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**Vignettes in Patient Safety - Volume 4** http://dx.doi.org/10.5772/intechopen.75373

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**4. Dietary Advices for a Nutritionally Adequate and Balanced Diet in Children with Celiac Disease**

#### *4.1. Education and Compliance*

The first step towards a balanced diet starts from early education on CD and GFD, possibly provided by a skilled dietitian and/or by a physician with expert knowledge in CD. The diet is complicated and can be overwhelming if not presented using a thorough and proactive approach. Early education is fundamental to promote adherence to GFD. In fact, studies focusing on compliance to GFD indicate that adherence is compromised by a number of factors, including a lack of education and continued support by a physician and dietitian [33,34]. In a study conducted by Charalampopoulos *et al.* on determinants of the adherence of GFD in children with CD, baseline education was one of the main determinant factors influencing compliance, suggesting the importance for frequent reinforcement and an accurate explanation of dietary recommendations [34].

#### *4.2. Dietary Intake*

In CD children on GFD, the recommended distribution of daily calorie intake for a healthy and balanced diet does not differ from that recommended to the general population. According to the dietary reference intake values (DRI), the total daily dietary calories should be ideally obtained as 55% from complex and simple carbohydrates, 15% from dietary protein and 25%–30% or less from lipids. The intake of unsaturated fat (monounsaturated and polyunsaturated) should be preferred. Monounsaturated and polyunsaturated fatty acids should provide more than 15% and 10% of total calories, respectively. They are found in foods, such as vegetable oils, nuts, seeds and fish high in lipid content, such as salmon, trout and herring. Thus, it is important to inform and recommend children with CD on the ideal distribution of the daily calorie intake.

#### *4.3. Gluten-Free Foods*

It is advisable to prefer consumption of naturally GF foods, since it has been shown that they are more balanced and complete under both the macro- and micro-nutrient point of view. In fact, these foods are considered to have a higher nutritional value in terms of energy provision, lipid composition and vitamin content as opposed to the commercially purified GF products. Within the range of naturally GF foods, it is preferable to consume those rich in iron and folic acid, such as leafy vegetables, legumes, fish and meat. During explanation of naturally GF foods to patients, it is a good approach for healthcare professionals to bear in mind the local food habits and recipes of each country. This may provide tailored dietary advice, improving acceptance and compliance to GFD. Furthermore, increasing awareness on the availability of the local naturally GF foods may help promote their consumption, resulting in a more balanced and economically advantageous diet. Indeed, these aspects should always be addressed during dietary counseling. With regards to the commercially purified GFPs, it is recommended to pay special attention to the labeling and chemical composition. In European countries, the currently accepted definition of GF is the one designed by Codex Alimentarius (the gluten-free certification organization, 2007) [35]. The term "gluten-free" refers only to foods containing less than 20 ppm of gluten. In addition, the claim "very low gluten" is used for foods, such as bread, produced using cereals that have been specially processed to remove most of the gluten and containing less than 30 mg daily. Furthermore, some GFPs are enriched/fortified with vitamins and/or minerals, thus the choice of these products is preferred, to prevent the deficiencies associated with GFD, mentioned above. Clear labeling of GFPs and education of CD patients on how to interpret them is fundamental to helping CD subjects make safer and more informed food choices.

#### *4.4. Pseudo-Cereals and Minor-Cereals*

Pseudo-cereals, such as amaranth, quinoa and buckwheat, and other minor cereals represent a healthy alternative to frequently used ingredients in gluten-free products. They are a good source of carbohydrates, protein, dietary fiber, vitamins and polyunsaturated fatty acids [36]. In fact, the fiber content in these grains ranges from seven to 10 g/100 g [37], which is higher compared to those of other plant foods and cereals and approximately the same as the content in wheat (fiber 9.5 g/100 g). Furthermore, they are a valid source of protein, as their content is superior to that of wheat in terms of the quantity and quality of proteins: in particular, lysine, arginine, histidine, methionine and cysteine can be found in high amounts [38,39]. Although the lipid content of pseudo-cereals is higher compared to other plant foods, they are characterized by a higher content of unsaturated fatty acids,


**8. Contribution and Conclusions**

Demand elasticities are helpful tools for tourist industry planning. Because there are significant fluctuations in the exchange rate, it is useful to understand the impact of this variable on tourist demand. This study was based on two countries that are important for the Norwegian tourist industry, namely Sweden and Germany. How income and currency changes affect camping tourism from Sweden and Germany to Norway has never been studied before.

Using available data, we calculated the currency and revenue elasticity of the inflows of camping tourists from these two countries. The analysis revealed that income had an impact on demand, but the effect was small. This was consistent with previous studies that reported that the demand for campsites was quite inelastic. This research suggested that a weaker Norwegian exchange rate stimulated demand for Norwegian campsites, but with a currency elasticity below 1.0. Furthermore, the result showed that a stronger Swedish currency relative to the euro had a positive influence on overnight stays at Norwegian campsites. The explanation was presumably that it led to more Swedes holidaying abroad and that there was complementarity between neighboring countries and Sweden.

Little research has been performed on camping tourism in Norway. Thus, little is known about what influences this type of tourism. When there is limited knowledge, there is a greater risk that the wrong investment decisions will be made. This may lead to the waste of the society's resources. Our contribution expands the knowledge of what influences camping tourism and provides decision-makers with a better decision basis.

**Author Contributions:** All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.


# **Characterization of Antibody Responses to Virus Infections in Humans**

Edited by Philipp A. Ilinykh and Kai Huang

Printed Edition of the Special Issue Published in *Pathogens*

www.mdpi.com/journal/pathogens


**Abstract**

Arteriovenous malformations (AVM) are vascular malformations composed of a network of abnormal vessels connecting directly between the artery and vein without an intervening capillary bed. Cerebral arteriovenous malformations have an incidence of 0.8–1.3 per 100.000 person years. Clinical symptoms include intracranial hemorrhage, seizure, headache, and focal neurological deficit. Annual mortality rate associated with hemorrhage is 1–5%, and 10–30% of survivors will have disability. Treatment options for cerebral AVMs include open surgery, embolization, and radiosurgery. Depending on the grade and angioarchitectural characteristics, a combination of these modalities can be used. Endovascular treatment can be performed for size and grade reduction, presurgical devascularization, size reduction before radiosurgery, targeted embolization, and as stand-alone treatment for cure. Targeted embolization can address intranidal or flow-related aneurysms and high flow arteriovenous shunts. Complications of the endovascular treatment include hemorrhage related to vessel perforation or normal pressure breakthrough phenomenon, ischemia, microcatheter retention, and other general complications associated with angiographic procedures. Mortality associated with endovascular treatment is less than 2% and permanent neurological deficit can be seen up to 2–8.9% of cases. New endovascular techniques include balloon-assisted embolization, transvenous embolization, and double microcatheter techniques like pressure cooker technique.

**Keywords:** cerebral arteriovenous malformation, intracranial hemorrhage, endovascular, embolization


**3. Results**

#### *Climatology of Annual TLH, TST, Their Symmetric and Antisymmetric Parts*

Figure 2 shows the climatology of the annual means of TLH and TST and their symmetric and antisymmetric parts, respectively. The annual mean TLH in Figure 2a shows a zonal band of TLH maxima located between 20◦S and 20◦N over the whole-tropics, with the maxima value reaching over 150 W m<sup>−</sup>2, corresponding to the ITCZ and SPCZ over the Pacific and the ITCZ over the equatorial Atlantic and the monsoonal rainfall over the vast region from Africa to Asia and the Western Pacific, and the rainfall maxima over the Amazon Basin. The symmetric component of the TLH (Figure 2b) exhibits three centers: the largest is located at the Equatorial Indian Ocean to the Western Pacific Ocean, which corresponds well to the largest warm pool of SST (Figure 2); the second is located over the northern part of South America, which corresponds to a relatively low (land) surface temperature, but is also nearby warm Equatorial Atlantic SST at its east flank; the third and smallest center is located over Equatorial Africa, which again corresponds to a relatively low land surface temperature. The antisymmetric component of the TLH (Figure 2c) reveals that: (1) The positive atmospheric latent heating regions in the Northern Hemisphere are associated with the ITCZ over the Pacific and Atlantic and monsoonal precipitation over Asia and maritime continent; (2) The positive atmospheric latent heating regions in the Southern Hemisphere are associated with the SPCZ over the Pacific and ITCZ over the Indian Ocean, and also the two broad northwest–southeast-oriented regions over Southern Africa to the Western Indian Ocean and over South America to West Atlantic. By comparing

the corresponding annual-mean TST pattern (Figure 2d) and its symmetric (Figure 2e) and antisymmetric (Figure 2f) components with the TLH pattern, we find that the high TLH (rainfall) zones over the sea surface correspond to high SST, but the high TLH zones over the land surface are associated with relatively lower LST. Indeed, the high LST regions are usually desert areas with little rainfall, but the low LST regions are usually associated with tropical forests. This asymmetry in the TLH-TST relation between the land and sea surfaces may have important implications for tropical atmospheric dynamics.

**Figure 2.** The left panel: (**a**) climatology of the annual-mean TRMM-based tropical latent heating (TLH), and its (**b**) symmetric and (**c**) antisymmetric parts. Unit: W/m2. The right panel: (**d**) climatology of the annual-mean tropical surface temperature (TST) and its (**e**) symmetric and (**f**) antisymmetric parts. Units: ◦C.

