Article Assemblage Recovery in a Chronosequence of Tropical Dry Forests in Costa Rica

Branko Hilje 1,* , Natalie V. Sánchez 2 , Erin M. Bayne 2 and Arturo Sánchez-Azofeifa 3

1 Council on International Educational Exchange, Monteverde 60109, Costa Rica 2 Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; [email protected] (N.V.S.); [email protected] (E.M.B.) 3 Center for Earth Observation Sciences (CEOS), Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada; [email protected] * Correspondence: [email protected]; Tel.: +506-2645-5626

 Received: 7 April 2020; Accepted: 26 May 2020; Published: 2 June 2020 

Abstract: Research Highlights: While forest structure recovery in successional tropical forests is well studied, the recovery of fauna and changes in species composition and assemblage along forests succession is not well understood in many areas of the Neotropics like tropical dry forests (TDFs). Background and Objectives: To compare bird species richness and assemblage in tropical dry forests (TDFs) of different ages of recovery from cattle ranching and relate both to forest structural characteristics. Materials and Methods: To sample bird species richness and assemblage in 16 successional forest of different ages (i.e., 20, 30, 40, and 60 years old) using autonomous sound recording units in the TDFs in Costa Rica. Results: A total of 64 species of was detected across all forest age classes. The highest species richness was found in the 20-year-old class. Species richness decreased as canopy openness increased, suggesting low forest structural complexity and low availability of perches, nesting sites and food sources. However, bird assemblages were similar among the different forest age classes, suggesting that age itself was not a strong predictor, likely because of high variation in structure within age classes. Conclusions: TDFs can recover structural characteristics important to birds in only a few decades, supporting a rapid bird species assemblage recovery. However, this seems to depend on the starting conditions of the site prior to being recovered. Young TDFs, 20 years old, provide similar habitats for birds as 60-year-old forests do. These findings provide relevant information on the influence of TDF recovery after severe human impact on a highly threatened ecosystem.

Keywords: forest recovery; forest structure; forest succession; secondary forests; species richness; autonomous recording units; Area de Conservación Guanacaste; Sector Santa Rosa

1. Introduction Successional forests are becoming more abundant in the tropics due to the abandonment of lands previously used for crop production and cattle ranching [1]. Economic policies toward industry and services have reduced the number of farmers in some areas. Reduced agricultural intensity is allowing some forests to recover, creating a mix of successional forests of different ages [1]. Forest structural recovery across successional stages will vary in rate and time depending on the degree of human disturbance before and during the recovery phase [2,3]. For example, disturbances that harvest trees are less likely to have as long-lasting an effect on forest structure and composition as disturbances that impact soils (e.g., agricultural conversion vs. single tree extraction) [2]. Recovery of plant species also depends on the proximity to forest patches and remnants, as well as the nature of the landscape matrix [2,4].

Forests 2020, 11, 629; doi:10.3390/f11060629 www.mdpi.com/journal/forests Forests 2020, 11, 629 2 of 13

