Datos de investigación
Data for nested analysis of variance of students master responses regarding the protection of different biodiversity scenarios
Autores:
Bermudez, Gonzalo Miguel Angel
Publicador:
Consejo Nacional de Investigaciones Científicas y Técnicas
Fecha de depósito:
12/02/2025
Fecha de creación:
11/02/2025
Clasificación temática:
Resumen
The definition of biodiversity stated by the Convention on Biological Diversity (CBD) in 1992 was conceived as occurring on three different organizational levels: genetic, species, and ecosystems. However, current understanding of biodiversity includes other components, such as the number, abundance, composition, and spatial distribution of species and functional groups. This paper aimed to identify high school students’ frameworks of biodiversity, to assess their conceptual understanding of biodiversity against scientific definitions, and to analyze the influence of sex and school location on students’ understanding of biodiversity. By administering a written questionnaire in which ten different biodiversity scenarios were presented, each consisting of two environments which differed in certain biodiversity components, we asked students (n = 321, 15–18 years old) to choose and argue their preference for biodiversity conservation. Students held a range of frameworks of biodiversity, with some of them being in agreement with scientific conceptualizations (idea of variance as the number of species, functional groups, and trophic relationships). However, students were strongly centered on species richness and undervalued population size, functional characters, species evenness, and alpha diversity. Biodiversity was associated with a notion of balance, by which a proportioned trophic chain prevents species extinction. Overall, students used few components of biodiversity in their argumentations, with no influence of school location or sex. We recommend that teachers fully integrate students’ frameworks with more updated definitions of biodiversity than that of the CBD, conceptualizing its components in order to empower students to decide on current socioscientific issues.
Otro
Survey administration and sampling We conducted a questionnaire-based survey aimed at understanding students’ conceptual understanding of biodiversity. To begin with, thirteen high school teachers were selected with the help of a non-profit organization that provides a network for biology teachers, and an institution that provides a master course in science education. Selection criteria were that teachers had not only taught about biodiversity in the year of study, but were also representing different school locations (urban, and rural). All questionnaires were personally administered by author 1, and students (58% girls) had twenty-five minutes to complete the tasks. Overall, 321 students of the last three years of the Argentinean mandatory system (15-18 years old) from thirteen schools (always one class per school and participating teacher) filled-in the questionnaire. Seven schools were located in an urban environment. The last three grades of Argentine secondary education (4th, 5th and 6th) are equivalent to grades 10, 11 and 12 of the US education system. The questionnaire We used a ‘scenario approach’ to represent different components of biodiversity. A scenario consists of a plausible alternative situation based on a particular set of assumptions, which projects the impact of management decisions on biodiversity (Díaz et al. 2015b). In the questionnaire, ten different biodiversity scenarios were randomly presented to the students (Online resource 1). In each scenario, two environments were depicted which differed in certain biodiversity components. For each scenario, participants had to judge which of the two environments should be chosen for biodiversity conservation. According to our operational definition of biodiversity, the environments differed in species richness in and among habitats (alpha and beta diversity), genetic and functional group richness, evenness, range in functional traits, species interactions, and population size. Students were introduced to a problem-solving task as follows: “If you were hired by the government in order to create a new national park for biodiversity conservation, and the following schemes were shown to you (of which a and b represent different environments that are available to conserve), which one would you choose?”. Students were also provided with two more answers (‘c’ and ‘d’) in case they thought that both environments were equally important to protect (option ‘c’), or in case the depicted features were not considered to be related to biodiversity and no decision could be made (option ‘d’). Students were also asked to provide a reason for each of their choices (‘a’-‘d’) with the aim to interpret their conceptual understanding of biodiversity and limit the effect of chance (see Figure 1). From here on, the order of presentation and analysis of biodiversity scenarios is organized in biodiversity components rather than following the sequence numbering of the questionnaire (Online resource 1). According to the conceptual-phenomenological dimension in the elicitation of students’ conceptual frameworks (Driver and Erickson 1983), the conceptual aspect of the current study was the design of the scenarios. Based on our operational definition of biodiversity, justifications for the scenarios and the correct choices from a scientific point of view were as follows: -Scenario 1 (same species richness, same evenness): the relationship between biodiversity and key ecosystem processes depends, among other components, on species richness and evenness (Chapin III et al. 2002; MEA 2005). Consequently, as species richness and evenness were the same in both environments in scenario 1, the correct answer would be ‘c’ (equal importance for biodiversity conservation). -Scenario 7 (same species richness, different evenness): in an environment, most species are rare, while only few species are abundant. A more even distribution