To further investigate the relationship between TST and TLH, SVD analysis is employed for the original seasonal TST anomaly and the original seasonal TLH anomaly. Note the anomalies in both fields mean that the mean seasonal cycles of TLH and TST have been removed. It can be observed in Figure 3a,b that the leading SVD mode of the TST and TLH is related to El Niño–Southern Oscillation (ENSO), with anomalously warmer SST over the Equatorial Central and Eastern Pacific, cooler SST over the Western Pacific, and also anomalously warmer SST over the Indian Ocean. Correspondingly, anomalously larger TLH is associated with more precipitation over the Central Pacific (near 180◦E) and the Equatorial Indian Ocean; also, equatorially shifted ITCZ and SPCZ are found over the Pacific, while anomalously smaller TLH dominates over the Western Pacific (Figure 3b).

**Figure 3.** The left (**a**) and right (**b**) spatial pattern of the first mode of SVD using TST and TRMMbased TLH. The squared covariance fraction of the first mode, expressed as a percent, is printed on the upper right-hand corner of each map. (**c**,**d**) and (**e**,**f**): same as (**a**,**b**), but using symmetric and antisymmetric components of TST and TLH, respectively. (**g**,**h**): The time series of the expansion coefficient of the three left patterns and three right patterns, respectively, the blue dotted line is the seasonal-averaged Nino 3.4 index.

To compare the difference between the symmetric and antisymmetric TSTs' connection with the original TLH, SVD analyses are also applied to the symmetric and antisymmetric components of TST paired with the original TRMM-based TLH as shown in Figure 3c,d for the first SVD mode of symmetric TST and original TLH, and Figure 3e,f for the first SVD mode of antisymmetric TST and original TLH. We surprisingly find that the first SVD modes of original TLH corresponding to the first SVD modes of symmetric and antisymmetric components of TST show very high similarities (Figure 3d,f), which resemble the pattern in Figure 3b. This seems at odds with the classic Mastuno–Gill theory, which implies a one-toone correspondence between the (anti) symmetric forcing and (anti)symmetric atmospheric response. To verify the above results, we check the time series of the expansion coefficient of the three left (TST) patterns (Figure 3g) and three right (TLH) patterns (Figure 3h). The high correlation coefficients between each other indicate that the leading symmetric and antisymmetric SVD modes co-vary temporally (Table 1) and are highly consistent with the evolution of El Niño events in the tropical Pacific (Table 2).

**Table 1.** The correlation coefficients between the time series of the expansion coefficient of three left and right SVD patterns.

**Table 2.** The correlation coefficients between the time series of the expansion coefficient of three left and right SVD patterns and the Oceanic Niño Index.

To further prove the results obtained from the SVD analysis, we independently employed the EOF analysis for the original seasonal TST anomaly and its symmetric and antisymmetric components (Figure 4). We find the first EOF (EOF1) mode of the seasonal TST anomaly resembles the first left (TST) SVD model in Figure 3a, both indicating the pattern of El Niño events. Furthermore, the EOF1 mode of the symmetric TST in Figure 4b shows a very high similarity with the EOF1 of the original TST (Figure 4a), with the pattern correlation being 0.99, with a significance level less than 0.01. However, the EOF1 mode of the antisymmetric TST (Figure 4c and Table 3) indicates there is also a weaker but non-negligible antisymmetric TST component across the whole-tropics. In addition, the correlation between the corresponding principle components (PCs) of the EOF1 of symmetric and antisymmetric TSTs is 0.82 (Figure 4c and Table 3), with a significance level of less than 0.01. In short, it can be concluded that while the principal EOF mode of TST interannual variability is dominated by its equatorially symmetric component, there is a non-negligible equatorially antisymmetric component that well co-varies with the symmetric part over most parts of the tropical land and ocean areas. Figure 4d (see also the correlation coefficients in Table 4) shows that the PC1s of original, symmetric, and antisymmetric TST fields are consistent with the evolution of El Niño conditions in the tropical Pacific.

Then we further reveal the link between the symmetric or antisymmetric components of TST and the TRMM-based TLH by calculating the correlation (Figure 5a,e) and regression (Figure 5b,f) of the TLH with/onto the corresponding PCs of the EOF1 of symmetric and antisymmetric TSTs. The regression patterns of TLH in Figure 5b,f are also further separated into equatorially symmetric and antisymmetric components in Figure 5c,d (regressed onto PC1 of the symmetric TST) and in Figure 5g,h (regressed onto PC1 of antisymmetric TST), respectively. It is obvious that the correlation or regression patterns of the TLH associated with the PC1 of symmetric TST (the left panel of Figure 5) are very similar to that associated with the PC1 of antisymmetric TST (the right panel of Figure 5). As such, we confirm the results obtained from the SVD analysis are right.

**Table 3.** The correlation coefficients between the principle components of the EOF1 of TST, TST\_SYM, and TST\_ASYM.

**Figure 4.** The first EOF (EOF1) mode of the seasonal-mean: (**a**) original, (**b**) the symmetric part and (**c**) antisymmetric components of TST over the whole-tropics, and (**d**) the corresponding principle components for (**a**–**c**).

**Table 4.** The correlation coefficients between principle components of the EOF1 of TST, TST\_SYM, TST\_ASYM, and the Oceanic Niño Index.

**Figure 5.** (**a**) The correlation and (**b**) the regression of the TRMM-based TLH with/onto the PC1 of equatorially symmetric TST. (**c**) and (**d**) are the symmetric and antisymmetric parts of (**b**), respectively. (**e**–**h**) are the same as (**a**–**d**), but associated with the PC1 of equatorially antisymmetric TST. The areas with a significance level of less than 0.05 are dotted.

To depict the geographic dependence of the symmetric–antisymmetric connection, the local correlations between the symmetric and antisymmetric components of both TST and TLH are calculated in Figure 6a,b. By definition, the high correlation in Figure 6 implies the co-variation between symmetric and antisymmetric components, in which the regions with positive correlation further indicate the dominant regions in the symmetric–antisymmetric connection. Indeed, this corresponds to the condition of equatorially asymmetric variation, i.e., temporal variation is strong in one hemisphere only while very weak in the other hemisphere. On the other hand, low correlations mean that one of the equatorially symmetric and antisymmetric components is dominant, and the symmetric and antisymmetric components vary independently (i.e., being mathematically orthogonal with each other). The low correlation region for TST mainly extends from the Equatorial West Pacific to the Middle Equatorial Pacific (Figure 6a), consistent with the equatorially symmetric SST variability over this region, while the high positive correlations (Figure 6a) in the tropics are located over the Southeastern Pacific, Northern Equatorial Atlantic, North Africa, Australia, South Asia, Southern South America, clearly being consistent with asymmetric TST variability associated with the equatorially asymmetric land–sea distribution. On the other hand, the low correlation regions for TLH (Figure 6b) are mainly limited in the narrow regions of equatorial Africa, the Indian Ocean, western to middle equatorial Pacific, and equatorial South America, but the regions with a high positive correlation of TLH are consistent with the monsoonal rainfall over Africa, Asia, and North America, and also the ITCZs over the Pacific and Atlantic in the Northern Hemisphere, with the SPCZ over the Pacific, ITCZ over the Indian Ocean, and rainfall region over Southern Africa and Southern America in the Southern Hemisphere.

**Figure 6.** The local correlation between equatorially symmetric and antisymmetric components of (**a**) TST and (**b**) TRMM-based TLH. The areas with a significance level of less than 0.05 are dotted.

Figure 6 indicates that the symmetric and antisymmetric components of both TST and TLH co-vary over vast regions of the tropics, and hence the corresponding Matsuno–Gill modes are intrinsically coupled with each other in the tropical ocean-atmosphere–land system. Untangling their relationship through model simulations and mechanistic analysis is needed for a better theoretical understanding of tropical dynamics.

#### **4. Summary and Discussion**

By utilizing seasonally averaged satellite-based TRMM precipitation data as a proxy of tropical latent heating (TLH) and ERA5-based tropical surface temperature (TST) data from 1998 to 2018, we investigate the cross-hemispheric connection in the TLH and TST variability and their co-variability. The interannual variability of both the TST and the TLH is equatorially asymmetric and can be decomposed as the sum of equatorially symmetric and antisymmetric components. Based on the decomposition, we reveal some new features of variability and co-variability of the TST and TLH. The main results are summarized as follows:

and antisymmetric PCs of TST are both nearly coincident with the ENSO index during the 21 years of 1998–2018.

While these results are obtained for all four seasons with the mean seasonal cycle being removed, we note they basically hold individually for each season. Results on individual season-based analyses will be reported later in more detail.

The above results raise some interesting puzzles in the theoretical understanding of tropical atmospheric dynamics. First is that if they are really at odds with the classic Matsuno–Gill theory [1,2] or not. We might expect from the linear Matsuno–Gill theory a one-to-one correspondence between equatorially (anti) symmetric TST forcing and equatorially (anti) symmetric TLH pattern, but due to the intrinsic nonlinearity in tropical dynamics, say, related to convective precipitation, the one-to-one correspondence may at least partially be broken up. Because of the strong co-variability in the observed TST-TLH relation, we may not obtain mechanistic understanding directly from statistical analysis of the observations. Well-designed modeling experiments and theoretical analysis are needed for a decisive solution to the puzzle.

The second puzzle is to what extent are the equatorially symmetric and antisymmetric components of the joint TST-TLH variability interactive with each other? What is the underlying mechanism responsible for the interaction? Indeed, it is quite reasonable to assume that the Matsuno–Gill theory still holds to some degree for the interannual joint TST-TLH variability, but the departure from the theory due to nonlinear interactions may be essential to better understand and predict the tropical variability.

