Rural children know cavity-nesting birds of the Atlantic Forest but may underappreciate their critical habitat
Data files
Oct 11, 2024 version files 82.04 KB
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drawings_rawdata.csv
11 KB
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freelist_rawdata.csv
52.23 KB
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nestfarm.csv
1.06 KB
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nestpark.csv
2.73 KB
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randomfarm.csv
2.57 KB
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randompark.csv
6.10 KB
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README.md
3.18 KB
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trees_dissimilarity.csv
2.28 KB
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treesdrawing.csv
892 B
Abstract
Cavity-nesting birds are a diverse and charismatic community, with a common need for tree cavities that makes them vulnerable to land management by humans. However, little research has formally integrated human social aspects into management recommendations for the conservation of cavity-nesting birds. In agroecosystems, people's management decisions modify and define the habitat availability for native cavity-nesting species. These behaviors during adulthood are related to people's worldviews and are shaped, in part, by childhood experiences. On-going forest loss may reduce opportunities for children to interact with and learn from cavity-nesting birds and their habitats. We used a social-ecological framework to assess rural children's knowledge and representations of native cavity-nesting birds and their habitats in agroecosystems of the threatened Atlantic Forest of Argentina. We employed “freelists” and "draw-and-explain" strategies with 235 children from 19 rural schools, and then compared results with a 4-year dataset of trees (n = 328) and tree-cavity nests (n = 164) in the same study area. Children listed a high diversity (93 taxa) of native cavity-nesting birds, especially parrots (Psittacidae), toucans (Ramphastidae), and woodpeckers (Picidae), which they mostly recognized as cavity-nesters. However, children drew agricultural landscapes with few of the habitat features that these birds require (e.g., tree cavities, native forest). Exotic trees were overrepresented in drawings (40% of mentions) compared to our field dataset of nests (10%) and trees on farms (15%). Although children mentioned and depicted a high diversity of native cavity-nesting birds, our results may reveal a problematic extinction of experience regarding how these birds interact with their habitat. To strengthen children's contextualized knowledge and promote their long-term commitment to the conservation of cavity-nesting species, we recommend fostering meaningful experiences for children to interact with native cavity-nesting birds and recognize their habitat needs. A version of this article translated into Spanish is available in the Supplementary Material 1.
Description of the data and file structure
Freelists
To assess children’s knowledge of birds we used a freelisting method. Every participant wrote a list of bird species, and we systematized the lists in freelist_rawdata.csv
We used the ‘Anthrotools’ package to analyze the information (analyses.R).
Drawings
To assess children’s perception of the farms and bird’s habitats, we performed a draw-and-explain method.
We analyzed the drawings to identify: native bird species, exotic bird species, tree cavities, dead trees, tree species, and presence of native forest (showed in drawings_rawdata.csv).
We also used a database of tree species of: nest trees in protected forest (nestpark.csv), random trees in protected forest (randompark.csv), nest trees on farms (nestfarm.csv), and random trees on farms (randomfarm.csv).
We performed a rarefy analysis and a calculation of Morisita’s Dissimilarity Index in order to compare tree species in drawings (treesdrawings.csv) with nest trees and random trees in parks and on farms (trees_dissimilarity.csv).
We used ‘vegan’, ‘dplyr’, and ‘tidyverse’ packages to process and analyze the information (analyses.R).
Files and Variables description
freelist_rawdata.csv
Subj = Numeric code for each participant. Repeated as many times as the number of birds that participant listed.
Sch_Part = Code for each participant, showing school_participant (e.g. A_1). Repeated as many times as number of birds that participant listed.
CODE = Bird listed by the participant. Cientific names and synonyms for each code are shown in Supplementary Material 2.