In general, forest structure recovers gradually during succession, which means that forests of different ages should have different structures that species will respond to [5]. Typically, young forests have lower basal area (BA), higher density of stems, more open canopies, even canopy heights, and lack tall trees [6–9]. On the other hand, when forests become older, larger trees become more common and the canopies show variable heights [6–9]. Consequently, the variation in forest structure observed across a gradient of forest ages should affect the richness and composition of the forest fauna [10]. While the study of forest structure post-abandonment from human disturbance is relatively common, the recovery of fauna, specifically the change in species composition across a succession (from early stages until the forests reach old growth features) is unclear in many areas of the Neotropics. Bird species assessments in successional forests have provided valuable information on the level of forest recovery [11], since birds provide information on the functioning of various ecosystem processes (e.g., pollinators, seed dispersers, and arthropod population controllers) [12]. For example, some insectivorous bird species are not able to use deforested habitats because of a lack of forest structure to disperse [13]. In general, older tropical forests provide a greater availability of sites for nesting [14], perching and roosting [15–17], more stable microclimatic environments (e.g., temperature and relative humidity) due to canopy closure [18–20], and greater food availability (e.g., arthropods) [21]. Thus, greater species richness should be expected as succession of the forest occurs because more niches become available. However, most research into bird species richness and assemblage in tropical forests succession been carried out in wet forests, with little attention to patterns in tropical dry forests (TDFs) that also hold high bird diversity [16,22,23]. This study has two main objectives: (1) to compare bird species richness and assemblage in TDFs of different ages (i.e., 20, 30, 40, and 60 years old), and (2) to establish the relationship between bird species richness, assemblage, and forest structural characteristics. This study was conducted using autonomous recording units (ARUs) to estimate bird species richness and assemblage in successional forests, and remote sensing techniques to assess forest age and structural characteristics. Based on forest recovery evidence reported in the literature and its relationship with bird responses, bird species richness is expected to increase as forest structure becomes more complex along a successional gradient. Older successional forests will provide a higher structural complexity (e.g., nesting sites, breeding and roosting perches, and protection from predators) than young successional forests [11,14,23,24]. Additionally, the biggest difference in bird assemblage is expected between the early (20 years) and late forest ages (60 year) as the early stages are expected to have the least complex forest structure. This study provides evidence of forest recovery and its effects on the local avifauna after severe human impacts in the dry forest, a highly threatened habitat in the Neotropics.

2. Materials and Methods

2.1. Study Site This study was carried out in the Área de Conservación Guanacaste (ACG), which contains more dry forest than any other protected area in . Specifically, the study was carried out in Sector Santa Rosa (SSR), located in the province of Guanacaste, Costa Rica (10◦4805300 N& 2 85◦3605400 W) (Figure1). The SSR covers an area of 390 km , and the climate has a dry season of six months (December–May) with a mean annual precipitation of 1700 mm and a mean annual temperature of 25 ◦C[25]. The SSR current land cover is a mixture of forest successional stages that recovered from intense in the past 200 years due to human activities such as pasturelands for cattle ranching [26]. Current secondary forests are dominated by -pollinated and wind-dispersed tree species [27]. The SSR has a very heterogeneous forest that holds a high diversity of habitats and biotic communities [28]. Forests 2020, 11, 629 3 of 13 Forests 2020, 11, x FOR PEER REVIEW 3 of 14

FigureFigure 1. 1. LocationLocation of the 16 dry forest study study sites sites at at the the Sector Sector Santa Santa Rosa Rosa within within the the Area Area de de ConservaciConservaciónón Guanacaste Guanacaste (ACG)(ACG) (black(black section), Guanacaste, Guanacaste, Costa Costa Rica Rica (modified (modified from from [29]). [29 ]).