While the PCs of joint TST-TLH variability are clearly associated with the ENSO cycle in the tropical Pacific, the co-variability over the Indian Ocean, tropical Atlantic, and tropical land areas should not be neglected from our analysis. Recent studies have suggested the importance of pantropical interaction or cross-basin interaction in tropical dynamics [6,7]. We further suggest that a whole-tropics perspective that takes the different but connected nature of equatorially symmetric and antisymmetric modes across the wholetropics into consideration may well be useful in understanding and predicting tropical climate variability.

#### **5. Historical Note**

After graduating from the University of Chicago with his famous thesis on energy dispersion in the atmosphere, T.C. Yeh stayed there and became a member of the team on tropical dynamics, second to H. Riehl, the head of the team. The other two members were J. Malkus (J. Simpson) and N. LaSeur. Before his return to China, he published two classic papers on the intensity of the Hadley cell [23] and on trade inversion [24], among others. Although later on he did not focus on tropical dynamics in his lifelong career, his works on tropical dynamics also clearly reflect his style and character in research, i.e., being thorough, systematic, and insightful. This short note is devoted to Prof. Yeh's contribution to the field of tropical dynamics—J. Lu.

**Author Contributions:** Conceptualization, J.L.; methodology, J.L.; validation, Y.G., X.L. and J.L.; formal analysis, Y.G. and X.L.; resources, J.L.; data curation, Y.G.; writing—original draft preparation, J.L. and X.L.; writing—review and editing, J.L.; visualization, Y.G.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China, grant number 42175070.

**Data Availability Statement:** All of data are available by acquiring from the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.


**7. Conclusion**

It is easy to understand the moral obligation of helping a man who is bleeding or a woman in labor on the street. The moral obligation is no different when we do not see them directly. Most of the major moral theories, if used to solve the question of providing healthcare coverage to refugees and internationally displaced population, allow us to conclude that in order to be on the right side of morality, it is essential that we do everything we can to provide basic healthcare coverage to all people.  In the long run, providing healthcare services to all people residing in a country will ensure the welfare of the whole population: citizens, temporary residents, and asylum seekers.


**1. Introduction**

Nonprofit organisations (NPOs) co-create services and social value through public participation in resource integrating activities such as donations, advocacy, social support, and recognition. The need for public participation has prompted the adoption of social networking platforms (SNPs) to allow supporters, donors, and volunteers, who act as "free agents", to work jointly with organisations [1]. SNPs, often called social media, "employ mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss and modify user-generated content" ([2], p. 241). The power of SNPs for organisations remains in the opportunities for public participation,

involvement, and engagement in organisational activities. For NPOs, Twitter enables strategic engagement of stakeholders through dialogic and communitybuilding practices better than company websites [3]. Broadly, SNPs are recognised for organisational visibility, information sharing, community, relationship building, and taking action [3, 4]. Beyond such basic capabilities, SNPs are increasing getting recognised for their interactive resources that enable collaboration [5], co-creation [6], and innovation [7].

The many opportunities afforded by SNPs are not just an end in themselves but mechanisms for the co-creation of nonprofit services [8]. Co-creation involves interactions between the community and organisations in creative activities [9]. SNPs have become instrumental in public *participation*, *involvement*, and *engagement* in nonprofit services [10]; moreover, the three activities are critical pillars of service co-creation [11]. Similarly, collaborative networked organisations and communities enable co-innovation and value co-creation [9]; but such outcomes are driven by the platform capabilities and the relationships between actors [12, 13]. Collaborative networked organisations often seek to build productive and sustainable relationships with a target community, and such relationships are here coined as organisation-community relationships (OCRs). OCRs are seemingly productive at the start, especially when NPOs pursue advocacy-based goals [14]; but, the relationships slowly collapse and become unproductive in due course [10, 15]. This trend can be attributed to the limited use of SNP interactive capabilities, which reduces the organisation's network activity [16, 17]. Particularly, the use of SNPs as co-creative platforms calls for a thorough understanding of the properties, structure, or types of OCR [13].

Most NPOs have a social media page, but such presence does not in itself advance OCR, create awareness of the organisation's activities, or trigger an influx of community participation [18]. Similarly, information sharing is a common practice but does not directly facilitate community engagement [17] and service co-creative activities [8]. Conversely, research, planning, and focused implementation of SNPs could pave the way for productive OCR [18]. NPOs could succeed in SNP implementation if two considerations are met [19], i.e., understand how to build online relationships and establish an ongoing positive and valuable bond with their audience. Increasing online network activity (involvement) is the first step to generating significant returns for NPOs and building long-term support from their community (ibid). Undeniably, "cultivating supporter relationships" is crucial and is the next best step to enabling collaboration and building affinity in stakeholder communities [20]. However, the dynamics of surrounding these relationships are often lost in translation, for instance the nature of such relationships, influencing factors, and associated outcomes. Responding studies, e.g., [21–25] have articulated relationship types and characteristics but the factors for productive OCR are yet to be explained. Responding to the issues and research gap mentioned above, this chapter seeks to address the question that what factors influence the organisationcommunity relationships in CoSNPs and how can such factors be classified?

This chapter discusses the co-creative potential of SNPs coined as co-creative social networking platforms (CoSNPs), the cultivation of OCR on these platforms, and the influencing factors. We discuss the potential of such platforms to drive productive OCR in the co-creation of nonprofit services and draw on the ecological systems perspective [26] to identify the influencing factors. The ecological systems perspective draws our attention to how organisations operate with their environment, internal and external [27]. Based on this perspective, the chapter illustrates multi-level influences shaping OCR in CoSNPs. The rest of the chapter is divided into six sections. In Section 2, we provide related work on the characteristics of CoSNPs and OCR and articulate the emergence, growth, and collapse of OCR in

*Building Organisation-Community Relationships in Co-Creative Social Networking Platforms… DOI: http://dx.doi.org/10.5772/intechopen.99732*

CoSNPs. Section 3 introduces the ecological systems perspective into the organisational context to classify multi-level influences of OCR in CoSNPs. In Section 4, we deduce four theoretical propositions and present a model of multi-level ecological influences of OCR in CoSNPs. In Section 5, we discuss our observations and provide implications for practice. Lastly, in Section 6, we conclude our discussion and provide some directions for future research.


8. Gene expression during development

Different cells in an embryo express different sets of genes at different times. A common set of genes is present in all cells at all times and is called as the "House-keeping genes." These genes carry out the normal functions of a cell. Speciality genes are the special genes that enable a cell to carry out special functions. The genes responsible for embryonic body plan Homeobox genes, proto-oncogenes and PAX genes.

The gene expression is regulated by three sets of proteins called as the promoter, silencer and enhancer proteins. These proteins can modify the gene expression and define a particular cell type. There are also signaling proteins that facilitate the development of adjacent areas in a particular organ.

The SRY gene on the Y chromosome produces testis determining factor, which in turn activate the SOX9, and steroidogenesis factor (SF1) and it stimulates development of Sertoli and Leydig cells. WNT4 is the master gene for ovarian development. It inhibits SOX9 and up regulates DAX1. WNT4 along with other genes promotes formation of cortical cords and causes regression of medullary cords and prevents the tunica albuginea to develop [2].

ectodermal ridge (AER), which is a localized area of thickening on the surface ectoderm covering the limb bud, produces growth signals such as FGF4 and FGF8, which result in growth in proximal-distal axis. A collection of cells at the posterior margin of limb bud near body wall also undergoes proliferation to form the zone of polarizing activity which produce morphogen retinoic acid which in turn expresses SHH (Sonic Hedge Hog) which plays a huge role in determining the anteroposterior axis for limb development (Figure 9). Bones of the limb are derived by endochondral ossification of the parietal layer of the lateral plate mesoderm which is determined by HOX genes (HOXA and HOXD), which regulate the type, and shape of the bones. Digits are formed due to organized apoptosis of the AER into separate ridges and ultimately by interdigital apoptosis. HOXD13 mutations result in synpolydactyly, which is a dominantly inherited condition. Hand foot genital syndromes results from mutations of HOXA13 and presents hypospadias in males and bilateral thumb and great toe hypoplasia [6]. Dorsalization of the limb bud is under the control of genes like radical fringe and Wnt7a. Holt Oram syndrome an autosomal dominant inheritance is due to mutation of T-box 5 gene and is characterized by forelimb abnormalities and cardiac defects thus T-box genes determine the limb identity by activating FGFs. Another gene is the PitX, which contribute in forming hind limb characteristics [6].

Figure 9. Molecular regulation of limb development. Fibroblast growth factor 10 and AER initiate limb growth. These are induced by bone morphogenetic protein S (RA is retinoic acid, ZPA is zone of polarizing activity, AER is apical endoder-

Developmental Genetics and Preimplantation Genetic Diagnosis

http://dx.doi.org/10.5772/intechopen.77965

85

The most important feature in development of head and neck is the formation of pharyngeal arches resembling the gills of the fish. These arches are bars of mesenchyme tissue appearing at fourth or fifth week of development with an outer covering of ectoderm and inner layer of

10. Head and neck formation

mal ridge).

The extracellular growth factors regulate cell division and differentiation by the process of signal transduction and thus mutations in genes, which produce these factors and receptors, have been implicated in cancer development and also in developmental anomalies. The Fibroblast growth factor receptors (FGR) play an important role in embryogenesis. Mutations in FGR have been associated with two groups of disorders of skeletal system, which broadly include the craniosynostosis syndromes and achondroplasias. Genes such as Fibrillin-I and Elastin code for proteins that are needed for arranging microfibrils in the matrix and thus the mutations are associated with Marfans' syndrome [3].