Order = Order in which every bird was listed by each participant.
drawings_rawdata.csv
ID = Unique code
Native = 1 = presence of a native animal species (including birds); 0 = absence of native animal species
NatID = Common name of the native species
Exotic = 1 = presence of exotic animal species (including birds); 0 = absence of exotic animal species
ExoID = Common name of the exotic species
NativeTree = 1 = presence of a native tree species; 0 = absence of a native tree species
NatTreeID = Common name of the native tree species
ExoTree = 1 = presence of a exotic tree species; 0 = absence of a exotic tree species
ExoTreeID = Common name of the exotic tree species
Forest = 1 = presence of native forest; 0 = absence of native forest
Cav = 1 = presence of tree cavities; 0 = absence of tree cavities
Nest = 1 = presence of a nest; 0 = absence of a nest
nestpark.csv
Tree.ID = Unique code
Tree = Tree species
randompark.csv
Tree.ID = Unique code
Tree = Tree species
nestfarm.csv
Tree.ID = Unique code
Tree = Tree species
randomfarm.csv
Tree.ID = Unique code
Tree = Tree species
treesdrawing.csv
Sp = indicates scientific name of trees in drawings
x = indicates the number of mentions of each tree especies in all drawings
trees_dissimilarity.csv
All columns indicate scientific tree species = numbers indicate the number of individual trees or mention in the drawings
Study area and socioecological context
We worked in high altitude terrain within the department of San Pedro, Misiones province (26° 36'S, 54° 01'W; 500-700 m a.s.l., 1200-1400 mm annual rainfall). The study area encompassed much of the remaining extent of Araucaria mixed rainforest in Argentina. This forest is composed of >100 tree species, including Nectandra spp. and Ocotea spp. (laureles), Balfourodendron riedalianum (guatambú) and Araucaria angustifolia (Paraná pine), a critically endangered species (Cabrera 1976, Kershaw and Wagstaff 2001, Thomas 2013). The study area covers two Important Bird Areas: San Pedro (AICA AR123) and Cruce Caballero Provincial Park (AICA AR122; Bodrati and Cockle 2005, Bodrati et al. 2005, Birdlife International 2019). Here, researchers have recorded at least 75 bird species in 21 families that are known or strongly suspected to nest in tree cavities (Bonaparte 2024). Twenty-four of these species are endemic to the Atlantic Forest and seven are internationally threatened or near-threatened. In well-preserved Atlantic Forest, many cavity-nesting species select cavities in large, live, native trees for nesting (Cockle et al. 2011). However, in family agroecosystems, dead trees with cavities excavated by woodpeckers become increasingly important to the cavity-nesting community, probably because they replace the resource of large native trees with decay-formed cavities that are scarce in agroecosystems (Bonaparte et al. 2020).
The study area encompassed both public and private lands and comprised a mosaic of small and medium-sized family farms (mean ± SD = 36 ± 24 ha). This mosaic is characterized by patches and corridors of forest, as well as open paddocks, annual and perennial plantations, and both native and exotic tree plantations, interspersed with three provincial parks that have varying histories of selective logging and other land uses (Varns 2012). Scattered native and exotic trees are common in plantations, in pastures, and around residential areas; provide diverse ecosystem services to agricultural families; and constitute important habitat elements for many cavity-nesting bird species (Bonaparte et al. 2020). Traditionally, people that live and farm in rural areas of Misiones call themselves "colonos", and the rural areas they inhabit are referred to as "colonia". The “colono” families have varied origins (many are immigrants from Europe, Brazil, or Paraguay). In many cases, they arrived in Misiones during the 20th century with permits to occupy small plots on fiscal lands or as occupants of private lands. In our study area, 67% of the human population resides in rural areas (IPEC 2015) and their main productive activity is family agriculture, with no salaried labor (or little when it exists) and low accumulation potential (Baranger et al. 2008). Their production may be destined for family consumption, informal sales, and industry-oriented sales (Furlán et al. 2015).