2.2.2.2. Dry Dry Forest Forest Structural Structural CharacteristicsCharacteristics and Forest Age Age SixteenSixteen forest forest sites sites at at didifferentfferent agesages of successional recovery recovery were were selected. selected. Each Each site site consists consists of of 2 a pointa point located located within within a a forest forest ofof aa particularparticular age. The The sites sites were were located located in in an an area area of of40 40km km2, where, where thethe largest largest section section of of continuous continuous forests forests is is found found at at the the SSR. SSR. This This is is a flata flat area area where where bird bird acoustic acoustic data collectiondata collection is not aisff ectednot affected by hills, by edges, hills, edges, and canyons, and canyons, and this and is alsothis is the also area the where area where the most the intensive most cattleintensive ranching cattle activities ranching occurred activities inoccurred the past in within the past SSR. within Forests SSR. wereForests categorized were categorized in age classesin age of 20classes (four sites),of 20 (four 30 (four sites), sites), 30 (four 40 (fivesites), sites), 40 (five and sites), 60 (three and 60 sites) (three years sites) old years on old the on basis the ofbasis the of date the on whichdate theyon which were they detected were and, detected therefore, and, consideredtherefore, considered forests (i.e., forests 10 years (i.e., old) 10 years in aerial old) photographs in aerial andphotographs satellite images and satellite [29]. The images minimum [29]. The area minimum of a forest area was of 0.2 a forest ha and was the 0.2 maximum ha and the area maximum was 432 ha. Thearea minimum was 432 distanceha. The minimum between thedistance two closest between sites the was two 250 closest m and sites the was maximum 250 m and distance the maximum was 6000 m (Figuredistance1). was However, 6000 m these(Figure two 1). sitesHowever, were these within two the sites same were age within class. the Aside same from age class. these Aside sites, from 500 m isthese the minimum sites, 500 m distance is the minimum between distance two other between sites. two Forests other sites. were Forests further were characterized further characterized using forest structuralusing forest characteristics structural characteristics previously measuredpreviously inmeasured standardized in standardized 0.1-ha radial 0.1-ha plots radial in eachplots sitein each based site based on Gentry’s dataset [29,30]. These included the percentage of canopy openness (CO) on Gentry’s dataset [29,30]. These included the percentage of canopy openness (CO) extracted from extracted from hemispherical photos; the plant area index (PAI) which is the area of foliage and wood hemispherical photos; the plant area index (PAI) which is the area of foliage and wood (i.e., stems, (i.e., stems, twigs, lianas) present in a hemispherical photo; canopy height (CH) measured with a twigs, lianas) present in a hemispherical photo; canopy height (CH) measured with a terrestrial laser terrestrial laser scanner; tree basal area (BA) calculated from measurements of tree diameter at breast scanner; tree basal area (BA) calculated from measurements of tree diameter at breast height (DBH) height (DBH) for all the trees observed in each plot (>5 cm of DBH); and the liana-to-tree ratio (LR) for(i.e., all thethe treestotal number observed of in lianas each in plot a plot (>5 cmdivided of DBH); by the and total the number liana-to-tree of trees) ratio [29]. (LR) A (i.e.,vegetation the total numbergreenness of lianas index in was a plot used divided to collect by information the total number on the ofpresence trees) [ 29of ].green A vegetation leaves on greennesstrees surrounding index was usedeach to site collect (i.e., informationthe Normalized on theDifference presence Vegetation of green leavesIndex (NDVI)). on trees surroundingNDVI values eachwere siteextracted (i.e.,the Normalizedfrom satellite Di ffimageserence Vegetationfrom MODIS Index collectio (NDVI)).n 6 MOD13Q1 NDVI values and were MYD13Q1 extracted from from the satellite Aqua (AM images fromcollection) MODIS and collection Terra (PM 6 MOD13Q1 collection) andinstruments, MYD13Q1 respectively, from the Aquawhich (AMoverpassed collection) the SSR and [31]. Terra The (PM