**3.2 Vitamin D3 at different doses reverses anxiety-like profile of the middle-aged and old OVX and OVX rats given with 17β-estradiol after long-term absence of estrogen as measured in light: dark box**

Two-way ANOVA statistical test revealed a significant interaction between hormone condition and treatments ([F(5,44) = 11.52, *p* < 0.01) and [F(5,44) = 16.75, *p* < 0.05], respectively), with significant effects of hormone conditions ([F(5,44) = 19.22, *p* < 0.05 and [F(5,44) = 9.56, *p* < 0.001], respectively) and treatment ([F(5,44) = 9.88, *p* < 0.01] and [F(5,32) = 7.26, *p* < 0.05], respectively) in the time spent and number of entries in the light compartment of the middle-aged OVX rats. The *post-hoc* test demonstrated significant differences for these groups (*p* < 0.05).

Vitamin D3 supplementation at all doses did not alter the time spent and number of entries in the light compartment in the intact middle-aged rats as compared to the control rats (**Figure 3a, b**, *p* > 0.05). Following 12 weeks of post-ovariectomy period, the middle-aged female rats showed a profound decrease of the time spent and number of entries in the light compartment as compared to the control females (**Figure 3a, b**, *p* < 0.05). The 17β-E2 injection (0.5 μg/kg, s.c.) resulted in an increase of the time spent and number of entries in the light compartment in the middleaged OVX rats as compared to the middle-aged OVX rats administered with solvent (**Figure 3a, b**, *p* < 0.05), but did not reach the values of control rats (**Figure 3a, b**, *p* < 0.05).

Vitamin D3 treatment at dose of 5.0 mg/kg markedly elevated time spent and number of entries in the light compartment of the middle-aged OVX rats as compared to the middle-aged OVX rats given with solvent (**Figure 3a, b**, *p* > 0.05). Co-administration of vitamin D3 at dose of 5.0 mg/kg and 17β-E2 to the middle-aged OVX rats produced greater increase of time spent and number of entries in the light compartment than that of the middle-aged OVX rats given 17β-E2 or solvent (**Figure 3a, b**, *p* > 0.05). Vitamin D3 treatment at doses of 1.0 mg/kg and 2.5 mg/kg individually or plus 17β-E2 did not change time spent and number of entries in the light compartment of the middle-aged OVX as compared to the middle-aged OVX rats treated with 17β-E2 (**Figure 3a, b**, *p* > 0.05). The time spent and number of entries in the light compartment in the middle-aged OVX rats administered vitamin D3 at these doses in combination with 17β-E2 were lower than that for middle-aged control rats and were higher than for middle-aged OVX rats (**Figure 3a, b**, *p* > 0.05).

Two-way analysis of variance showed a significant hormone condition × treatment interaction ([F(5,44) = 11.52, *p* < 0.01] and [F(5,44) = 12.74, *p* < 0.05], respectively), significant effect of hormone conditions ([F(5,44) = 11.22, *p* < 0.05] and [F(5,44) = 11.56, *p* < 0.001], respectively) and significant effect for treatment ([F(5,44) = 10.08, *p* < 0.01] and [F(5,32) = 12.26, *p* < 0.05)], respectively) for time spent and number of entries in the light compartment of the old OVX rats. *Post-hoc* analyses revealed differences among the groups for the anxiety-like state during experimental sessions (*p* < 0.05).

**43**

**Figure 3.**

(**Figure 4a, b**, *p* > 0.05).

*Behavioral Effects of Vitamin D3 at Estrogen Deficiency in Females of Different Age*

The old intact rats treated with vitamin D3 at doses of 1.0, 2.5 or 5.0 mg/kg failed to modify time spent and number of entries in the light compartment as compared to the old control rats (**Figure 4a, b**, *p* > 0.05). The old OVX rats given with solvent displayed a significant decrease of time spent and number of entries in the light compartment as compared to the old control rats (**Figure 4a, b**, *p* > 0.05). Vitamin D3 supplementation administered in all doses to the old OVX rats induced increase of the time spent and number of entries in the light compartment as compared to the old OVX rats treated with solvent (**Figure 4a, b**, *p* > 0.05). Combined administration of cholecalciferol at dose of 1.0 mg/kg with 17β-E2 to the old OVX rats more significantly increased time spent and number of entries in the light compartment as compared to the old OVX rats treated with solvent or 17β-E2 (**Figure 4a, b**, *p* > 0.05). The old OVX rats administered with vitamin D3 at doses of 2.5 mg/kg and 5.0 mg/kg plus 17β-E2 showed identical parameters of time spent and number of entries in the light compartment like as old OVX rats treated with 17β-E2

*administered dose of 17β-estradiol (17β-E2) was 0.5 μg/rat s.c., once daily, for 14 days.*

*Effects of cholecalciferol administration on anxiety-like behavior of the middle-aged OVX rats following longterm estrogen deficiency in the light/dark test. (a) Time spent in the light box, sec; (b) The number of entrances in the light box. The obtained results show the mean ± standard error of the mean (SEM). \*—p < 0.05 as compared to the control group of sham-operated rats, #—p < 0.05 as compared to the old OVX rats treated with solvent, ##—p < 0.05 as compared to the old OVX rats treated with 17β-estradiol. Each group comprised a minimum of eight rats. Cholecalciferol was given at 1.0, 2.5 or 5.0 mg/kg/day s.c., once daily, for 14 days. The*

**3.3 Vitamin D3 administration changes behavioral reactivity of the middle-aged**

Accordingly to the two-way ANOVA test, there were significant differences for the grooming behavior between hormone conditions ([F(5,44) = 8.12, *p* < 0.05] and [F(5,44) = 9.22, *p* < 0.05], respectively) between drug treatment ([F(5,44) = 12.51,

**and old OVX and OVX rats treated with 17β-estradiol**

*DOI: http://dx.doi.org/10.5772/intechopen.82596*

*Behavioral Effects of Vitamin D3 at Estrogen Deficiency in Females of Different Age DOI: http://dx.doi.org/10.5772/intechopen.82596*

#### **Figure 3.**

*Fads and Facts about Vitamin D*

Vitamin D3 supplementation administered in all doses to the old OVX rats resulted in increase of the time spent into the open arms and the number of entries into the open arms as compared to the old OVX rats treated with solvent (**Figure 2a, b**, *p* > 0.05). Combined administration of cholecalciferol at dose of 1.0 mg/kg with 17β-E2 to the old OVX rats more significantly increased the time spent into the open arms and the number of entries into the open arms as compared to the old OVX rats treated with solvent or 17β-E2 (**Figure 2a, b**, *p* > 0.05). The old OVX rats administered with cholecalciferol at doses of 2.5 mg/kg and 5.0 mg/kg plus 17β-E2 showed similar values of the time spent into the open arms and the number of entries into the open arms like as old OVX rats treated with 17β-E2 (**Figure 2a, b**, *p* > 0.05).

**3.2 Vitamin D3 at different doses reverses anxiety-like profile of the middle-aged and old OVX and OVX rats given with 17β-estradiol after long-term absence**

Two-way ANOVA statistical test revealed a significant interaction between hormone condition and treatments ([F(5,44) = 11.52, *p* < 0.01) and [F(5,44) = 16.75,

([F(5,44) = 19.22, *p* < 0.05 and [F(5,44) = 9.56, *p* < 0.001], respectively) and treatment ([F(5,44) = 9.88, *p* < 0.01] and [F(5,32) = 7.26, *p* < 0.05], respectively) in the time spent and number of entries in the light compartment of the middle-aged OVX rats. The *post-hoc* test demonstrated significant differences for these groups (*p* < 0.05). Vitamin D3 supplementation at all doses did not alter the time spent and number of entries in the light compartment in the intact middle-aged rats as compared to the control rats (**Figure 3a, b**, *p* > 0.05). Following 12 weeks of post-ovariectomy period, the middle-aged female rats showed a profound decrease of the time spent and number of entries in the light compartment as compared to the control females (**Figure 3a, b**, *p* < 0.05). The 17β-E2 injection (0.5 μg/kg, s.c.) resulted in an increase of the time spent and number of entries in the light compartment in the middleaged OVX rats as compared to the middle-aged OVX rats administered with solvent (**Figure 3a, b**, *p* < 0.05), but did not reach the values of control rats (**Figure 3a, b**,

Vitamin D3 treatment at dose of 5.0 mg/kg markedly elevated time spent and number of entries in the light compartment of the middle-aged OVX rats as compared to the middle-aged OVX rats given with solvent (**Figure 3a, b**, *p* > 0.05). Co-administration of vitamin D3 at dose of 5.0 mg/kg and 17β-E2 to the middle-aged OVX rats produced greater increase of time spent and number of entries in the light compartment than that of the middle-aged OVX rats given 17β-E2 or solvent (**Figure 3a, b**, *p* > 0.05). Vitamin D3 treatment at doses of 1.0 mg/kg and 2.5 mg/kg individually or plus 17β-E2 did not change time spent and number of entries in the light compartment of the middle-aged OVX as compared to the middle-aged OVX rats treated with 17β-E2 (**Figure 3a, b**, *p* > 0.05). The time spent and number of entries in the light compartment in the middle-aged OVX rats administered vitamin D3 at these doses in combination with 17β-E2 were lower than that for middle-aged control rats

and were higher than for middle-aged OVX rats (**Figure 3a, b**, *p* > 0.05).

ment interaction ([F(5,44) = 11.52, *p* < 0.01] and [F(5,44) = 12.74, *p* < 0.05], respectively), significant effect of hormone conditions ([F(5,44) = 11.22, *p* < 0.05] and [F(5,44) = 11.56, *p* < 0.001], respectively) and significant effect for treatment ([F(5,44) = 10.08, *p* < 0.01] and [F(5,32) = 12.26, *p* < 0.05)], respectively) for time spent and number of entries in the light compartment of the old OVX rats. *Post-hoc* analyses revealed differences among the groups for the anxiety-like state during

Two-way analysis of variance showed a significant hormone condition × treat-

*p* < 0.05], respectively), with significant effects of hormone conditions

**of estrogen as measured in light: dark box**

**42**

experimental sessions (*p* < 0.05).