Study design and participants
There are some difficulties in assessing children's ideas because they may lack the vocabulary they need to express themselves, or because they are sometimes shy and it is difficult for an unfamiliar person to access their opinion (Sullivan et al. 2018). However, there are several tools adapted for children of different ages that help researchers understand how they see and what they know about the landscape around them. A widely used tool in ethnobiology is the "freelisting" method, hereafter referred to as freelists. This method highlights elements within a given domain that are locally important or significant to respondents (Puri 2010). From freelist data (see below), researchers can calculate relative salience (a statistic that includes rank and frequency) of items within a given domain across all respondents (Quinlan 2005). Another tool used to assess children's representations and interpretations of their environment is the "draw-and-explain" method (Moseley et al. 2010), which seeks to access, in an easy and familiar way, children's ideas and visual representations of a given place (Barraza and Robottom 2008, Franquesa-Soler and Serio-Silva 2017). The combined assessment of these two activities constitutes a mixed approach that allowed us to obtain quantitative and qualitative information about children's knowledge, observations of their environment, and the most salient, important, and familiar elements of their surroundings.
In this study we used a mixed methods approach composed of two steps. The first step consisted of two independent activities, specially adapted for rural students in the last three grades of formal primary education in Argentina (10 to 13 years of age). The activities developed with the participant students consisted of a freelisting method (Puri 2010) and a drawing activity (“draw-and-explain” method; Moseley et al. 2010), carried out at school. The second step consisted of comparing the results obtained from the activities with the participants with field data on the cavity-nesting bird community in the area and the characteristics and species of trees they use for nesting (e.g., tree species used as nest trees).
Prior to starting the data collection at each school, we held a private, in-person meeting with the principal or teacher in charge. During these 15- to 30-minute meetings, we provided a formal letter describing our objectives, methodology, scope of the study, and expected forms of disseminating results. We then verbally described the details written in the letter, explained the planned activities, and answered questions about the research and logistics. Finally, we verbally requested their free, prior, and informed consent to carry out the activities (Newing 2010), and agreed on a date to visit the school and perform the activities.
During April and May 2019, we visited 19 rural schools. Previously, we visited one additional school as a pilot to test and adjust the activities with 18 students; the results of the activities in the pilot school are not presented here. All schools visited were rural public schools with 12 to 120 students each. Participants were 236 students aged 10 to 13 years (mean ± SD = 11.6 ± 0.8; 9% 10 years old, 37% 11 years old, 43% 12 years old, and 11% 13 years old), in the last three years of formal primary education in Argentina. We decided not to gather data on the gender of study participants because our research was not focused on gender-related questions (Radi 2021). Collecting these data a posteriori based on participants' first names leads to misgendering and reinforces harmful cisnormative constructs. We consider the participant group in this study, students of public rural primary schools, to be representative because there are no private schools in the area and we did not observe gender bias in the groups of students attending classes.
Description of methodologies at each school
Upon arrival in the classroom, we conducted a playful icebreaker and gave a brief introductory talk (Barreau et al. 2016). During the introductory talk, we described in a general but clear way our objectives and the activities we would conduct with the participants, trying not to bias their upcoming answers. We asked the participants to complete the activities individually. Additionally, we informed them that our proposal was neither a school assignment nor mandatory, so they could opt out of the activities if they wished.
The first activity we developed at each school was the freelist to assess the salience and knowledge of native birds. For this, we provided each participant with a pencil and a sheet of paper with spaces to write their name, age, and grade, followed by ten numbered rows to write the names of bird species. We instructed the children to complete their personal information and then to write down the ten species of native birds they knew that lived in the wild on farms that first came to mind. If they did not know up to ten species, they could write down as many as they could and, if they could name more than ten, they had the option to continue writing on the back of the paper. During the development of the freelist and drawing activities, two coordinators (EBB and MHS) were present answering students' questions and encouraging them to perform the tasks individually. After writing down all the species they remembered, participants received a highlighter. We asked them to highlight only the species on their lists that they considered to nest in tree cavities. This entire activity took 10-20 minutes and then the lists were collected.
Following the freelists, we used a drawing assessment method, adapted from the Draw-an-environment Test Rubric (DAET-R; Moseley et al. 2010). To perform the drawing activity, each participant received a white sheet of paper and colored pencils. We asked them to close their eyes and imagine the landscape of their farm, especially a place they liked. If they did not have a farm they could think of a relative's farm. Then, we instructed them to represent that mental image in a drawing. When each participant finished their drawing, one of the coordinators asked them individually to describe the landscape in their drawing, naming all the species and other elements that they drew. The coordinator wrote down on the drawing each name given to each element or group of elements. This entire activity took 30-60 minutes and then the drawings were collected.