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collection)Forests 2020 instruments,, 11, x FOR PEER respectively, REVIEW which overpassed the SSR [31]. The spatial resolution for4 of NDVI14 product is 250 m. NDVI values were collected in each site encompassed in a unique pixel for the same datesspatial where resolution the acoustic for NDVI data product collection is 250 was m. carried NDVI out.values were collected in each site encompassed in a unique pixel for the same dates where the acoustic data collection was carried out. 2.3. Acoustic Data Collection 2.3. Acoustic Data Collection 2.3.1. Acoustic Detection Extent 2.3.1. Acoustic Detection Extent Autonomous recording units (ARUs) (SM3 Songmeters) [32] were used for bird sound data collection.Autonomous The recording recording distance units (ARUs) for ARUs (SM3 in Songmeters) forests from [32] di werefferent used ages for wasbird establishedsound data by performingcollection. a soundThe recording attenuation distance experiment. for ARUs Six 90-min forests length from transects different were ages established was established in each ofby three ageperforming classes: early a sound (i.e., 20–30attenuation years experiment. old), intermediate Six 90-m (i.e., length 30–40 transects years were old), established and late (> in60 each years of old) three age classes: early (i.e., 20–30 years old), intermediate (i.e., 30–40 years old), and late (>60 years at SSR [27]. We established this distance based on a test showing that playbacks at a 120-m distance old) at SSR [27]. We established this distance based on a test showing that playbacks at a 120-m from the sound source were completely attenuated. At the beginning of each transect, a low-frequency distance from the sound source were completely attenuated. At the beginning of each transect, a low- Crypturellus cinnamomeus callfrequency (i.e., 900–1600 call (i.e., Hz) 900–1600 from a Hz) Thicket from Tinamoua Thicket Tinamou ( (Crypturellus cinnamomeus, Tinamidae), Tinamidae) was was played backplayed and recordedback and recorded by ARUs by located ARUs located at 30 at m, 30 60 m, m, 60 andm, and 90 90 m m along along thethe transect. transect. Both Both the thesound sound sourcesource and and the the recorders recorders were were placed placed at at 1.5 1.5 mm height.height. The The sound sound pressure pressure level level (SPL) (SPL) of the of theplayback playback waswas set upset atup 100.1at 100.1 decibels decibels at at 1 1 m m from from the the soundsound source. This This SPL SPL is iswithin within the the normal normal range range for for severalseveral bird bird species species calls calls [33 ].[33]. A low-frequencyA low-frequency bird bird call call was was selected selected since since low-frequency low-frequency sounds sounds with longwith wavelengths long wavelengths transmit transmit better thanbetter high-frequency than high-frequency sounds sounds in forests in forests [34,35 [34,35].]. This This will providewill informationprovide information about the distance about the ARUs distance can ARUs record can bird reco sounds,rd bird sounds, but also but to also establish to establish the proper the proper distance betweendistance ARUs between to avoid ARUs recording to avoid recording the same the birds. same Ravenbirds. Raven Pro 1.5 Pro was 1.5 was used used to determineto determine SPL SPL from oscillogramsfrom oscillograms extracted extracted from the from playbacks the playbacks recorded recorded at di atfferent different distances distances from from the the sound sound source source [36]. [36].The SPL from the playback decreased as the distance increased from the sound source, following The SPL from the playback decreased as the distance increased from the sound source, following the inverse distance law equation [37]: (Excess)SPL = (SPL1 SPLi) [20 Log 10 (di/1)], where SPL1 the inverse distance law equation [37]: (Excess)SPL = (SPL1 −− SPLi) − [20− × Log× 10 (di/1)], where SPL1 is the SPL of the playback measured at 1 m used as a reference value, SPLi is the SPL recorded and is the SPL of the playback measured at 1 m used as a reference value, SPLi is the SPL recorded and measuredmeasured at four at four distances distances from from the the sound sound source source (i.e., (i.e.,30 30 m,m, 6060 m,m, and 90 m), and and d d is is the the distance distance in metersin meters to the to sound the sound source source (Figure (Figure2). 2). The The sound sound attenuation attenuation showedshowed a a similar similar pattern pattern for for the the three three forestforest age age classes, classes, having having close close SPL SPL values values atat 9090 m that ranged ranged from from 50.1–53.9 50.1–53.9 dB. dB. Based Based on these on these observationsobservations and and in resultsin results from from an an initial initial test,test, ARUs are are not not able able to to collect collect sounds sounds from from birds birds calling calling at aat 120-m a 120-m radius radius in TDFsin TDFs of of the the age age classes classes analyzed.analyzed. Since Since sound sound attenuation attenuation is similar is similar among among age age classes,classes, the the probability probability of of recording recording sounds sounds from a a bird bird is is the the same same for for the the different different classes. classes. Lastly, Lastly, ARUsARUs should should be deployedbe deployed at leastat least 120 120 m m apart apart from from each each other other to to avoid avoid recordingrecording thethe sounds from the samethe individual same individual bird. bird.

FigureFigure 2. Low-frequency 2. Low-frequency bird bird call call playback playback attenuationattenuation in in decibels decibels related related to todistance distance from from the thesound sound sourcesource at three at three diff differenterent forest forest age age classes. classes. TheThe logarithmic curves curves generated generated from from the the inverse inverse distance distance lawlaw equation equation are are shown shown for for each each age age class class (dotted (dotted lines).lines).

