, dos a la Provincia Limpopo y solo dos a la municipalidad de Malamulele.

Estos resultados indican, primero, la necesidad de integrar valores de mitigaci on negativos a las ERR. Conla inclusi on de los valores negativos, los investigadores tendr an una imagen mas precisa de las amenazas

a la biodiversidad y de las tendencias temporales y espaciales de los sitios. Se recomienda que los sitios

medidos con la clasificaci on de ERR original sean reevaluados con la clasificaci on modificada. Segundo, los

practicantes deben considerar cuidadosamente la necesidad y las consecuencias de generalizar las amenazas

en categor as genericas para evaluaciones comparativas. Finalmente, se requiere el refinamiento continuo

de la metodolog a y su extensi on para facilitar la transferencia de estrategias de conservaci on exitosas.

Palabras Clave: amenazas a la biodiversidad, evaluacion de reduccion de amenazas, exito de conservacion,Parque Nacional Kruger Provincia Limpopo

Introduction

The importance of biological diversity as natural resourcecapital for economic development and sustaining human

welfare has been well documented (Ehrlich & Ehrlich1992; Costanza et al. 1997; Reaka-Kudla et al. 1997). Nev-ertheless, the rate at which natural resources continue tobe degraded and the persistent deterioration of human

welfare in developing countries have caused concern atlocal, national, and international levels (MEA 2005). This,in part, has led to greater scrutiny and development ofeffective monitoring and evaluation systems (Noss 1990;Margoluis & Salafsky 1998; Teder et al. 2007).

Nevertheless, monitoring the state of biodiversity isoften costly, arduous, and time-consuming and has ledto the development of a range of conservation perfor-mance assessments by various institutions that focus onthreats to biodiversity. These include the World Com-mission on Protected Areas (WCPA) Framework for As-sessing the Management of Protected Areas (Hockings etal. 2000), The Nature Conservancys (TNC) 5-S Frame-

work (TNC 2000, 2003), Wildlife Conservation Societys(WCS) Living Landscapes Program (WCS 2002), World

Wide Fund for Natures (WWF) Rapid Assessment andPrioritization of Protected Area Management (RAPPAM)

(Ervin 2003a, 2003b), and conservation audits with theConservation Measures Partnerships (CMP) Open Stan-dards (CMP 2004). Each of these conservation perfor-mance assessments has strengths and weaknesses on thebasis of their feasibility, accuracy, and ease of utility (e.g.,Stem et al. 2005; Tucker 2005). Despite these tools, how-ever, measuring biodiversity and its loss continues to poseunique challenges across scales, particularly where thereis a paucity of long-term data (Purvis & Hector 2000;Stedman-Edwards 2000; The Royal Society 2003; Wolman2006).

Threat reduction assessment (TRA) has been proposedas an alternative evaluation tool to assess the successof conservation interventions, especially where baselinestudies on biodiversity threats are absent (Salafsky &Margoluis 1999; Margoluis & Salafsky 2001). Threat re-duction assessments have been used to assess projectsin Papua New Guinea, Indonesia, Madagascar (Salafsky& Margoluis 1999), Mexico (Hastings & Fischer 2001),Kenya, Tanzania, and Uganda (Persha 2001; Mugisha &

Jacobson 2004), and are considered a quick, low-cost,practical alternative to more cost- and time-intensive ap-proaches. Rather than monitoring changes to biologicalparameters themselves, TRA monitors threats to the re-sources as a proxy measurement of conservation impact.It is sensitive to changes over short periods of time andthroughout a project site, allowing gross comparisons ofperformance among projects at different sites (Margoluis& Salafsky 2001). The key principle of TRA as an evalu-ation tool is that if threats to an area are mitigated, thenthe management will have succeeded. Conversely, if thethreats are not mitigated, the management approach willhave failed.