*p* < 0.05).

*Effects of cholecalciferol administration on anxiety-like behavior of the middle-aged OVX rats following longterm estrogen deficiency in the light/dark test. (a) Time spent in the light box, sec; (b) The number of entrances in the light box. The obtained results show the mean ± standard error of the mean (SEM). \*—p < 0.05 as compared to the control group of sham-operated rats, #—p < 0.05 as compared to the old OVX rats treated with solvent, ##—p < 0.05 as compared to the old OVX rats treated with 17β-estradiol. Each group comprised a minimum of eight rats. Cholecalciferol was given at 1.0, 2.5 or 5.0 mg/kg/day s.c., once daily, for 14 days. The administered dose of 17β-estradiol (17β-E2) was 0.5 μg/rat s.c., once daily, for 14 days.*

The old intact rats treated with vitamin D3 at doses of 1.0, 2.5 or 5.0 mg/kg failed to modify time spent and number of entries in the light compartment as compared to the old control rats (**Figure 4a, b**, *p* > 0.05). The old OVX rats given with solvent displayed a significant decrease of time spent and number of entries in the light compartment as compared to the old control rats (**Figure 4a, b**, *p* > 0.05).

Vitamin D3 supplementation administered in all doses to the old OVX rats induced increase of the time spent and number of entries in the light compartment as compared to the old OVX rats treated with solvent (**Figure 4a, b**, *p* > 0.05). Combined administration of cholecalciferol at dose of 1.0 mg/kg with 17β-E2 to the old OVX rats more significantly increased time spent and number of entries in the light compartment as compared to the old OVX rats treated with solvent or 17β-E2 (**Figure 4a, b**, *p* > 0.05). The old OVX rats administered with vitamin D3 at doses of 2.5 mg/kg and 5.0 mg/kg plus 17β-E2 showed identical parameters of time spent and number of entries in the light compartment like as old OVX rats treated with 17β-E2 (**Figure 4a, b**, *p* > 0.05).

## **3.3 Vitamin D3 administration changes behavioral reactivity of the middle-aged and old OVX and OVX rats treated with 17β-estradiol**

Accordingly to the two-way ANOVA test, there were significant differences for the grooming behavior between hormone conditions ([F(5,44) = 8.12, *p* < 0.05] and [F(5,44) = 9.22, *p* < 0.05], respectively) between drug treatment ([F(5,44) = 12.51,

#### **Figure 4.**

*Effects of cholecalciferol administration on anxiety-like behavior of the old OVX rats following long-term estrogen deficiency in the light/dark test. (a) Time spent in the light box, sec; (b) The number of entrances in the light box. The obtained results show the mean ± standard error of the mean (SEM). \*—p < 0.05 as compared to the control group of sham-operated rats, #—p < 0.05 as compared to the old OVX rats treated with solvent, ##—p < 0.05 as compared to the old OVX rats treated with 17β-estradiol. Each group comprised a minimum of eight rats. Cholecalciferol was given at 1.0, 2.5 or 5.0 mg/kg/day s.c., once daily, for 14 days. The administered dose of 17β-estradiol (17β-E2) was 0.5 μg/rat s.c., once daily, for 14 days.*

*p* < 0.01] and [F(5,44) = 12.56, *p* < 0.01], respectively) and an interaction between hormone condition and treatments ([F(5,44) = 7.16, *p* < 0.01] and [F(5,44) = 9.26, *p* < 0.01], respectively) in the middle-aged and old OVX rats. Further *post-hoc* test revealed differences for grooming between experimental groups of the OVX rats with different age (*p* < 0.05).

Vitamin D3 injected at several doses failed to demonstrate any changes of behavioral reactivity of the middle-aged and old intact females in the OFT as compared to the middle-aged control rats (**Tables 1** and **2**, *p* > 0.05).

A significant decrease of grooming behavior was registered in the middle-aged and old OVX rats given with solvent as compared to the control (**Tables 1** and **2**, *p* < 0.05). 17β-E2 significantly reduced grooming reactions in the middle-aged and old OVX rats as compared to the middle-aged OVX rats (**Tables 1** and **2**, *p* < 0.05). The middle-aged and old OVX rats treated with vitamin D3 in all tested doses alone or in a combination with 17β-E2 did not demonstrate any modifications of motor and rearing activities as compared to the middle-aged OVX rats given with solvent (**Tables 1** and **2**, *p* < 0.05).

However, the middle-aged and old OVX rats treated with vitamin D3 at doses of 1.0, 2.5 and 5.0 mg/kg demonstrated an increase of grooming behavior as compared to the middle-aged OVX rats. A co-administration of vitamin D3 at these doses with 17β-E2 decreased grooming behavior as compared to both middle-aged, as well as old intact and OVX rats received with solvent or 17β-E2 (**Tables 1** and **2**, *p* < 0.05).

**45**

**Table 1.**

*\**

*#*

*Behavioral Effects of Vitamin D3 at Estrogen Deficiency in Females of Different Age*

**OVX and OVX females treated with 17β-estradiol**

**3.4 Modifications of 25-hydroxyvitamin D3, estradiol and calcium levels in the blood serum following vitamin D3 administration in the middle-aged and old**

The middle-aged intact rats treated with cholecalciferol at doses of 1.0, 2.5 and 5.0 mg/kg increased 25-OH-VD3 levels (**Figure 5**, *p* < 0.05) and failed to alter estradiol levels in the serum blood as compared to the control rats (**Figure 6**, *p* > 0.05). Long-term ovariectomy in the middle-aged female rats resulted in a significant decrease of estradiol and 25-OH-VD3 levels in the blood as compared to the middleaged control females (**Figures 5** and **6**, *p* < 0.05). The 17β-E2 supplementation (0.5 μg/kg, SC) failed to modify 25-OH-VD3 levels in the blood of the middleaged OVX rats as compared to the middle-aged OVX rats administered with solvent (**Figure 5**, *p* > 0.05), and the value of this parameter in the middle-aged OVX/17β-E2 females were lower than that of the value of middle-aged control rats. However, 17β-E2 supplementation significantly increased estradiol levels in the blood of the middle-aged OVX rats as compared to the middle-aged OVX rats given

The middle-aged OVX rats treated with cholecalciferol at all tested doses significantly increased estradiol levels in the serum blood as compared to the middle-aged OVX rats treated with solvent (**Figure 6**, *p* < 0.05). However, the value of estradiol levels in the middle-aged OVX rats treated with cholecalciferol at these doses were lower than that of the value of middle-aged control rats. The middle-aged OVX rats treated with cholecalciferol at doses of 2.5 and 5.0 mg/kg significantly increased 25-OH-VD3 levels in the serum blood as compared to the middle-aged OVX rats

**Groups Crossing Rearing Grooming** Middle-aged control rats + solvent 73.3 ± 4.2 12.1 ± 0.8 3.0 ± 0.2 Middle-aged intact rats + cholecalciferol 1.0 mg/kg 68.0 ± 2.4 12.0 ± .05 3.2 ± 0.2 Middle-aged intact rats + cholecalciferol 2.5 mg/kg 59.3 ± 5.6 10.5 ± 0.8 3.2 ± 0.2 Middle-aged intact rats + cholecalciferol 5.0 mg/kg 69.5 ± 4.2 11.7 ± 0.8 3.5 ± 0.2 Middle-aged OVX rats + solvent (OVX/solvent rats) 62.4 ± 2.3 12.3 ± 0.6 1.2 ± 0.5\* Middle-aged OVX rats +17β-E2 (OVX/17β-E2 rats) 60.3 ± 2.6 13.2 ± 0.3 3.3 ± 0.4# Middle-aged OVX rats + cholecalciferol 1.0 mg/kg 65.2 ± 2.5 12.1 ± 0.6 4.0 ± 0.2# Middle-aged OVX rats + cholecalciferol 2.5 mg/kg 76.9 ± 4.2 13.2 ± 0.8 3.8 ± 0.4# Middle-aged OVX rats + cholecalciferol 5.0 mg/kg 72.3 ± 4.4 11.0 ± 0.5 4.1 ± 0.6#

*The obtained results show the mean ± S.E.M. Each group comprised a minimum of eight rats. Cholecalciferol was given at 1.0, 2.5 or 5.0 mg/kg/day, s.c., once daily, for 14 days. 17β-Estradiol (17β-E2) was given at 0.5 μg/rat, s.c.,*

*Cholecalciferol influences on behavioral parameters of the middle-aged OVX rats following long-term estrogen*

62.1 ± 2.8 12.6 ± 0.6 0.6 ± 0.2\*,#,##

73.2 ± 2.4 10.2 ± 0.4 0.5 ± 0.2\*,#,##

65.4 ± 5.6 12.5 ± 0.8 0.8 ± 0.2\*,#,##

*DOI: http://dx.doi.org/10.5772/intechopen.82596*

with solvent (**Figure 6**, *p* < 0.05).

Middle-aged OVX rats + cholecalciferol 1.0 mg/

Middle-aged OVX rats + cholecalciferol 2.5 mg/

Middle-aged OVX rats + cholecalciferol 5.0 mg/

*p < 0.05 as compared to the control group of the old sham-operated rats.*

*p < 0.05 as compared to the old OVX rats treated with solvent. ##p < 0.05 as compared to the old OVX rats treated with 17β-estradiol.*

kg + 17β-E2

kg + 17β-E2

kg + 17β-E2

*once daily, during 14 days.*

*deficiency in the open field test for 5 min.*


**5.2 Object oriented approaches (Bock et al. 2005; Díaz Varela et al. 2008)**

Bock et al. (2005) have proposed an object-oriented approach for EUNIS habitat mapping using remote sensing data at multiple scales with good results. The approach performs well when applied to high resolution satellite data (Landsat 30m) for the production of habitat maps at regional level with coarse thematic resolution; also it performs extremely well when applied to very high spatial resolution data (Quickbird 0,7m) for the production of local scale maps with fine thematic resolution.