Nest trees and random trees in family agroecosystems and protected areas
We compared the representation of trees in the drawings with information about nest trees and randomly selected trees that we collected from protected forest and family agroecosystems for a case-control study of nest-site selection (Bonaparte et al. 2020). We found nests from 2015 to 2018 by observing adult birds, listening for nestlings, inspecting cavities, and rechecking the contents of cavities used in previous years. Each nest was confirmed if it contained eggs or nestlings, or if adults exhibited nesting behavior. For each nest tree, we randomly selected two additional trees, in a random direction, at a distance of 20 to 100 m (Bonaparte et al. 2020). Here we use information about the condition of the tree (dead or alive) and its species for each nest tree and random tree.
Evaluation of activities and statistical analyses
All statistical analyses were performed using R 3.6.1 (R Core Team 2021). In this article, we will refer to a 'taxon' (and 'taxa' in plural) as a unified group of birds that, for taxonomic classifications, can correspond to a species, genus, family, or order. We use this term when regrouping various common names provided by participants which do not always correspond to a single species. To analyze the freelists, we identified (1) the most specific taxa we were able to assign to the common name listed (species, genus, or family; for updated scientific names we used Remsen et al. 2024), (2) family to which each taxon belongs, (3) whether each taxon was native to the study area, (4) which taxa were highlighted with highlighter, and (5) which taxa listed included cavity-nesting birds. We assigned each taxon to a habitat (forest in good conservation status, secondary or selectively logged forest in agroecosystems, and/or other modified habitats such as crops and pastures) following Stotz et al. (1996) and Bodrati et al. (2010). To analyze the freelists we used two functions in the AnthroTools package (Purzycki and Jamieson-Lane 2017). Using the CalculateSalience function we calculated the salience of each item in each list, as:
(n + 1 - k) / n
where n is the total number of birds listed and k is the rank order in which an item was listed. Then, for each taxon mentioned, we used the SalienceByCode function to calculate the Smith salience (sum of the salience of each taxon divided by the total number of lists) and the average salience (sum of the salience of each taxon divided by the number of lists in which it was included; Purzycki and Jamieson-Lane 2017). Finally, we used the same package to calculate the frequency of mentions for each taxon.
To analyze the content of the drawings, we identified in each the presence, number, and identity of (1) native and exotic birds, (2) cavity-nesting birds, (3) native and exotic trees, and (4) dead trees. We also determined the presence of (5) tree cavities, (6) bird nests, and (7) native forest. We developed the content assessment of the representations using an iterative method with a group of drawings to capture all relevant information. First, we randomly selected 100 drawings (43%). EBB and KLC scored these 100 drawings independently, and we calculated the intraclass correlation coefficient, which measures inter-rater reliability for interval data (ICC; Hallgren 2012). Using the icc command of the "irr" package (Gamer et al. 2019), we obtained an ICC of 0.92, which is considered excellent inter-rater reliability (Hallgren 2012). Thus, EBB scored the remaining drawings and we based our analysis on her scores alone.
To compare the tree taxa mentioned in the drawings with nest trees and random trees in protected forest and agroecosystems, we used a rarefaction method to statistically contrast the species accumulation curve (using the rarefy function in the “vegan” package in R), and a dissimilarity index to compare the taxonomic composition of the three groups (Morisita index, using the vegdist function in the “vegan” package; Oksanen et al. 2020). We used a standardized rarefaction method using an individual approach (Gotelli and Colwell 2001). Each mention of a species in the drawings was counted as a sampled individual of that species. For the field data, each nest tree or random tree was counted as a sampled individual. To evaluate the similarity of the samples we used the Morisita index, which is a non-parametric index that evaluates differences in the abundance of species found in different samples. This index is sensitive to common species, handles unequal sample sizes, and varies from 0 (completely equal sample) to 1 (completely different sample; Wolda 1981, Oksanen et al. 2020).