DOI: 10.5061/dryad.kkwh70sd8
title: Rural children know cavity-nesting birds of the Atlantic Forest but may underappreciate their critical habitat

description: 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: <strong>Study area and socioecological
 context</strong> 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 &gt;100 tree
 species, including <em>Nectandra</em> spp. and
 <em>Ocotea</em> spp. (laureles), <em>Balfourodendron
 riedalianum</em> (guatambú) and <em>Araucaria
 angustifolia</em> (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).
 <strong>Study design and
 participants</strong> 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 <em>a
 posteriori</em> 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. <strong>Description of
 methodologies at each school</strong> 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. <strong>Nest trees and random trees
 in family agroecosystems and protected areas</strong>
 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. <strong>Evaluation of activities and
 statistical analyses</strong> 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 <em>AnthroTools</em> package (Purzycki and
 Jamieson-Lane 2017). Using the <em>CalculateSalience</em>
 function we calculated the salience of each item in each list,
 as: (<em>n </em>+ 1
 - <em>k</em>) / <em>n</em>
 where <em>n</em> 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 <em>SalienceByCode</em> 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
 <em>vegdist</em> 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).
description: # Rural children know cavity-nesting birds of the Atlantic Forest but may
 underappreciate their critical habitat ## 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