The use of a multi-scale segmentation (implemented in the software package eCognition, Definiens, http://www.definiens.com) allow for the accurate classification of habitat types, which occur at different scales: for example, large-scale woodland habitat can be detected at coarser segmentation levels, while small-scale habitats such as woodland corridors can be detected at finer segmentation levels.

The main advantages of the object-oriented approaches to habitats mapping are: i) the ability to integrate ancillary data into the classification processes, related to shape, texture, context, etc.; ii) the option of developing knowledge-based rules in the classification process. Both questions make especially possible the accurate identification of habitats with similar spectral properties. Some results that show the advantage of these issues are (Bock et al. 2005): i) the effective separation of different grassland types like calcareous and mesotrophic grassland habitats to a high degree of accuracy through the use of geological data; ii) the use of multi-temporal remote sensing data to distinguish among arable lands, manage grasslands and semi-natural habitats.

#### **5.3 The use of binary classifications by decision trees (DT) (Boyd et al. 2006; Foody et al. 2007; Franklin et al. 2002; Franklin et al. 2001)**

Some studies have shown binary classifications as one of the more appropriate methods to identify habitats in the territory. Binary classifications can be implemented by nonparametric Decision Trees (DT) algorithms. Some of these studies have focused on the mapping of one specific thematic class (Boyd et al. 2006; Foody et al. 2007) hypothesizing that non-parametric algorithm would be more suitable to habitats of conservation interest because of the scarce spatial distribution usually associated to them (the size of the training sample will be smaller). Other studies have combined that kind of techniques (binary classifications by DT) in hybrid approaches (Franklin et al. 2001). The hybrid approaches assumes that parametric algorithms like standard maximum likelihood (ML) are the best

Assessing Loss of Biodiversity in Europe

*Terras do Miño*

Through Remote Sensing: The Necessity of New Methodologies 33

Fig. 2. Flowchart of the methodological steps for the model applied to Biosphere Reserve

band was not included in the classification processes.

habitats should be favored by those variables.

Early spring (March 26, 2002), late spring (May 26, 2001) and summer (August 17, 2002) images were selected for this model to account for the seasonal trends in vegetation communities. Images were geometrically registered using ground control points (GCP), first order transformations and nearest neighbor interpolation. The August 2002 image was georeferenced to 1/25,000 digital maps, produced by the National Geographical Institute of Spain and used as a reference for geometrical correction of the other images. Atmospheric correction was based on the dark-object technique proposed by Chavez(Chavez 1996). Correction for effects of ground slope and topographic orientation was computed using the Lambertian cosine method initially proposed by Teillet et al. (Teillet et al. 1982) and later modified by Civco (Civco 1989). To model illumination conditions a Digital Elevation Model (DEM) was generated using contour lines from 1/5000 digital cartography. The thermal

It was hypothesized that derived and ancillary variables would provide critical information for landscape classification and enable the identification of complex habitats. For example, topographic features and vicinity to fluvial corridors have an important influence on the distribution of natural and semi-natural habitats; therefore, the discrimination of this type of

The input dataset for classification processes included satellite derived variables (reflectance, vegetation indices, texture measures and spectral mixture analysis) along with continuous ancillary data. Reflectance bands were included using principal components transform (Mather 2004) in order to reduce the dimensionality of the dataset and optimize the number of training samples. Vegetation indices were: NDVI (Normalized Difference Vegetation Index) (Rouse et al. 1974) and NDII (Normalized Difference Infrared Index; using Landsat TM Band 5) (Hunt and Rock 1989). Texture measures (homogeneity using Band 3 of each Landsat ETM+ image) were calculated using the co-ocurrence matrix as designed by Haralick et al. (Haralick et al. 1973). The co-ocurrence matrix was computed from a window of 3x3 pixels, which was considered an optimum size for measuring neighbor conditions. Linear spectral mixture analysis (SMA) (Mather 2004) generated

option with spectrally different habitats while applies non-parametric algorithms to other complex habitats, in a so-called Integrated Decision Tree Approach (IDTA). The IDTA (Franklin et al. 2001) consist on a process with a simple set of classification decision steps, readily understood and repeatable. The approach allows mixing unsupervised, supervised and stratification decision rules such that requirements for training data were minimized.

The general advantages of this kind of approaches are: i) those linked to the use of nonparametric algorithms (Tso and Mather 2009), for example less restrictions with the size of the training sample; ii) the use of key input variables combined with key algorithms defined following specific characteristics of individual habitats; iii) The use of a type of geospatial input data (nominal, ordinal, interval or ratio data like forest inventory maps, biophysical and derived maps) that are difficult to incorporate into a statistical classifier.

#### **5.4 New model based on the identification of ecological-units and on the selection of habitat-key variables (Advances from Martinez et al., 2010)**

The methodology proposed in this model is summarized in Figure 2. The approach includes two main steps: the adaptation of an international classification scheme and the generation of *ecological unit* maps. The concept of *ecological unit* goes farther than land cover notion: through ecological expert knowledge it is possible identify in the study area ecological units directly related to Annex I habitats (Table5). Each ecological unit is linked to a distinctive set of characteristic habitats through ecological expert knowledge. Consequently, the system allows identifying and assessing the habitats listed in Annex I of Habitats Directive 92/43/EC through the identification of land covers by remote sensing.

Ecologically significant units of analysis were defined (Table 5), based on the *EUNIS* pan-European classification proposed by the EEA and the CORINE Biotopes System of Classification (www.eea.europa.eu, last accessed, May 2011). By using these systems, the approach can be applicable to other European regions and it will produce cross-comparable results.

The generation of *ecological unit* mapping is based on the selection of key input variables (spectral, derived and ancillary variables) as a function of the main characteristics of the target habitats and on the use of a standard maximum likelihood classification (MLC) algorithm (Swain and Davis 1978). The target habitats are those defined in the Annex I of the Habitats Directive for the Atlantic Biogeographical Region. Because of the *not direct* correspondence between spectral classes and habitat types, we propose the combination of ecological expert knowledge to find the relationship between both of them (step 1) along with the selection of suitable input variables (step 2) in order to achieve the best possible classification of habitats.

The study area was the Biosphere Reserve of *Terras do Miño* in the Northwest of Iberian Peninsula (Figure 3).The classification process was undertaken by the use of multi-temporal Landsat ETM+ images and ancillary data.

Input variables were rectified to the Universal Transverse Mercator Projection (UTM 29T) using the European Datum 1950 (ED50) and resampled to 30m grid size. Training samples were taken by fieldwork. They were located with a global positioning system (GPS) differential receiver. Training sites were selected as to be large and representative enough to characterize each target class and provide efficient and unbiased estimators using stratified sampling. At least 50 additional points per ecological unit were surveyed on the field for results assessment. These data were not used in the training process.

option with spectrally different habitats while applies non-parametric algorithms to other complex habitats, in a so-called Integrated Decision Tree Approach (IDTA). The IDTA (Franklin et al. 2001) consist on a process with a simple set of classification decision steps, readily understood and repeatable. The approach allows mixing unsupervised, supervised and stratification decision rules such that requirements for training data were minimized. The general advantages of this kind of approaches are: i) those linked to the use of nonparametric algorithms (Tso and Mather 2009), for example less restrictions with the size of the training sample; ii) the use of key input variables combined with key algorithms defined following specific characteristics of individual habitats; iii) The use of a type of geospatial input data (nominal, ordinal, interval or ratio data like forest inventory maps, biophysical

**5.4 New model based on the identification of ecological-units and on the selection of**

The methodology proposed in this model is summarized in Figure 2. The approach includes two main steps: the adaptation of an international classification scheme and the generation of *ecological unit* maps. The concept of *ecological unit* goes farther than land cover notion: through ecological expert knowledge it is possible identify in the study area ecological units directly related to Annex I habitats (Table5). Each ecological unit is linked to a distinctive set of characteristic habitats through ecological expert knowledge. Consequently, the system allows identifying and assessing the habitats listed in Annex I of Habitats Directive

Ecologically significant units of analysis were defined (Table 5), based on the *EUNIS* pan-European classification proposed by the EEA and the CORINE Biotopes System of Classification (www.eea.europa.eu, last accessed, May 2011). By using these systems, the approach can be applicable to other European regions and it will produce cross-comparable

The generation of *ecological unit* mapping is based on the selection of key input variables (spectral, derived and ancillary variables) as a function of the main characteristics of the target habitats and on the use of a standard maximum likelihood classification (MLC) algorithm (Swain and Davis 1978). The target habitats are those defined in the Annex I of the Habitats Directive for the Atlantic Biogeographical Region. Because of the *not direct* correspondence between spectral classes and habitat types, we propose the combination of ecological expert knowledge to find the relationship between both of them (step 1) along with the selection of suitable input variables (step 2) in order to achieve the best possible

The study area was the Biosphere Reserve of *Terras do Miño* in the Northwest of Iberian Peninsula (Figure 3).The classification process was undertaken by the use of multi-temporal

Input variables were rectified to the Universal Transverse Mercator Projection (UTM 29T) using the European Datum 1950 (ED50) and resampled to 30m grid size. Training samples were taken by fieldwork. They were located with a global positioning system (GPS) differential receiver. Training sites were selected as to be large and representative enough to characterize each target class and provide efficient and unbiased estimators using stratified sampling. At least 50 additional points per ecological unit were surveyed on the field for

and derived maps) that are difficult to incorporate into a statistical classifier.