Forests 2020, 11, 629 5 of 13

2.3.2. Bird Acoustic Data Collection and Analysis An ARU was deployed in each exact site where forest structural characteristics were previously measured by Sánchez-Azofeifa et al. [29] to obtain information on forest structure to relate to bird species richness. Based on this, some of the sampling sites were not located in the center of each forest age patch (Figure1). The ARUs were deployed for three consecutive days in May 2017, which is during the breeding period for most TDF bird species [38]. This sampling design is widely used in bird species surveys to collect reliable data on species presence for occupancy modelling analysis [39,40]. It also allows for similar meteorological conditions and, therefore, the same plant phenological characteristics that are considered important forest structural characteristics as resources for birds, when sampling. Migrant species from North America are not expected in recordings by this time of the year. ARUs recorded for one full minute every 10 min during the peak period of avian acoustic activity at the SSR (05:30–06:40 h) [41]. The file format used for recording was “wav” at 16 bits per file, with a minimum noise gain of 88 decibels (dB), and a sampling rate of 44.1 kHz. Recordings from − 05:30–06:40 h were selected for three consecutive days and listened to in order to determine the species present at each site for this period. The software Songscope [32] was used for both aural and sound bird species identifications from spectrograms. Eight one-minute-long recordings per day for three consecutive days were analyzed per forest site for a total of 384 min for the whole study area. Wind and rainfall were not present in recording files.

2.4. Data Analysis A Linear Discriminant Analysis (LDA) was performed to determine which forest structural characteristics best described forest based on age classes [42]. A permutational multivariate analysis of variance (PERMANOVA) test with 999 permutations was subsequently performed to determine differences in forest structural characteristics between the age classes. Both analyses were performed using MASS [43] and vegan [44] packages from R statistical software version 3.6.1 [45]. Bird species richness variation explained by forest structural characteristics and the area of each of the 16 sites was analyzed using Generalized Linear Mixed Models (GLMM), using a Poisson error family and forest site as a random factor [46]. We log-transformed the forest structural characteristics to improve normality of the residuals and models’ prediction. The best model was determined using the Akaike information criterion for small samples size (AICc) [47]. The best model was the one with the highest AICc weight (wi) and the lowest ∆AICc value [48,49]. If the ∆AICc value between the best model and the next best model was less than two units, we averaged the two models for parameter estimation [50]. We used R statistical software version 3.6.1 [45] for all statistical analyses. In addition, we performed accumulation species curves for each age class. To compare species assemblages in forests with different ages, we performed a Nonmetric Multidimensional Scaling (NMDS), using the R package “vegan” [44]. A Bray–Curtis dissimilarity matrix was used for this test. We plotted the NMDS scores for two dimensions and added 95% CI ordiellipses.

3. Results

3.1. Dry Forest Structural Characteristics and Forest Age Results from the LDA showed that forest structural characteristics were similar between forest ages (F(3,5) = 0.71, p > 0.05). Higher values of NDVI, PAI, and BA were observed in older forests (60 years), higher CH in forests 40 years old, and the highest CO in forests 20 years old (Figure3). Therefore, the forest age classes evaluated were not completely distinct and had a similarity in forest structural characteristics, suggesting high variation within each class for the variables measured (Table1). From the LDA, the first axis explains 74.5% of the total variance between forest age for the structural characteristics evaluated, while the second axis explained 21.5%. Since age classes are not distinct, age was not included in the GLMM analysis. Forests 2020, 11, x FOR PEER REVIEW 6 of 14 Forests 2020, 11, 629 6 of 13