Nevertheless, the TRA approach is not immune to bias.Its category percent threat reduced is probably thebiggest pitfall in this respect because, in addition to po-

tential subjectivity on behalf of the participants (Salafsky& Margoluis 1999; Mugisha & Jacobson 2004), TRAs in-corporate no scoring mechanism to allow for threats thathad either arisen or worsened during the assessment pe-riod. In the TRA scoring (see Salafsky & Margoluis 1999),if a threat had not been addressed at all, management

would score zero. Where management had fully miti-gated a threat, the score would be 100%. Thus, accordingto this scoring, threats to biodiversity could only eitherremain as they were or have positive mitigation. My jus-tification for modifying the TRA was that the original

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assessment design provides an oversimplistic andpotentially overoptimistic view of agencies abilities tomitigate threats.

A second identified limitation concerns the standard-ization of threat types (Salafsky & Margoluis 1999). Anumber of attempts have been made to produce genericthreat categories, with some overlap (EPA 1998; TNC2000; Ervin 2003a; Salafsky et al. 2002; Sanderson et al.2002). These attempts have been more formalized withinthe now widely used unified classification of directthreats (IUCN-CMP 2006), where direct threats are de-fined as [t]he proximate (human) activities or processesthat have caused, are causing or may cause the destruc-tion, degradation and/or impairment of biodiversity andnatural processes. For practitioners working with scarceresources, standardization of threat types enhances cross-project learning and allows for greater application in thecomparison of temporal and spatial variation across sitesor even regions experiencing similar threats. Standardiz-ing threat types can also facilitate transfer of successful

mitigation strategies between sites.

Case Study

South Africa is endowed with diverse natural resourcesand is ranked as the third-most biodiverse country inthe world (DEAT 1998). Despite this richness, however,South Africas biodiversity is under threat due to habi-tat loss, overexploitation, alien invasive species, and thechallenge of resolving conflicts between protected areasand local people (DEAT 1998; Perrings 2000; Steenkamp& Urh 2000; Anthony 2007).

Through provincial legislation and necessary coordi-nating structures, environment and conservation depart-ments in each province play an important role in devel-oping collaborations with other departments responsiblefor activities concerning the conservation and use of bio-diversity within the province. In Limpopo Province En-

vironmental Affairs is a branch within the Departmentof Finance and Economic Development (DFED-EA). Thisbranch is operationally subdivided into municipal dis-tricts, which provide conservation extension services andregulate and monitor the use of natural resources. TheDFED-EAs activities are largely governed by the LimpopoEnvironmental Management Act (LEMA) Number 7 of

2003, which is aligned with national legislation. An addi-tional function of DFED-EA is to promote sustainable de-

velopment outside protected areas through the forging ofappropriate partnerships with communities, NGOs, theprivate sector, and other government departments.

Many authors have posited that conserving habitat inareas surrounding protected areas supports wildlife pop-ulations within it (Taylor 1982; Western & Gichohi 1993;Homewood et al. 2001), and this is one of the reasonsdriving efforts to maintain biodiversity outside KrugerNational Park (KNP) (Pollard et al. 2003). Recently, as-

sessments of river-corridor health in South Africa showthat human activities outside protected areas can signifi-cantly affect the status of these ecosystems in protectedareas downstream (Foxcroft et al. 2007; Nel et al. 2007).

South Africas dramatic policy changes in 1994 affectedmanagement of biodiversity within and outside protectedareas (Cock & Fig 2000; Mabunda et al. 2003). In spite ofthis benchmark year, no comprehensive baseline studies

were conducted at that time to assess threats to biodiver-sity in my study area, and there have been no subsequentattempts to evaluate the effectiveness of policy changescollectively within KNP and DFED-EA administrations inmitigating threats to biodiversity. To address this I usedTRAs with both the original and modified scoring meth-ods to assess the effectiveness of KNP and DFED-EA man-agement in mitigating threats to biodiversity along KNPs

western border over a 10-year period, commencing in1994. I chose TRA methodology because it allows forcomparison across sites (and institutions) and becauseit is a useful tool when little or no baseline data exist.

Furthermore, an attempt was made to standardize identi-fied threats into the IUCN-CMP classification to allow forspatial and institutional comparison.

Study Area

The western border of KNP, from the Punda Maria gatesouth to the Klein Letaba River (Fig. 1), has 3 of the 16ecozones that exist within the KNP: Mopane/Bushwillow

Woodlands, Sandveld, and Riverine (Jacana Education2000). Kruger National Park has 8 main river catchments,including the Shingwedzi and Letaba in the study area.

