Characterization of soils from the Algarve region (Portugal): A multidisciplinary approach for forensic applications

Science and Justice 51 (2011) 77–82

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Science and Justice j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c i j u s

Characterization of soils from the Algarve region (Portugal): A multidisciplinary approach for forensic applications Alexandra Guedes a,⁎, Helena Ribeiro a, Bruno Valentim a, Andreia Rodrigues a, Helena Sant'Ovaia a, Ilda Abreu b, Fernando Noronha a a b

Centro de Geologia e DGAOT da Faculdade de Ciências da Universidade do Porto, Portugal Centro de Geologia e Departamento de Biologia da Faculdade de Ciências da Universidade do Porto, Portugal

a r t i c l e

i n f o

Article history: Received 9 June 2010 Received in revised form 14 September 2010 Accepted 29 October 2010 Keywords: Spectral colour Particle size distribution Magnetic susceptibility Pollen content

a b s t r a c t The Algarve is located at a very short distance from North Africa, in Southern Portugal, and as one of the most touristic regions of Portugal, it is accessible by air, land and sea. It is very susceptible to many illegal activities, such as illegal migration, drug trafficking, kidnapping, and murder, among others. Therefore, an Algarve soils database for forensic purposes is being conducted with the conjunction of geological and palynological methodologies on soils characterization, since this is of fundamental importance to assess reliable evidence on forensic investigations. In this study, the properties of soils from several proximate sites from the Algarve were investigated, namely: (i) colour determined by spectrophotometry; (ii) particle size distribution determined by laser granulometry; (iii) low-field magnetic susceptibility by a susceptibility meter; and (iv) pollen content using a light microscope. Finally, a hierarchical cluster analysis was applied to ascertain the capacity of the different soil properties for discrimination between samples. The study reveals the utility of geobotanical techniques for forensic discrimination of soils. Even though some similarities between some of the samples were found, each one presented a combination of colour, particle size distribution, magnetic susceptibility and pollen features that enable the determination of a fingerprint expected to reveal a specific site for future selection of coastal search areas in the Algarve region. © 2010 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction Soil is a complex and heterogeneous material composed of inorganic and organic matter. The ability to characterise their components at an increasing level of detail makes soil an important trace evidence for forensic investigations. Several analytical methods for soil characterization have been well established in forensic science [1–15]. Also important in forensic investigations is the comparison of questioned samples with one or more samples of known origin referenced in a database, and the quantification of the degree of similarity or dissimilarity observed between samples and its significance [1–4]. Nowadays, soil databases for forensic purposes are being developed, most of them in the UK [16,17] and in the USA [18] where geoforensics is relatively well advanced. Therefore, in Portugal, a sediments/soils database for forensic purposes is being developed and the sediments/soils analyzed and studied by the authors using different techniques. At the present time, this database has more than 200 ⁎ Corresponding author. Centro de Geologia da Universidade do Porto e DGAOT da Faculdade de Ciências, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal. Tel.: +351 220402474; fax: +351 220402490. E-mail address: [email protected] (A. Guedes).

geographically well-referenced samples from coastal Portugal. The Algarve samples used in our study, in addition to completing the reference soil database under development, also have the particularity of being proximate and therefore ascertain if it will be possible to discriminate them based on its geobotanical characteristics. Thus, samples that are geographically well-referenced in the database provide fast and relevant information for future selection of search areas for forensic purposes. The database will provide valuable contextual information on the characteristics of the samples collected [4] that are representative and that can be comparable, allowing for exclusion within the framework of sample variability rather than inclusion [1]. The Algarve region has a vast seashore, it is a short distance from North Africa, and has great tourist activity all year round, even in low season, a place where many foreign tourists come to enjoy the sunny and warm Mediterranean climate, and its beautiful landscape and relaxed walks on the many footpaths along the shore. These characteristics make this region very susceptible to many illegal activities, such as illegal migration, drug trafficking, kidnapping, and murder, among others. One can mention the famous and internationally spoken about disappearance of Madeleine McCann in Praia da Luz-Algarve as an example of the forensic relevance of this region. Furthermore, every year the Portuguese Police seize large amounts of drugs in Algarve's many coastal cliffs.