Figure 3. Linear Discriminant Analysis (LDA) for the different forest age classes (i.e., 20, 30, 40, andFigure 60 years 3. Linear old) andDiscriminant forest structural Analysis characteristics (LDA) for the (tree different basal forest area: age BA; classes percentage (i.e., 20, of 30, canopy 40, and openness;60 years CO; old) plant and areaforest index structural (PAI); characteristics canopy height: (t CH;ree andbasal Normalized area: BA; percentage Difference of Vegetation canopy openness; Index: NDVI).CO; plant Axis 1area (Can1) index explains (PAI); canopy 74.5% of height: the variance CH; an andd Normalized Axis 2 (Can2) Difference explains Vegetation 21.5%. Index: NDVI). Axis 1 (Can1) explains 74.5% of the variance and Axis 2 (Can2) explains 21.5%. Table 1. Mean (SD) forest structural characteristics recorded for the forest age classes by [29]. CO: the percentageTable 1. ofMean canopy (SD) openness, forest structural PAI: plant characteristics area index, recorded CH: canopy for height,the forest BA: age tree classes basal by area, [29]. LR: CO: the the liana-to-treepercentage ratio, of canopy and the openness, Normalized PAI: Di plantfference area Vegetationindex, CH: Index canopy (NDVI). height, BA: tree basal area, LR: the Ageliana-to-tree Class ratio, BA and the Normalized PAI Difference NDVI Vegetation CHIndex (NDVI). LR CO Age20 Class years 16.75 BA (13.19) 2.26 PAI (0.31) 0.83 NDVI (0.03) 15.38 (3.85) CH 0.03 (0.04) LR 14.11 (4.91) CO 2030 years years 16.75 22.40 (13.19) (11.09) 2.262.38 (0.31) (0.71) 0.84 0.83 (0.02) (0.03) 18.98 15.38 (5.78) (3.85) 0.17 0.03 (0.13) (0.04) 13.08 14.11(7.55) (4.91) 3040 years years 22.40 16.62 (11.09) (8.07) 2.382.47 (0.71) (0.77) 0.85 0.84 (0.02) (0.02) 24.86 18.98 (6.97) (5.78) 0.18 0.17 (0.13) (0.13) 13.12 13.08(6.19) (7.55) 4060 years years 16.62 25.00 (8.07) (9.40) 2.472.71 (0.77) (0.48) 0.88 0.85 (0.07) (0.02) 20.43 24.86 (7.85) (6.97) 0.07 0.18 (0.04) (0.13) 11.16 13.12(1.33) (6.19) 60 years 25.00 (9.40) 2.71 (0.48) 0.88 (0.07) 20.43 (7.85) 0.07 (0.04) 11.16 (1.33) 3.2. Bird Species Richness and Forest Age and Structural Characteristics 3.2. Bird Species Richness and Forest Age and Structural Characteristics A total of 64 species of bird were detected in the recordings from all forest age classes, with a range of 25–41A species total of at 64 each species site (Tableof bird2). were This detected represents in 60%the recordings of the total from bird speciesall forest that age communicate classes, with a byrange sound of in 25–41 the forests species at SSR at each (i.e., 110site species)(Table 2). [38 ].Th Inis addition,represents 41 60% of the species total bird were species detected, that correspondingcommunicate to by 79% sound of the in passerinethe forests speciesat SSR reported(i.e., 110 species) for the SSR[38]. forestsIn addition, (i.e., 52 41 species) passerine [38 species,51]. Halfwere of thedetected, total species corresponding detected wereto 79% shared of the among passerine the four species age classesreported (Table for 2the). SSR forests (i.e., 52 species) [38,51]. Half of the total species detected were shared among the four age classes (Table 2). Table 2. Total bird species detected from recordings in all the forest age classes evaluated. IUCN’s Red ListTable conservation 2. Total categoriesbird species shown. detected from recordings in all the forest age classes evaluated. IUCN’s Red List conservation categories shown. Species Species Latin Name 20 30 40 60 years Red List Species Latin Name Code Species Code 20 30 40 60 years Red List BandedBanded Wren Wren ThryothorusThryothorus pleurostictus pleurostictus BANWBANW × × × × LC LC × × × × BarredBarred Antshrike Antshrike ThamnophilusThamnophilus doliatus doliatus BAANBAAN × × × × LC LC × × × × Black-headedBlack-headed Trogon Trogon TrogonTrogon melanocephalus melanocephalus BHTRBHTR × × × × LC LC × × × × Boat-billedBoat-billed Flycatcher Flycatcher MegarynchusMegarynchus pitangua pitangua BOBFBOBF × × × × LC LC × × × × Bright-rumpedBright-rumped Attila Attila AttilaAttila spadiceus spadiceus BRATBRAT × × × LC LC × × × Brown-crested Flycatcher Myiarchus tyrannulus BCFL LC Brown-crested Flycatcher Myiarchus tyrannulus BCFL × ×× ×× × × × LC Canivet’s Emerald Chlorostilbon canivetii CAEM LC Canivet’s Emerald Chlorostilbon canivetii CAEM × × × × LC Clay-colored Thrush Turdus grayi CCTH LC Clay-colored Thrush Turdus grayi CCTH × ×× × × × LC Collared Forest-Falcon Micrastur semitorquatus COFF LC Collared Forest-Falcon Micrastur semitorquatus COFF × × × × LC Crested Guan Penelope purpurascens CRGU LC Crested Guan Penelope purpurascens CRGU × × × × × × LC Double-striped Thick-knee Burhinus bistriatus DSTK LC Double-striped Thick-knee Burhinus bistriatus DSTK × × LC Dusky-capped Flycatcher Myiarchus tuberculifer DCFL LC × × × Dusky-cappedElegant Trogon Flycatcher MyiarchusTrogon elegans tuberculifer ELTRDCFL × × × LC LC × × × × FerruginousElegant Pygmy-Owl Trogon GlaucidiumTrogon brasilianum elegans FEPOELTR × × × × LC LC × FerruginousGreat Curassow Pygmy-Owl GlaucidiumCrax rubra brasilianum GRCUFEPO × VU LC Great Curassow Crax rubra GRCU × × VU