Annual precipitation ranges from 500 to 700 mm in the

area (Jacana Education 2000).Land use adjacent to the western border of the KNP

is characterized by slightly undulating plains containingvillages surrounded by areas for subsistence farming. Inaddition, there remain relatively sizeable vacant, bush-land areas, especially between the Shingwedzi and KleinLetaba rivers (DWAF et al. 2001). Adjacent areas are de-marcated from the KNP with a boundary fence origi-nally intended to control the spread of foot-and-mouthdisease.

Methods

I conducted 2 TRAs by organizing group discussions in August 2004 with KNP representatives from the man-agement and law enforcement departments from eachof the primary KNP ranger sections in the study area(Punda Maria, Shangoni). I conducted 2 additional TRAs

with DFED-EA staff from the Greater Giyani and Mala-mulele municipality offices. Participants either held po-sitions within the study area at the time of the TRA orhad worked in the area for at least 10 years and were fa-miliar with local biodiversity and its threats. To avoid


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Figure 1. (a) Location of Kruger

National Park (KNP) in South

Africa and study area and (b)areas for threat reduction

assessment (TRAs) (dotted lines)

and KNP border (bold line).

redundancy during discussions, I secondarily selectedparticipants so the group would be diverse but wouldbe able to communicate in the same language (Krueger1994).

First, I asked KNP and DFED-EA staff to identify, in theirown words, all threats to biodiversity, concentrating theirassessment on the period specified and area within their

jurisdiction. Jurisdictions were Punda Maria (KNP borderto 5 km inside park, from Luvuvhu River to the southend of ranger section); Shangoni (KNP border to 5 kminside park, along entire western edge of ranger section);Malamulele (KNP border fence to 15 km outside park,between the Luvuvhu and Shingwedzi rivers); and Giyani(KNP border fence to 15 km outside park, between theShingwedzi and Klein Letaba rivers).

Threats were defined as any human-related phenom-ena that negatively affect biodiversity of the area in ques-tion (Margoluis & Salafsky 2001). Natural phenomena,such as natural fires, were not considered threats, al-though illegally lit fires were.

Second, to help participants focus their thinking aboutspecies richness, habitat condition and area, and ecosys-tem functioning, they were asked to rank threats accord-ing to their relative importance to one another. They con-sidered the portion of habitat(s) in the site that the threataffects; the impact or severity of destruction caused bythe threat; and the urgency or immediacy of addressingthe threat. A total sum score was computed after all thethreats were ranked.

Third, a consensus building exercise was used with thegroups to assess the extent to which the KNP or DFED-EA

management had mitigated each threat. All participants were given approximately 5 min to think about eachthreat and independently evaluate the extent to whichmanagement approaches had addressed a specific threat.Scores were assigned on a percentage basis. In contrastto the original TRA scoring, the option for a negativemitigation score was added for cases in which threats

had worsened, and a score of 100% was assigned to thethreat if it arose after1994 and had not been mitigated. Af-ter the scoring and ranking exercise, total ranking scoresfor each threat were multiplied by the percentage of thethreat met to get a raw score for that threat. The threatreduction index (TRA-I) value was derived by dividingthe sum of the raw scores for each threat by the totalpossible rankings of all the threats and multiplying by100: (TRA-I = total raw scores / total rankings 100) (Margoluis & Salafsky 2001). Where participants in-dicated either a new or worsening threat, I inserted theirestimate of the degree to which the threat had worsenedinto the TRA worksheet alongside the value (0) accord-

ing to the original TRA scoring design. This procedurewas carried out for the 2 ranger sections and 2 munici-palities so a meaningful comparison of the indices insideand outside the park and throughout the study area couldbe made. I compared original and modified TRA-I valuesfor the 4 areas with a paired ttest, which allows compar-isons of means between paired data (Wheater & Cook2000). Threat definitions and what would constitute100% mitigation for each threat across all assessment ar-eas are indicated in the TRA worksheets (see SupportingInformation).