1355-0306/$ – see front matter © 2010 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.scijus.2010.10.006

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Therefore, these cliffs' superficial soil can be useful in investigative intelligence, either as a known origin sample referenced in a database or via its retrieval from a suspect's clothing, footwear, vehicle, or crime scene. In forensic investigation, modern statistical methods are available to support and confirm impressions and interpretations of investigations both in the field and the laboratory [19,20]. Classification methods, such as cluster analysis, allow for the identification of groups on the basis of dissimilarity or similarity measurement [19] and can allow the formation of a potential link or unlink to a suspect or crime scene, as well as the reduction of the possibility of false-positive results [12,15]. The scope of this study was focused on the characterization of some soil properties of several proximate sites from Algarve, namely: colour quantification, particle size distribution, magnetic susceptibility and pollen content and to ascertain the potential of the tested characteristics to discriminate samples within the same geographical area. This data will contribute to a better knowledge of using different techniques and interdisciplinary methods in soil characterization. 2. Materials and methods 2.1. Sample collection and handling In February 2008, nine soil samples from nine different sites were collected in the Algarve (Portugal) region (Fig. 1, Table 1). These sampling sites are located relatively close together, in the South Portuguese Meso-Cenozoic borderland, and correspond to cliff areas. The bedrock of the studied soils consists of Jurassic and Cretaceous limestones and/or marls (AG4, AG11, AG12, AG14, AG15 and AG20), Miocene carbonate formation (AG1 and AG22), and Pleistocene sandstones (AG8). During soil sampling, a plant inventory of each site was also performed. The sites are surrounded by dune forests, mainly Pinus spp. and Olea sylvestris-Querceto suberis sigmetum. The soil cover is dominated or co-dominated by thorny shrub and bush communities. It also has Mediterranean salt grasslands, halophilous vegetation (Mediterranean and thermo-Atlantic) and other halonitrophilous annual species. In order to characterize the nine soil samples, and in the framework of a forensic science study, 200 to 400 g per sample of soil at each site were manually collected from the surface soil (b5 cm depth), with a plastic spade (carefully cleaned after each sampling) along transects perpendicular to the coastline and put into a plastic bag, following the procedures described by Saye and Pye [16] to develop a coastal soil

database for forensic applications. After, the samples were dried on a stove at 40 °C and divided into two sub-samples, one was kept as a duplicate and stored into a plastic box kept in a cool place, and the other used to perform several analyses: colour, particle size distribution, magnetic susceptibility and pollen content.

2.2. Colour analysis Several experiments on soil colour description and comparison have been conducted throughout the years, comparing various soil presentation/pre-treatment methods prior to colour testing (air-drying, wetting, organic matter decomposition, iron oxide removal, ashing, size fraction separation and milling) [7,13,21–23]. Variations in the L*a*b* values between different presentation/pre-treatment methods were demonstrated from the results obtained. Guedes et al. [13] demonstrated that the measured L*a*b* values on dried, unsieved bulk samples allowed for higher discrimination between samples than measures performed on other presentation/ pre-treatment methods. Croft and Pye [7] suggested that colour should be measured on the bulk material and on a specific size fraction according to the soil nature. Colour measurements were performed on bulk samples using a Konica Minolta CM-2600d spectrophotometer programmed with the following settings: measurement area of 0.8 mm of diameter; specular component included; CIE Standard Illuminant D65 corresponding to the average daylight at a temperature of 6504 K, including the ultraviolet wavelength region; CIE 1964 Standard Observer (10° Observer). The spectrophotometer was set to take three sequential measurements, giving the colour coordinates obtained means. Before measurement, the spectrophotometer was calibrated according to the manufacturers' instructions and regularly calibrated throughout the measurement period due to the repeated nature of the measurements carried out. Negative calibration was performed directing the apparatus measuring port into the air while positive calibration was performed with an international standard white calibration plate. Sample material was homogenised and presented in a standard glass petri dish and an average of five to eight sub-samples taken from the original sampled collected at each site were measured. The colour parameters recorded correspond to the uniform colour space CIELAB and were directly computed by the spectrophotometer through the SpectraMagic NX software.