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Table 2. Cont.

Species Species Latin Name 20 30 40 60 years Red List Code Great Kiskadee Pitangus sulphuratus GKIS LC × × Inca Dove Columbina inca INDO LC × × Ivory-billed Woodcreeper Xiphorhynchus flavigaster IBIW LC × × Keel-billed Toucan Ramphastos sulfuratus KBTO LC × Laughing Falcon Herpetotheres cachinnans LAFA LC × × × × Lesser Greenlet Hylophilus decurtatus LESG LC × × × Lesser Ground-Cuckoo Morococcyx erythropygus LEGC LC × × Lesson’s Motmot Momotus lessonii LEMO LC × × × × Long-billed Gnatwren Ramphocaenus melanurus LBGN LC × × × × Long-tailed Manakin Chiroxiphia linearis LOTM LC × × × × Northern Barred-Woodcreeper Dendrocolaptes sanctithomae NOBW LC × × × × Olive Sparrow Arremonops rufivirgatus OLSP LC × × × × Orange-fronted Parakeet Aratinga canicularis OFPA LC × × × × Pacific Screech-Owl Megascops cooperi PASO LC × × Pale-billed Woodpecker Campephilus guatemalensis PBIW LC × × × Plain Chachalaca Ortalis vetula PLCH LC × Plain Xenops Xenops minutus PLXE LC × × Red-billed Pigeon Patagioenas flavirostris RBPI LC × × × × Ruby-throated Archilochus colubris RTHU LC × × × Rufous-and-white Wren Thryothorus rufalbus RAWW LC × × × × Rufous-capped Warbler Basileuterus rufifrons RCWA LC × × × × Rufous-naped Wren Campylorhynchus rufinucha RNAW LC × × × × Scrub Euphonia Euphonia affinis SEUP LC × × × Slate-headed Tody-Flycatcher Poecilotriccus sylvia SHTF LC × × Social Flycatcher Myiozetetes similis SOFL LC × × Squirrel Cuckoo Piaya cayana SQCU LC × × × Steely-vented Hummingbird Amazilia saucerrottei SVHU LC × × Streak-backed Oriole Icterus pustulatus SBAO LC × × Streak-headed Woodcreeper Lepidocolaptes souleyetii SHWO LC × × × × Stripe-headed Sparrow Peucaea ruficauda SHSP LC × × × Stripe-throated Hermit Phaethornis striigularis SRTH LC × × × × Sulphur-bellied Flycatcher Myiodynastes luteiventris SBFL LC × × × Summer Piranga rubra SUTA LC × × × × Tennessee Warbler Oreothlypis peregrina TEWA LC × × × Thicket Tinamou Crypturellus cinnamomeus THTI LC × × × × Tropical plumbea TRGN LC × × × × Tropical Kingbird Tyrannus melancholicus TRKI LC × Turquoise-browed Motmot Eumomota superciliosa TBMO LC × × × × White-fronted Parrot Amazona albifrons WFPA LC × × × × White-lored Gnatcatcher Polioptila albiloris WLGN LC × × × × White-throated Magpie-Jay Calocitta formosa WTMJ LC × × × × White-tipped Dove Leptotila verreauxi WTDO LC × × × × White-winged Dove Zenaida asiatica WWDO LC × × × × Yellow Warbler Dendroica petechia YWAR LC × × × × Yellow-green Vireo Vireo flavoviridis YGVI LC × × × × Yellow-naped Parrot Amazona auropalliata YNPA EN × × × × Yellow-olive Flycatcher Tolmomyias sulphurescens YOFL LC × × × × Yellow-throated Euphonia Euphonia hirundinacea YTEU LC × × × × Yellow-throated Vireo Vireo flavifrons YTVI LC × × × ×