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Finally, an attempt was made to standardize threattypes by combining all threats for the 4 assessment areasinto general categories on the basis of IUCN-CMP classifi-cation (IUCN-CMP 2006). The standardized threats werethen prioritized on the basis of their presence or absencein the 4 assessed areas.

Table 1. Modified threat reduction assessment (TRA) results for Malamulele, Punda Maria, Giyani, and Shangoni assessment areas in South Africa.

Assessment Total Percent threat Rawarea Threat a Area Intensity Urgency rank reduced b scoreb

Malamulele illegal commercial harvesting of trees 10 7 10 27 50 (0) 13.5 (0)agricultural expansion 6 9 8 23 40 (0) 9.2 (0)illegal harvesting of trees for subsistence 7 6 9 22 10 (0) 2.2 (0)subsistence poaching 9 8 4 21 30 6.3mining sand 2 10 5 17 40 6.8illegal fire 4 5 7 16 60 9.6residential expansion 5 4 6 15 20 (0) 3 (0)commercial poaching 8 2 3 13 40 5.2road construction/maintenance 3 3 2 8 60 4.8

disease transfer 1 1 1 3 50 1.5

total 55 55 55 165 6.3 (34.2)TRA index 3.8 (20.7)

Punda Maria poaching with dogs and/or snares 11 5 10 26 15 (0) 3.9 (0)poaching fish 5 11 8 24 30 (0) 7.2 (0)alien species 10 7 6 23 70 16.1illegal harvesting of trees for medicine 2 10 11 23 60 (0) 13.8 (0)illegal fire 9 8 5 22 0 0poaching with firearms 6 4 9 19 80 15.2illegal harvesting of live 3 9 4 16 0 0

trees and/or dry woodincreasing elephant population 8 6 1 15 60 (0) 9 (0)highly infectious alien diseases 7 2 3 12 80 (0) 9.6 (0)commercial huntingluring lions 1 3 7 11 5 0.55

endemic disease transfer 4 1 2 7 15 1.05total 66 66 66 198 10.6 (32.9)TRA index 5.4 (16.6)

Giyani illegal harvesting of trees for subsistence 7 4 8 19 60 11.4illegal fire 4 8 6 18 30 5.4illegal commercial harvesting of trees 5 5 7 17 20 3.4subsistence poaching 8 2 4 14 50 7mining sand 2 7 5 14 50 (0) 7 (0)commercial poaching 6 3 3 12 40 4.8road construction/maintenance 1 6 2 9 50 4.5disease transfer 3 1 1 5 95 4.75

total 36 36 36 108 34.25 (41.25)TRA index 31.7 (38.2)

Shangoni poaching wild animals 8 4 8 20 90 18

poaching fish 1 8 7 16 50 8illegal fires 5 7 4 16 70 11.2poaching grass/trees 7 2 5 14 50 7commercial hunting 3 5 6 14 100 (0) 14 (0)increasing elephant population 4 6 2 12 50 (0) 6 (0)disease transfer 6 3 1 10 50 5alien plant species 2 1 3 6 80 4.8

total 36 36 36 108 34 (54)TRA index 31.5 (50.0)

aDetailed description of threats available (see Supporting Information).bValues in parentheses incorporate original TRA scoring (see Methods).

Results

The TRA-I values according to the original scoring rangedfrom 16.6 (Punda Maria) to 50.0 (Shangoni), whereas val-ues incorporating the modified scoring ranged from5.4(Punda Maria) to 31.7 (Giyani) (Table 1 & Fig. 2). Modified


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Figure 2. Comparison of threat reduction assessment

(TRA) index values for assessment areas with original

and modified scoring.

TRA-I values were significantly lower than values incor-porating the original TRA scoring (t= 4.793, df= 3, p 100 (e.g., Baumert

et al. 2005; Parsley & Halabisky 2008), especially whenmaking comparisons with a reference. Thus, it makessense to adopt this terminology (rather than instituting acap of 100% mitigation) when calculating the degree to

which a threat has worsened over an assessment period.If original TRA values have been used to measure con-

servation success, reevaluation of these cases may bevaluable. This should be done if TRAs have been usedto develop strategies for resource management (Hast-ings & Fischer 2001) or when comparing effectivenessof conservation approaches (Mugisha & Jacobson 2004).In such cases, results are likely to be less optimistic, with

TRA-I values remaining where they are in best-case sce-narios, but in all likelihood decreasing when incorporat-ing any new or worsening threats.