Fig. 1. Simplified geological map of Southern Portugal and sampling locations at Algarve.

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Table 1 Sampling sites location, underlying geology and surrounding vegetation. Sample

Location

Underlying geology

Surrounding vegetation

AG1 AG4

Access to Portimão's Marine Top of cliff

Dune forests mainly of Pinus spp. and Olea sylvestris-Querceto suberis sigmetum.

AG8

Situated between railway line and S. Roque beach (1200 m) Top of cliff in Marinha Beach Resort Residence, in Sagres Top of cliff in Ponta de Sagres, between Rua da Fortaleza (50 m) and EN 268-2 (200 m) Top of cliff in Mareta beach in Sagres, between Rua do Infante D. Henrique (250 m) and Rua Comandante Matoso (175 m) Top of cliff near EM 1257 and Praia da Ingrina Top of cliff in Burgau beach between Rua 25 de Abril (100 m) and Largo dos pescadores (40 m) Top of cliff in Almagem beach, in Loulé

Miocene carbonate formation Jurassic and Cretaceous limestones and/or marls Pleistocene sandstones Jurassic and Cretaceous limestones and/or marls Jurassic and Cretaceous limestones and/or marls Jurassic and Cretaceous limestones and/or marls

Soil cover dominated or codominated by thorny shrub and bush communities.

AG11 AG12 AG14

AG15 AG20 AG22

Jurassic and Cretaceous limestones and/or marls

Mediterranean salt grasslands, halophilous vegetation (Mediterranean and thermo-Atlantic), other halonitrophilous annual species.

Miocene carbonate formation

2.3. Particle size distribution This is a technique that allows sample weights of only 50 mg to be analysed [2,4] and the results obtained can give important information to infer about soil or sediments nature and provenance. Wet sieving was conducted for particle size distribution, using a set of Retsch® stainless steel sieves. The b63 μm size fraction was automatically determined using a Coulter LS130 Laser Beam Granulometer, coupled with a liquid module, at LNEG - National Laboratory of Energy and Geology (Porto, Portugal) and the evaluation of the accuracy and precision of this method was regularly tested. 2.4. Magnetic susceptibility Magnetic susceptibility is directly proportional to the quantity and grain size of ferromagnetic materials in the sample which can be originated by desegregation of parent rocks during pedogenesis, by lithogenic processes, and by anthropogenic activities. The accuracy and precision of this analytical method is regularly tested. Magnetic susceptibility is a measure of the magnetic response of a material to an external magnetic field. The specific or mass susceptibility χ, measured in units of m3/kg, is defined as the ratio of the material magnetization J (per unit mass) to the weak external magnetic field H: J = χ H. Magnetic susceptibility determination was performed with a Kappabridge, model KLY-4S of Agico balance equipped with the Sumean software. Measures were performed on 1 g of each sample after homogenisation, the magnetic susceptibility (χ) being calculated. 2.5. Pollen analysis For palynological studies, 5 g of soil were removed from the subsample. Afterwards, samples were dried, sieved at 200 μm through a disposable mesh and a series of chemical procedures were carried out, aiming to obtain rich pollen residue for qualitative and quantitative analysis based on the techniques outlined in a technical note published by Horrocks [24] where a guide for sub-sampling and preparing forensic samples for pollen analysis is presented. Subsequently, the pollen residues were mounted on microscope slides in glycerol jelly. It was necessary to count more than two replicates for some samples, up to a maximum of four replicate slides, depending on the total number of pollen grains registered, to obtain representative pollen counts (even though one sample still presented pollen counts lower than 100). The sum of the replicate counts was considered for each sample. Pollen qualification and quantification was carried out using a light microscope at a magnification of ×400

along ten equidistant full lengthwise traverses for every slide. Pollen counts for each type identified were then converted into percentages of the total pollen counts. Pollen grains were classified by appearance and morphological characteristics and identified, where possible, by comparison with bibliographic material [25–27].