**habitat-key variables (Advances from Martinez et al., 2010)**

92/43/EC through the identification of land covers by remote sensing.

results assessment. These data were not used in the training process.

results.

classification of habitats.

Landsat ETM+ images and ancillary data.

Fig. 2. Flowchart of the methodological steps for the model applied to Biosphere Reserve *Terras do Miño*

Early spring (March 26, 2002), late spring (May 26, 2001) and summer (August 17, 2002) images were selected for this model to account for the seasonal trends in vegetation communities. Images were geometrically registered using ground control points (GCP), first order transformations and nearest neighbor interpolation. The August 2002 image was georeferenced to 1/25,000 digital maps, produced by the National Geographical Institute of Spain and used as a reference for geometrical correction of the other images. Atmospheric correction was based on the dark-object technique proposed by Chavez(Chavez 1996). Correction for effects of ground slope and topographic orientation was computed using the Lambertian cosine method initially proposed by Teillet et al. (Teillet et al. 1982) and later modified by Civco (Civco 1989). To model illumination conditions a Digital Elevation Model (DEM) was generated using contour lines from 1/5000 digital cartography. The thermal band was not included in the classification processes.

It was hypothesized that derived and ancillary variables would provide critical information for landscape classification and enable the identification of complex habitats. For example, topographic features and vicinity to fluvial corridors have an important influence on the distribution of natural and semi-natural habitats; therefore, the discrimination of this type of habitats should be favored by those variables.

The input dataset for classification processes included satellite derived variables (reflectance, vegetation indices, texture measures and spectral mixture analysis) along with continuous ancillary data. Reflectance bands were included using principal components transform (Mather 2004) in order to reduce the dimensionality of the dataset and optimize the number of training samples. Vegetation indices were: NDVI (Normalized Difference Vegetation Index) (Rouse et al. 1974) and NDII (Normalized Difference Infrared Index; using Landsat TM Band 5) (Hunt and Rock 1989). Texture measures (homogeneity using Band 3 of each Landsat ETM+ image) were calculated using the co-ocurrence matrix as designed by Haralick et al. (Haralick et al. 1973). The co-ocurrence matrix was computed from a window of 3x3 pixels, which was considered an optimum size for measuring neighbor conditions. Linear spectral mixture analysis (SMA) (Mather 2004) generated

Assessing Loss of Biodiversity in Europe

1/5000 river map using raster processing.

(endmember-mz)+ FMo 3x3

MLC1 75,56 0,733 50,126\*

class, [PCs]: Principal Components,

are significantly different.

\*MLC2

Comparison with MLC1

[FMo]: modal filter

although the model was allowed to produce a residual image.

Through Remote Sensing: The Necessity of New Methodologies 35

non\_photosynthetic vegetation (NPV). SMA models the reflectance of each pixel as a linear combination of reflectance of those four components. It was assumed constrains of no negative values and that the four components explained the whole variation of reflectance,

Slope gradient was calculated from the DEM. Proximity to rivers was calculated from the

Results were assessed by cross-tabulation with a sample of pixels included in test plots. Global, user and producer accuracies were evaluated using an error matrix (Congalton and Green 2009). Additionally the Kappa analysis (Congalton and Green 2009) was used to evaluate the accuracy of the results: we used KHAT statistic to measure how well the remotely sensed classification agrees with the reference data, the Z statistic to determine the significance of a matrix error and the Z pairwise comparison to decide if two KHAT values

**(%) KHAT**

82,75 0,811

**CODE VARIABLES GA**

PCs (may+march+august) + NDVI (my,ag) + NDII5 (my,ag) + prox. streams + SLOPE+ Homogeneity- B3 (my, mz, ag)+ WATER

MLC1 PCs (may+march+august) 75,56 0,733

Legend: [GA] Gloabl accuracy (%). [MLC]: Maximun likelihood classification, [\*] without *Bare Land*

**CODE GA (%) KHAT Z PAIR-WISE Z SCORES\*\***

Legend: [MLC]: Maximun likelihood classification [\*] Significant at the 95% confidence level. [\*\*]

The results of this model showed 82.75% global accuracy after the application of a modal filter. The best result provided a Kappa value (Congalton and Green 2009) of 0.811 with a Z value indicating very good agreement between classification results and the reference data. Tables 6 and 7 shows the accuracy assessment for two processes: i) one of them based on the principal components of the three images (MLC1); ii) the second one also includes the group of ancillary and derived variables (MLC2). Some variables like slope, distance to rivers, NDVI, NDII and homogeneity showed its valuable potential (Table 9). The combination of all of them in the best MLC trial produced a significant increase in global accuracy along with an increase on user and producer accuracies for the most part of the classes of habitats (Table 8). MLC2 showed user and producer accuracies above 70% and 80% in the most part

Table 6. Global accuracies for parametric multi-temporal processes (MLC algorithm)

MLC2 82.75 0,811 62,089\* 3,496569\*

Table 7. Kappa analysis for parametric multi-temporal processes (MLC algorithm)

of habitats. Only WH and forests showed producer or user accuracies less than 60%.

endmember spectra, which are defined as the proportion of each pixel covered by a basic spectral class. The included endmembers were water, soil, green vegetation (GV), and

Table 5. Ecological units directly related to CD 92/43/EEC habitats in the proposed model

34 Biodiversity Loss in a Changing Planet

endmember spectra, which are defined as the proportion of each pixel covered by a basic spectral class. The included endmembers were water, soil, green vegetation (GV), and

**ECOLOGICAL UNIT NATURA 2000 Code**

**Standing Water** 3110/3120/3130

**WH** Bogs (Raised and blanket bogs) and Atlantic wet heaths 7130/7110\*/7120

**RF** Alluvial and riparian forests 91D0\*/91E0\*/91F0

**HM** Tall and mid-herb humid meadows 6430,6410

**DF** Deciduous oak forests 9230

**DH** Siliceous rocky habitats and dry heaths 4030/8220

and grasslands 6410/6510

grasslands 6410/6510

Table 5. Ecological units directly related to CD 92/43/EEC habitats in the proposed model

*Natural –Seminatural Landscape*

**Inland no-wooded wetlands**

**Inland wooded wetlands**

*Anthropic Landscape*

**Forest plantations P** *Pine sp.* groves

**BL** Bare land

**Other natural and seminatural forests**

**Rocky habitats and other heaths**

**E** *Eucalyptus sp.* plantations

**TF** Rural system mainly made up of pastures

**TR** Rural system mainly made up of corn and pasture in rotations

**CG** Traditional rural mosaic with fenced fields, dominated by crops

**WG** Traditional rural mosaic with fenced fields, dominated by wet

**B** Buildings for agricultural, forestry and industrial use

**Transformed rural landscape**

**Traditional rural landscape**

**Man-made landscape**

**Ur** Urban areas (villages, towns) **ME** Mining exploitations

**I** Communication infrastructures

**Running Water W** Water courses **(Main Habitat)**

3140/3150/3160 3260/3270

7230, 4020\*

non\_photosynthetic vegetation (NPV). SMA models the reflectance of each pixel as a linear combination of reflectance of those four components. It was assumed constrains of no negative values and that the four components explained the whole variation of reflectance, although the model was allowed to produce a residual image.

Slope gradient was calculated from the DEM. Proximity to rivers was calculated from the 1/5000 river map using raster processing.

Results were assessed by cross-tabulation with a sample of pixels included in test plots. Global, user and producer accuracies were evaluated using an error matrix (Congalton and Green 2009). Additionally the Kappa analysis (Congalton and Green 2009) was used to evaluate the accuracy of the results: we used KHAT statistic to measure how well the remotely sensed classification agrees with the reference data, the Z statistic to determine the significance of a matrix error and the Z pairwise comparison to decide if two KHAT values are significantly different.

Legend: [GA] Gloabl accuracy (%). [MLC]: Maximun likelihood classification, [\*] without *Bare Land* class, [PCs]: Principal Components, [FMo]: modal filter

Table 6. Global accuracies for parametric multi-temporal processes (MLC algorithm)

Legend: [MLC]: Maximun likelihood classification [\*] Significant at the 95% confidence level. [\*\*] Comparison with MLC1

Table 7. Kappa analysis for parametric multi-temporal processes (MLC algorithm)

The results of this model showed 82.75% global accuracy after the application of a modal filter. The best result provided a Kappa value (Congalton and Green 2009) of 0.811 with a Z value indicating very good agreement between classification results and the reference data.

Tables 6 and 7 shows the accuracy assessment for two processes: i) one of them based on the principal components of the three images (MLC1); ii) the second one also includes the group of ancillary and derived variables (MLC2). Some variables like slope, distance to rivers, NDVI, NDII and homogeneity showed its valuable potential (Table 9). The combination of all of them in the best MLC trial produced a significant increase in global accuracy along with an increase on user and producer accuracies for the most part of the classes of habitats (Table 8). MLC2 showed user and producer accuracies above 70% and 80% in the most part of habitats. Only WH and forests showed producer or user accuracies less than 60%.

Assessing Loss of Biodiversity in Europe

in Galicia (NW Spain)

accurate identification of many ecological units.