The standardization of threats continues to challengeTRAs. I attempted to align categories with the IUCN-CMPdirect threat classification on the basis of the nature of thethreat, akin to content coding in qualitative data analysis(Taylor & Bogdan 1984; Weisberg et al. 1996). On onehand, this categorization has helped KNP and DFED-EAstaff conceptualize local threats to biodiversity and maylead to more focused action and appropriate response.On the other hand, generalizing threats, similar to cod-ing qualitative data, may inevitably result in a loss of rich-

ness of the threat description, leading to responses thatmay not necessarily address the locally identified threat.For example, subsistence poaching, poaching with dogsor snares, poaching with firearms, and luring of lionsfor illegal commercial hunting are all combined into thehunting & collecting terrestrial animals (IUCN-CMP no.5.1) categorical threat in this study. Nevertheless, strate-gies to combat each of these threats may require quitediscrete policies and actions, drawing on different legis-lation and institutions. Careful attention must be takento understand the reasons for threat standardization and

where potential ineffective and counter-productive re-sponses may be made as a result of such generalizations.For example, if the goal of the assessment is to prior-itize proximate threats and thus to choose appropriateconservation strategies, developing subcategories withinbroader threat classifications may be warranted. In do-ing so, more detailed threat descriptions can be retained.

Alternatively, assessments could be conducted in a step-wise fashion considering scale (i.e., local or site, regionalor national, and international), with codes at each levelallowing threats to be summarized, depending on thescale being investigated. In this way, detailed informa-tion is not lost, but can be generalized when necessaryto understand larger-scale threats and trends.

Finally, although I propose modifications to TRA thatshould lead to a more accurate assessment of threats tobiodiversity, continued refinement of the approach isneeded. The following are particular avenues for con-tinuing efforts that would improve the usefulness of thistool.

Identify techniques to incorporate threat magnitude.For example, if illegal mining of sand affected 1 river inan assessment site and this threat increased to 3 riversover the assessment period, then the threat would haveincreased by 200%, and mitigation would be given aTRA score of200%. In such cases in which threatshave more than doubled, the original TRA scoring hasan inherent weakness in that it cannot accurately mea-sure such changes. I propose defining 100% mitiga-tion as the complete elimination of a threat, but donot suggest an upper limit to how bad a threat canpotentially become (allowing for threats to increase>100%). Because TRAs use relative rather than abso-lute values, how to standardize this aspect of threatmagnitudes will need further development if cross-sitecomparisons are to be made.

To improve application of the technique, TRAs shouldbe conducted at the onset and over multiple time pe-riods of a conservation intervention to identify trendsin specified threats. In my study, no such baseline as-sessment was made, and results would have benefitedfrom previously conducted TRAs. Longitudinal studiesutilizing TRAs would improve the adaptive manage-ment utility of the approach and allow practitioners

to estimate probable time frames for reaching 100%abatement.

Finally, compiling a database of successful strategiesto combat specific threats, which can be easily ac-cessed by managers, practitioners, and researchers,

would be a useful secondary outcome of the TRAmethodology. This may be integrated into ongoing ini-tiatives including the IUCN and CMPs Classification ofConservation Actions [http://www.iucn.org/themes/ssc/sis/classification.htm] or Conservation Evidence[http://www.conservationevidence.com/].


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Acknowledgments

I thank the CEU Doctoral Research Support Program forfunding, staff from Conservation Services and People &Conservation (KNP), and Mopani and Vhembe districts(DFED-EA) for support. I also thank 3 anonymous review-ers and the assigning editor for constructive commentson an earlier version of this manuscript.

Supporting Information

The TRA worksheets for Malamulele (Appendix S1),Punda Maria (Appendix S2), Giyani (Appendix S3), andShangoni (Appendix S4) are available as part of the on-line article. The author is responsible for the contentand functionality of these materials. Queries (other thanabsence of the material) should be directed to the corre-sponding author.

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