2.6. Statistical analysis Appropriate descriptive statistical analysis for each measured property was performed. Reproducibility within-sample was evaluated by the coefficient of the variation in terms of colour analysis, with L*a*b* colour parameters, and magnetic susceptibility. A hierarchical cluster analysis was performed in order to ascertain if it is possible to obtain discrimination between samples by combining the results of colour, particle size distribution, magnetic susceptibility and pollen analysis. The number of clusters was determined using: i) the Euclidean distance as a distance measure and ii) the Ward method as a linking method. The statistical analysis software that was used for all the analysis was SPSS (16.0).

3. Results and discussion 3.1. Colour analysis Concerning the L*a*b* system colour sphere (Table 2), L* values measured varied between 74.14 and 46.23 (samples AG14 and AG15, respectively); a* varied between 12.95 and 2.73 (samples AG22 and AG14, respectively); b* between 20.00 and 10.27 (samples AG22 and AG20, respectively). The coefficient variation of L*a*b* values obtained were always lower than 5%, giving measure reproducibility. The lowest values were observed on L*, while the parameter with the highest variation for all samples was a* (Table 2). Guedes et al. [13] also reported the a* parameter as the one with highest overall within-sample variation. When analysing the chromaticity diagrams (Fig. 2), which represent the relationship between a* and b* measures, it was observed that a* and b* always presented positive values, indicating that samples are positioned in the saturation zone closest to the red and yellow continuums. Reflectance values over the 400–700 nm range were also compared (Fig. 3) for the studied samples. Within the latter set, samples AG14, AG15, and AG20 may be discriminated, since they clearly show distinct reflectance curves.

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Table 2 Algarve soil samples: descriptive statistics of L*a*b* indices, particle size, and magnetic susceptibility parameters of each sample. CV — Coefficient of variation. Colour measures

Particle size distribution (μm)

L*

a*

Magnetic Susceptibility (×10−8 m3/kg)

b*

Samples

Mean

CV%

Mean

CV%

Mean

CV%

Mean

Median

Mode

D10

D25

D75

D90

D90 - D10

Value

CV%

AG1 AG4 AG8 AG11 AG12 AG14 AG15 AG20 AG22

58.77 57.13 55.79 53.63 56.32 74.14 46.23 59.82 58.94

0.2 0.4 0.3 0.5 0.5 0.5 0.3 0.6 1.6

10.68 9.18 7.94 5.95 10.89 2.73 11.18 4.47 12.95

1.5 0.3 1.0 1.4 2.1 1.5 1.7 2.6 4.4

19.49 16.61 16.91 13.08 19.38 15.54 11.24 10.27 20.00

1.7 0.7 0.5 1.7 2.0 0.5 0.7 2.6 1.5

26.44 25.46 39.31 26.91 23.85 100.90 30.55 25.45 20.76

19.39 20.30 32.92 24.78 19.98 49.48 28.48 20.34 14.95

38.91 35.52 56.00 32.43 27.03 73.59 35.52 42.62 18.78

1.83 1.61 3.32 2.61 1.95 2.16 3.87 1.48 1.92

7.38 7.09 12.46 12.06 9.01 10.89 15.44 6.37 6.09

38.92 37.52 55.47 38.79 32.71 101.20 42.04 40.18 26.70

59.92 55.24 76.00 53.10 47.95 245.80 55.91 57.24 47.82

58.09 53.63 72.68 50.49 46.00 243.64 52.04 55.76 45.91

14.69 24.93 6.09 124.80 9.47 3.53 141.51 4.95 10.73

0.5 0.5 3.5 0.3 0.8 3.1 0.3 1.2 1.4

3.2. Particle size distribution Particle size descriptive parameters (Table 2) showed a mean size distribution between 20.76 μm and 100.90 μm; a mode between 18.78 μm and 73.59 μm; a median between 14.95 μm and 49.48 μm; and, the soil sorting varies between 45.91 μm and 141.51 μm. The lowest values of these parameters were always observed in sample AG22, and the highest in sample AG14. Fig. 4 compares particle size distribution curves, determined by laser diffraction, obtained from the different samples. With the exception of curves from samples AG8 and AG14, with particle size distribution patterns mainly located in the very coarse silt class, the other samples have similar curve shape. 3.3. Magnetic susceptibility The MS values range between 3.53× 10−8 m3/kg and 141.51 × 10−8 m /kg, with the lowest values obtained in sample AG14 and the highest in AG15. The coefficient variation obtained was always lower than 4%, giving measure reproducibility (Table 2). 3