Through Remote Sensing: The Necessity of New Methodologies 37

The special interest of this approach comes from the use of an international classification system (EUNIS) that will allow cross-comparable spatial and temporal assessments and make the methodology extrapolated to other regions. The definition of ecological units goes farther than the simple *land cover* idea and it allows the definition of a direct relation win AnnexI habitats and consequently with species. Finally the use of a standard maximum likelihood algorithm based on the selection of key input variables makes possible the

Fig. 3. Localization of the Biosphere Reserve *Terras do Miño* and de SCI *Parga-Ladra-Támoga*

The output classification of this model and the CLC map (5th level) were spatially compared with a habitat map of the Site of Community Importance (SCI) Parga-Ladra-Támoga in the Northwest of Iberian Peninsula (which belongs the Biosphere Reserve of *Terras do Miño)*. This map was elaborated by photo interpretation through aerial photography with different scales ranging from 1/20000 until 1/2000 (Ramil et al. 2005). It also based on expertise fieldwork and its minimum mapping unit was 0,5ha. The map was the reference to evaluate

Again, spatial inconsistencies between both sources (CLC and the model applied to Biosfere Reserve *Terras do Miño*) were evaluated using this map by the analysis of coincidences in the

this site as a candidate to belong to *Natura 2000* ecological network.

cells of two different grids (UTM based): 1 and 10 km2.

Table 8. User and producer accuracies for parametric multi-temporal processes (MLC algorithm)

principal components of the three images

Table 9. Improvement in global accuracy for multi-temporal analyses after adding to the classification the layers showed in the table.

The contribution of topographical variables and vegetation indices to the habitat mapping accuracy is appropriate for the analyses; the combination of vegetation indices was relevant in the analyses, with improvements in global accuracy (to a maximum of 2.35% of accuracy increase when both NDVI and NDII were combined in the process) (Table 9). The topographical variables (Slope and MDE) improved also the global accuracy of multitemporal classifications, although to a lesser extent than variables like homogeneity. Texture measures and SMA components did not provide significant improvements in global accuracy in parametric methods. However they showed to be suitable for improving the discrimination of some particular classes like HM, WH or Ur. Therefore, they could be considered as interesting and helpful variables for nonparametric methods in order to get good discrimination of some classes.

WG 60,41 87,00 74,22 95,00 TR 97,19 100,00 96,30 100,00 CG 84,62 74,00 85,85 87,50 HM 43,94 72,5 73,81 77,50 DF 59,74 44,23 76,39 52,88 RF 52,88 55,0 59,84 73,00 WH 53,33 26,67 73,91 56,67 P 97,92 90,38 96,23 98,08 E 94,74 90,0 90,91 100,00 TF 84,26 91,0 91,01 81,00 TI 100,00 91,67 100,00 91,67 W 100,00 100,00 100,00 100,00 ME 100,00 100,00 100,00 83,33 Ur+I+B 83,33 100,00 86,96 100,00 BL 66,67 30,0 ---- ---- DH 82,28 81,25 83,56 76,25

Legend: [MLC]: Maximun likelihood classification

Table 8. User and producer accuracies for parametric multi-temporal processes (MLC algorithm)

**Improvement in**

DEM + Slope 76,05 0,49 0,738 50,792\* 0,256709 Prox. to streams 76,34 0,78 0,742 51,213\* 0,412576 NDVI and NDII\*\* 77,91 2,35 0,758 53,603\* 1,248175 Homogeneity\*\* 77,22 1,66 0,751 52,467\* 0,867394 Endmember Water 75,75 0,19 0,735 50,442\* 0,102807 Legend: [\*] Significant at the 95% confidence level. [\*\*] Three images. [\*\*\*] In relation to the trial with

Table 9. Improvement in global accuracy for multi-temporal analyses after adding to the

The contribution of topographical variables and vegetation indices to the habitat mapping accuracy is appropriate for the analyses; the combination of vegetation indices was relevant in the analyses, with improvements in global accuracy (to a maximum of 2.35% of accuracy increase when both NDVI and NDII were combined in the process) (Table 9). The topographical variables (Slope and MDE) improved also the global accuracy of multitemporal classifications, although to a lesser extent than variables like homogeneity. Texture measures and SMA components did not provide significant improvements in global accuracy in parametric methods. However they showed to be suitable for improving the discrimination of some particular classes like HM, WH or Ur. Therefore, they could be considered as interesting and helpful variables for nonparametric methods in order to get

**multi-temporal\*\*\* KHAT Z**

**Kappa analysis**

**Pair-wise Z scores\*\*\***

**MLC1 MLC2 user producer user producer**

**ECOLOGICAL UNITS**

**Variables Global**

principal components of the three images

good discrimination of some classes.

classification the layers showed in the table.

**accuracy**

The special interest of this approach comes from the use of an international classification system (EUNIS) that will allow cross-comparable spatial and temporal assessments and make the methodology extrapolated to other regions. The definition of ecological units goes farther than the simple *land cover* idea and it allows the definition of a direct relation win AnnexI habitats and consequently with species. Finally the use of a standard maximum likelihood algorithm based on the selection of key input variables makes possible the accurate identification of many ecological units.

Fig. 3. Localization of the Biosphere Reserve *Terras do Miño* and de SCI *Parga-Ladra-Támoga* in Galicia (NW Spain)

The output classification of this model and the CLC map (5th level) were spatially compared with a habitat map of the Site of Community Importance (SCI) Parga-Ladra-Támoga in the Northwest of Iberian Peninsula (which belongs the Biosphere Reserve of *Terras do Miño)*. This map was elaborated by photo interpretation through aerial photography with different scales ranging from 1/20000 until 1/2000 (Ramil et al. 2005). It also based on expertise fieldwork and its minimum mapping unit was 0,5ha. The map was the reference to evaluate this site as a candidate to belong to *Natura 2000* ecological network.

Again, spatial inconsistencies between both sources (CLC and the model applied to Biosfere Reserve *Terras do Miño*) were evaluated using this map by the analysis of coincidences in the cells of two different grids (UTM based): 1 and 10 km2.

Assessing Loss of Biodiversity in Europe

**6. Conclusions**

suitable RS and ancillary data.

be, at least not in a direct way).

data: at spatial and temporal levels.

that it is uncertain to use CLC as a proxy of habitat maps.

definite nor any that has been standardized across Europe.

Through Remote Sensing: The Necessity of New Methodologies 39

Results show that, although both sources (CORINE and the model) are based on LANDSAT images with 30m of spatial resolution, the inclusion of decisive variables in the classification processes along with the identification of ecological units was crucial. And it is again proved

To meet the requirements of European policies such as *Natura 2000* Network and the 2020 EU Biodiversity Strategy the development of more cost and time effective monitoring strategies are mandatory. Remote sensing (RS) techniques contribute significantly to biodiversity monitoring and several approaches have been proposed to get on-going requirement for spatially explicit data on the ecological units, and the value and threats against natural and semi-natural habitats (Bock et al. 2005; Weiers et al. 2004), but no

The major obstacles to get standardized scientific monitoring methodologies for habitat monitoring form a complex patchwork. The immense versatility of RS, the full range of RS techniques and products, has led to numerous potential approaches but all of them are dependent of many factors: i) firstly the large variability in the quality of input variables, their semantic, thematic and geometrical accuracy; many approaches have assumed the suitability and representativity of the selected geospatial data; ii) secondly, the possible variability of the spectral, spatial and temporal resolutions; iii) finally, the availability of

There is no a simple relationship between habitats and biophysical parameters like land covers (Groom et al. 2006). *Habitat classes* are not the same that *land cover classes* and the inconsistencies and gaps when a land cover map, as CORINE Land Cover, is used as a surrogate of a habitat map are significant and it should be evaluated in each case. It is necessary to develop *ad hoc* criteria to get the objective of identifying and monitoring habitats from remote sensing. It should be found the optimal way (cost effective and in an acceptable time, and with an optimal level of accuracy) to get from one unit of land cover (which can definitely be detected directly by remote sensing) to a unit of habitat (which may

At the European Community level the appropriate criteria for getting that relation should be achieved through EUNIS system (Martínez et al. 2010; Moss and Davies 2002) since it is a common denominator that is compatible with the requirements of Annex I of the Habitat Directive. It will support the standardization because it makes possible cross-comparable

In regard to habitat identification through RS recent researches have suggested different relevant considerations and requirements: study areas specific approaches; ecological expert knowledge implemented as decision rules; the implementation/inclusion of key input variables selected following specific characteristics of individual habitats; the integration of ancillary data into the classification processes, related to shape, texture, context; the use of non-parametric algorithms implemented through binary classifications or decision trees that allow to include nominal, derived and ancillary geospatial data and also are advantageous with scarce training samples; (Bock et al. 2005; Boyd et al. 2006; Foody et al. 2007; Franklin et

On the other hand, insufficient integration at different scales is one of the constraints of the current biodiversity monitoring programmes (Pereira and Cooper 2006) and it is also urgent

al. 2001; Kerr and Ostrovsky 2003; Martínez et al. 2010; Mücher et al. 2009).

Table 10. Spatial correlations between CD 92/43/EEC habitats cartography, Corine Land Cover cartography (5th level) and the MODEL *Terras do Miño* in the SCI Parga-Ladra-Tamoga (NW Spain)

At 10km CLC shows a total gap in the most part of the habitats (Table 10). Only heaths and the woodlands with *Quercus spp.* (9230) have good correspondence. At 1 km CLC shows total or very high gap in any case. On the other hand, the model of *Terras do Miño* shows good results at 10km. At 1km, the most part of the habitats present good correspondence or moderate gap. Only two habitats present total gap which corresponds to water courses (3260 and 3270) which can be assigned to the constraints of the spatial resolution of the images.

Results show that, although both sources (CORINE and the model) are based on LANDSAT images with 30m of spatial resolution, the inclusion of decisive variables in the classification processes along with the identification of ecological units was crucial. And it is again proved that it is uncertain to use CLC as a proxy of habitat maps.