of individuals of species in an environment contributes more to ecosystem stability than a less even distribution, because dominant species account for most of the energy and nutrient flow through an ecosystem (Chapin III et al. 2002). Moreover, rare species are more likely to vanish after disturbances (Mulder et al. 2004). Hence, the correct answer to scenario 7 would be ‘a’. -Scenario 2 and scenario 4 (same or different species richness, different number of functional groups): species can be grouped into functional groups (e.g., trees, herbs, legumes). For a given ecosystem, functionally diverse communities are more likely to adapt to climate change and climate variability than impoverished ones, and to provide more ecosystem services (Secretariat of the Convention on Biological Diversity 2003). The higher the number of functional groups in an environment is, the lower the chance is that disturbances affect ecosystem functioning (Chapin III et al. 2002). Consequently, the correct answer to scenario 2 would be ‘b’, and to scenario 4 would be ‘a’. -Scenario 5 (different species richness at local scale, same richness among habitats): a basic measure of species richness is alpha diversity, i.e., the number of species found at given localities or single samples (3 point samples, options ‘a’ and ‘b’). Alpha diversity does not necessarily co-vary with beta diversity, or diversity among habitats or along an environmental gradient (Seidler and Bawa 2013). However, alpha and beta diversity increase from polar to tropical regions is one of the most important and well-documented macroecological patterns of biodiversity (Enquist et al. 2001). Also, alpha diversity measures the number of potentially interacting species, which may influence beta diversity in the context of environmental changes (Schneider 2001). Consequently, alpha diversity is higher in option ‘b’ (correct answer), while beta diversity is equal in both scenarios. -Scenario 6 and scenario 8 (same species richness, different diversity in canopy structure): the value and range of functional traits (functional diversity) determine ecosystem functioning more strongly than species numbers per se (Díaz and Cabido 2001). Diversity within a functional group (e.g., within trees) and a functional trait (e.g., canopy structure) increases the probability that natural and human-made disturbances can be buffered (Chapin III et al. 2002). Consequently, option ‘b’ would be the correct for both scenarios. -Scenario 9 (same species richness, different number of interactions): the wider meaning of biodiversity (Díaz et al. 2006) includes the interactions among species. The more complex and rich these interactions are, the more likely disturbances can be buffered (Chapin III et al. 2002; Hellmann 2013). Consequently, the correct answer to scenario 9 would be ‘a’. -Scenario 3 (same species richness, different population sizes): abundance matters more for ecosystem services than the presence or range of genetic varieties, species, and ecosystem types (MEA 2005). The probability of population bottlenecks due to environmental events or human activities is smaller in large populations than in small ones (Zedler and Lindig-Cisneros 2013). Consequently, the correct answer to scenario 3 would be ‘b’. -Scenario 10 (different genetic compositions of corn): genetic diversity is an important component of biodiversity (Hamilton 2005; Pingali and Smale 2013). Genetic diversity in a population of corn, for instance, increases the chance that at least some of their members can cope with changing environmental conditions such as drought. Consequently, the correct answer to scenario 10 would be ‘b’. Questionnaire validity In order to assess the content validity of the tasks, a draft version of the questionnaire was shown to one senior university lecturer in ecology and to another one in science education. Revisions were carried out based on their comments and suggestions. Later, the draft version was pilot-tested with a sample of ten last-year high school students and five first-year biology students. This allowed to reconfigure the options of the multiple choice tasks and to understand if students interpreted the scenarios correctly. After amendments, the last version of the questionnaire was reviewed and accepted by a science education and a social science expert. Data analysis Nomothetic study (objectives 2 and 3) Students’ choices for each scenario and respective explanations were separately and jointly analyzed in order to identify their levels of biodiversity understanding. The students’ choices (option a-d) were counted, expressed as proportions (%) and categorized into ‘correct’ (= ‘1’) or ‘incorrect’ (= ‘0’) (Table 1, Table 2) according to our operational definition of biodiversity. The answers to the open question (explanations for the choice of option a-d) were content-analyzed in terms of the types of reasons given and coded into categories (Driver and Erickson 1983). Coding was discussed in the research group and reliability judged by comparing their coding. To test for reliability, the authors transcribed the students’ answers of a random subset of questionnaires (30%) and independently coded them. After that, the authors discussed the codes and the most common students’ phrases that accurately described the ‘correct’ model answer for each of biodiversity scenario. The authors agreed upon the following: (a) when students say “species variety”, this was interpreted to mean ‘species number’ (richness); (b) “diversity” and “biodiversity” were considered to be synonyms; (c) “species diversity” was interpreted to mean ‘species number’ (richness); (d), “number of plants” ” was considered to refer to ‘number of individuals’ (population size); (e) “types of plants” was interpreted to mean ‘functional groups’; and (f) “proportion of” and “balance among” species were considered to refer to ‘species evenness’. The authors then reexamined the subset of questionnaires, compared their codings and achieved an agreement higher than 90% throughout