3.4. Plant inventory and pollen analysis A plant inventory of each site was performed (Table 3) for further helping in the pollen identification. The inventory showed qualitative vegetation similarity between all sampling sites, with differences occurring in representativeness of the several species. This was reflected on the results of pollen analysis of soils. Thirty-eight pollen-types were identified in the nine samples collected (Table 4), but only 10 pollen-types were present in all samples: Asteraceae, Brassicaceae, Chenopodiaceae–Amaranthaceae, Cyperaceae, Erica spp., Pinus spp., Plantago spp., Poaceaea, Quercus spp. and Urticaceae.

Samples AG1 and AG11 were dominated by Asteraceae (majority of Liguliflora type), Chenopodiaceae–Amaranthaceae (majority of Beta vulgaris type) and Poaceae, although with frequency differences. AG1 also presented considerable amounts of Brassicaceae pollen and more pollen type's diversity; while AG11 presented Plantago spp. pollen. Representing more than half of the pollen assemblages, Chenopodiaceae–Amaranthaceae (majority of Beta vulgaris type) dominated in samples AG4 and AG20. Sample AG4 also presented abundant pollen of Asteraceae (majority of Liguliflora type), while in AG20 it was Plantago pollen. Samples AG12 and AG14 were dominated by the pollen type Poaceae, Plantago spp. and Asteraceae, although in AG12, the majority Asteraceae was of the Tubuliflora type, while in AG14 it was of Liguliflora. Sample AG15 was dominated by pollen Plantago spp., Poaceae and Asteraceae (equivalent representativeness of Liguliflora and Tubuliflora types). Finally, representing half of the pollen assemblages, Plantago spp. dominated in sample AG8. This sample also had the particularity of presenting considerable pollen from Pinus spp. Sample AG22 was palynologically very poor, yielding relatively sparse palynomorphs and, therefore, was not considered in this analysis. Although all samples contain qualitative similarities in terms of dominant pollen types, each one presented a characteristic fingerprint, being likely to reflect some combination of pollen from a specific location. As an example, samples AG12 and AG14 separated by approximately 360 m have composition differences that enabled sample discrimination, pointing out that localized areas of similar

25 AG1

20 AG8

AG4

AG14

b 15

AG22 AG12

AG11 AG15

AG20

10 5 2

4

6

8

10

12

14

16

a Fig. 2. Chromaticity diagrams: scatter plot of a* and b* values measured on different studied samples.

Fig. 3. Reflectance curves (400–700 nm) for the different studied samples.

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Table 4 Percentage frequencies of pollen assemblages observed in the 9 surface soil samples of Algarve. Sample frequencies (%) Pollen types

Fig. 4. Particle size distribution obtained on the different studied samples.

vegetation, even within the same geographical region, can have significantly different pollen assemblages [5].

3.5. Cluster analysis In the cluster analysis (Fig. 5), colour, particle size distribution, magnetic susceptibility and pollen analysis were important to find between-site dissimilarities. The clustering of more similar samples was considered in the following groups with linkage distances higher

Table 3 Plant inventory from sampling sites in Algarve, south of Portugal. Plant Family

Species

Family

Species

Aizoaceae Alliaceae

Carpobrotus edulis Narcissus bulbocodium Beta vulgaris Pistacia lentiscus Arisarum vulgare Asphodelus spp. Calendula arvensis Carlina spp. Sonchus oleraceus Centaurea pullata Crysanthemum coronarium Senecio vulgaris Dittrichia viscosa Belis anua Anacyclus radiatus Heliechrysum italicum Arctotheca calendula Echium plantaginium Lithodora prostrata Lobularia maritimum Raphanus raphanistrum