the questionnaire. The authors also resolved through discussion the coding of students’ understanding of biodiversity for the entirety of questionnaires when a student’ answer differed from the previously agreed system. The agreed ‘correct’ model answers for each scenario are the following: ‘correct’ answers to scenarios 1 and 7 acknowledge that the number of species is equal in schemes ‘a’ and ‘b’ (possibly giving richness values), but that the proportion of individuals among the species is different: while evenness is the same in scenario 1, it is different in scenario 7 (possibly counting individuals per species). ‘Correct’ model answer to scenario 2 admits that the number of species is the same for the two schemes, but that the number of types of plants was higher in scheme ‘a’ (feasibly giving the number of trees, cacti, etc.). Conversely, ‘correct’ model answer to scenario 4 recognizes that species richness and number of functional groups are higher in scheme ‘b’. ‘Correct’ model answer to scenario 5 acknowledges that although species number is the same in both schemes (possibly providing species richness), it is higher at each sector (local scale) in scheme ‘b’. ‘Correct’ model answers to scenarios 6 and 8 admit that species richness are the same (possibly providing species number), but that the tree canopy structures are more contrasting in schemes ‘b’ than in schemes ‘a’. ‘Correct’ model answer to scenario 9 acknowledge that the number of alimentary interactions (and interconnectedness) among species is higher in scheme ‘a’, although the number of species is the same in both schemes. ‘Correct’ model answer to scenario 3 admits that the number of individuals of each species is higher in scheme ‘b’, yet the number of species is constant (possibly providing species richness). ‘Correct’ model answer to scenario 10 acknowledges that ‘corn’ is shown in both schemes, but that there are many more types in scheme ‘b’. Examples of ‘correct’ students’ answers for scenarios 1 and 7 are presented in Table 2 and in the Results section. After the coding of students’ answers, they were sorted into two broad categories of understanding of biodiversity: ‘incorrect/incomplete’ (= ‘0’) and ‘correct’ (= ‘1’). Although we acknowledge the difference between ‘incorrect’ and ‘incomplete’ understanding of biodiversity, we decided to group them into one category in order to facilitate the statistical analysis. In addition, the distinction of intermediate levels of understanding (Wiske 1998) would have allowed the coding of not mutually exclusive codes (To et al. 2017). In the current study, ‘incorrect’ students’ responses showed mainly their misunderstanding of the biodiversity components or the possibility of picking an option by chance. For instance, a boy’s answer to scenario 1 saying that “Scheme ‘b’ has more species” (6th grade, Table 2) was considered to be ‘incorrect’ because species richness was the same in both schemes. An ‘incomplete’ understanding of the biodiversity components points out that students’ conceptual frameworks are only partially correct within the context of the questionnaire. For instance, a girl’s response to scenario 1 saying that “There is no diversity, the species are the same in ‘a’ and ‘b’” (4th grade, Table 2) was interpreted to mean that she acknowledged ‘species composition’, but that she disregarded ‘species richness’ and ‘evenness’ for biodiversity conservation, and thus the girl’s answer was coded as ‘incomplete’. Other examples of ‘incorrect/incomplete’ students’ answers are presented in Table 2 (for scenarios 1 and 7) and in the result part. Students’ choices for each scenario and respective explanations were jointly analyzed by multiplying each choice (0 = ‘incorrect’, 1 = ‘correct’) with the corresponding explanation (0 = ‘incorrect/incomplete’, 1 = ‘correct’) (Table 2), and thus generating a new variable of students’ conceptual understanding. Coding for jointly biodiversity understanding included ‘master’, ‘novice’ and ‘naïve’ levels (Wiske 1998). A ‘master’ level of understanding of biodiversity represented students who had picked the ‘correct’ choice for a given scenario and also provided the ‘correct’ explanation for their choice (1 x 1 = 1). A ‘novice’ understanding of biodiversity was coded for ‘correct’ choices and ‘incorrect/incomplete’ reasons (1 x 0), and ‘naïve’ students’ understanding of biodiversity indicated the students’ ‘incorrect’ choices and ‘incorrect/incomplete’ reasons (0 x 0). After that, an additive scenario approach was undertaken in order to integrate the components of biodiversity of each scenario into our operational conceptualization of biodiversity. Therefore, a new variable was created by the summation of the answers coded as ‘master’ understanding throughout the questionnaire, resulting in a maximum score of 10 (= number of scenarios). This ‘additive master’ variable was seen as an indicator of the understanding of the operational conceptualization of biodiversity. Mean values, standard deviation (SD) and range were calculated and informed for the ‘additive master’ variable. Possible relationships between the explanatory variables and the ‘additive master’ variable were tested with nested analysis of variance (objective 3). The effect of school location (urban, rural) was tested against the residual variation among the classes, while the effects of school class and sex were tested against the error term. All analyses were carried out with IBM SPSS 22 for Windows.
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Datos de Investigación(CCT - CORDOBA)
Datos de Investigación de CTRO.CIENTIFICO TECNOL.CONICET - CORDOBA
Datos de Investigación de CTRO.CIENTIFICO TECNOL.CONICET - CORDOBA
Citación
Bermudez, Gonzalo Miguel Angel; (2025): Data for nested analysis of variance of students master responses regarding the protection of different biodiversity scenarios. Consejo Nacional de Investigaciones Científicas y Técnicas. (dataset). http://hdl.handle.net/11336/254113
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