Fabaceae

Ononis spp. Cytisus scoparius

Amaranthacae Anacardiaceae Araceae Asphodelaceae Asteraceae

Borraginaceae Brassicaceae

Caryophylaceae Chenopodiaceae Chenopidium murale Cistaceae Halimium spp. Cistus salvifolius Cistus ladaniferus Cistus monspeliensis Convovulaceae Convovulus althacoides Crassulaceae Sedum sediforme Cupressacea Juniperos spp. Euphorbiaceae Euphorbia spp. Fabaceae Acacia longiflia Acacia saligna Lotus ceticus Medicago spp. Medicago marina Medicago polymorpha

Fagaceae Frankeniaceae Fumariaceae Geraniaceae Gramineae

Lamiaceae

Erophaca baetica Ulex spp. Quercus spp. Frankenia spp. Fumana spp. Erodium cicutarium Erodium malacoides Ordeum spp. Gramineae

Rosmarinus officinalis Salvia spp. Teucrium spp. Lavandula spp. Phlomis purpurea Liliaceae Asparagus albus Malvaceae Lavatera arborea Myrtaceae Eucalyptus spp. Moraceae Ficus carica Oleaceae Olea europaea Oxalidaceae Oxalis pes-caprae Plantaginaceae Plantago spp. Pinaceae Pinus pinaster Rhamnaceae Rhamus oleoides Rosaceae Prunus dulcis Rubiaceae Galium spp. Scrophulariaceae Myoporum acuminatum Umbeliferae Margotia gummifera Ridolfia sagetum Daucus spp. Eryngium maritimum Urticaceae Urtica menbranaceae Parietaria spp. Valerianaceae Fedia cornucopiae Violoceae Viola arborescens

AG1

AG4

AG8

AG11 AG12 AG14 AG15 AG20 AG22

Present in all samples Asteraceae 18.4 Liguliflora 15.6 Tubuliflora 2.5 Brassicaceae 13.6 Raphanus 7.7 Lobularia – Chen-Amar 21.2 Beta vulgaris 19.1 Chenopodium 2.1 Cyperaceae 0.3 Erica spp. 0.1 Pinus spp. 2.5 Plantago spp. 9.9 Poaceae 22.2 Quercus spp. 1.6 Urticaceae 2.9

20.8 11.9 8.8 2.3 – 1.4 61.1 60.0 1.0 0.2 0.1 2.3 1.0 8.6 1.0 1.2

10.7 6.5 4.1 2.8 1.6 1.3 2.4 – 2.4 0.4 0.0 13.7 51.0 11.0 0.2 1.6

23.0 16.6 6.1 2.5 1.0 – 28.5 27.9 0.6 0.1 0.1 1.2 19.4 22.4 0.3 1.3

19.5 5.7 13.8 1.2 – 0.8 6.0 3.5 2.5 0.5 0.4 6.0 15.6 40.5 1.3 0.4

tr tr tr – tr tr tr – tr tr – – tr tr – tr – – tr 5.0 –

tr tr tr tr 0.6 tr – – – – – – – – tr tr – – tr – –

0.7 1.8 1.2 0.8 1.6 tr 0.8 – 0.5 tr tr tr tr tr – – – – – – –

Absent in at least one sample Umbelifera 1.4 tr Juniperus spp. – tr Olea spp. 2.1 tr Lamiaceae 0.4 tr Pistacia spp. 0.6 tr Alnus spp. 0.5 tr Fabaceae 0.6 – Medicago 0.5 – Ononis – – Cistaceae tr tr Rumex spp. tr tr Eucalyptus spp. 0.9 tr Rosaceae tr tr Betulaceae tr tr Typha spp. – tr Geraniaceae – – Solanaceae tr tr Fraxinus spp. tr – Acacia spp. – – Thalictrum – – Fedia spp. – tr

Total pollen counts for each sample (pollen grains) 2221 6814 2765 3138 1956

32.4 25.1 7.3 1.4 – 0.5 4.1 4.1 – 0.5 0.9 5.9 10.5 25.1 0.5 11.0

– – – – – – – – – – – – – – – –

15.4 7.1 8.3 3.5 – 0.3 4.2 4.2 – 0.3 0.3 3.5 21.2 20.8 4.2 9.3

0.5 0.6 2.7 7.7 0.9 – 0.6 4.5 2.7 – – – – 1.6 tr 0.9 – – tr – – – – – – –

219

312

5.4 3.7 1.6 0.2 – – 74.5 73.7 0.8 0.2 0.1 1.6 9.5 4.3 0.7 1.4

12.2 3.7 8.5 2.4 – – 3.7 3.7 – 1.2 2.4 19.5 13.4 11.0 6.1 14.6

tr tr 0.9 tr – – tr – – – tr – – tr tr tr tr – – – –

– 2.4 3.7 – – 1.2 2.4 2.4 – 2.4 – – – – – – 1.2 – – – –

1639

82

–: pollen type absent from sample; tr: present pollen types with frequencies lower than 0.5%; Chen-Amar: Chenopodiaceae–Amaranthaceae.

than 10: i) samples AG4 and AG20; ii) samples AG11 and AG15; iii) samples AG1, AG12 and AG8; iv) sample AG22; and v) sample AG14. Samples AG22 and AG14 were clearly distinguishable between them and from all other samples, presenting high linkage distance between each other and other groups. Some distinctive features of AG14 were the lowest magnetic susceptibility values and the highest

Fig. 5. Hierarchical cluster dendrogram combining the results of colour, particle size distribution, magnetic susceptibility and pollen analysis observed in the 9 soil samples collected in the South of Portugal.

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particle size descriptive parameters. On the other hand, sample AG22 presented the lowest values of particle size descriptive parameters and very poor pollen content. In the cluster of AG1, AG12 and AG8, the latter is distinguished from the other two mainly due to having quite a different pollen fingerprint and higher particle size descriptive parameters. Samples AG1 and AG12 present similar colour and particle size distribution values, however magnetic susceptibility values were quite different and pollen fingerprint, although with some similarities, presented some differences, mainly in the representativeness of pollen from Chenopodiaceae–Amaranthaceae and Poaceae. The cluster formed by samples AG11 and AG15 (geographically close) can result from the presence of similar colour, particle size distribution, magnetic susceptibility values. However, they present differences in pollen fingerprint such as the representativeness of pollen from Liguliflora vs Tubuliflora; Chenopodiaceae–Amaranthaceae or Poaceae and the considerable presence in the sample AG15 of Juniperus spp., Pistacia spp. and Cistaceae pollen. Finally, samples AG4 and AG20 were the ones presenting the closest values for the evaluated characteristics, having a linkage distance lower than 5. They present as distinct feature magnetic susceptibility and colour parameters a* and b* values. 4. Conclusions The study reveals the utility of geobotanical techniques for discrimination of soils. In the case of the nine samples analysed in our study, even though geographically very close and having some similarities, each one presented a combination of colour, particle size distribution, low-field magnetic susceptibility and pollen features that still enable its discrimination. Thus, the use of a multidisciplinary approach for the determination of a fingerprint for each site, expected to reveal a specific location, is important in forensic investigation for future selection of coastal search areas and comparison of questioned samples with one or more samples of known origin referenced in a database of the Algarve region. This information can be very useful as investigative intelligence associating a person to a crime scene, excluding the provenance of a questionable sample when its origin is unknown. Acknowledgements This work has been financially supported by the project PTDC/CTEGEX/67442/2006 and by the Pos-Doc scholarship SFRH/BPD/43604/ 2008-POPH-QREN (FCT-Portugal). The authors would like to thank Paulo Alves from CIBIO for his valuable help for the plant's inventory and to the three anonymous reviewers for their help in improving the earlier draft of this paper. References [1] R.M. Morgan, P.A. Bull, The philosophy, nature and practice of forensic sediment analysis, Prog. Phys. Geog. 31 (1) (2007) 43–58.

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