Forests aged 20 years had the highest species richness (56 species), followed by the 40 years class (52 species), 30 years (48 species), and 60 years (44 species). The only age class that reached the asymptote when analyzing species accumulation curves was 20 years (Figure4). When evaluating species richness at a finer scale by regressing forest structural characteristics (i.e., BA, PAI, CO, CH, LR, and NDVI) and the area of the sites within each age class against bird richness for each site using GLMM, CO explained 50% of the variation associated with bird species richness (AICc wi = 0.498; Table3, Figure5). As CO increased in forests, the number of bird species decreased (Figure5). The other forest structural characteristics, such as LAI, BA, CH, NDVI, liana ratio, and forest site area, were not important predictors of bird species richness (Table3). None of Forests 2020, 11, 629 8 of 13 the models, including additive effects or interactions, were important to explain variation in species Forests 2020, 11, x FOR PEER REVIEW 8 of 14 richnessForests 2020 (Table, 11,3 x). FOR PEER REVIEW 8 of 14

Figure 4. Bird species accumulation curves for the forest age classes. FigureFigure 4. 4.Bird Bird species species accumulationaccumulation curves curves for for the the forest forest age age classes. classes. Table 3. Generalized linear mixed models (Poisson error family) ranked according to the lowest AICc Table 3. Generalized linear mixed models (Poisson error family) ranked according to the lowest AICc Tablevalue explaining3. Generalized variation linear inmixed bird modelsspecies (Poissonrichness byerror forest family) structural ranked characteristics. according to the lowest AICc value explaining variation in bird species richness by forest structural characteristics. value explaining variation in bird species richness by forest structural characteristics. Models k AICc ΔAICc w Modelsspecies~COModels kk2 AICc95.203AICc ∆AICcΔ0.000AICc w0.498w species~COspecies~1species~CO (null) 22 1 95.20395.203 98.316 0.0000.000 3.113 0.4980.498 0.105 species~1species~1species~LAI (null) (null) 1 12 98.316 98.31698.883 3.113 3.1133.680 0.105 0.1050.079 species~LAIspecies~LAIspecies~area 222 98.88398.88398.930 3.6803.6803.727 0.0790.0790.077 species~area 2 98.930 3.727 0.077 species~CO+BAspecies~area 23 98.93099.780 3.7274.577 0.0770.050 species~CO + BA 3 99.780 4.577 0.050 species~CO+BAspecies~BA 32 100.18399.780 4.5774.980 0.0500.041 species~BA 2 100.183 4.980 0.041 species~COspecies~CO+LAIspecies~BA+ LAI 323 100.220100.183100.220 5.0174.9805.017 0.0410.0410.041 species~NDVImspecies~CO+LAIspecies~NDVIm 232 100.273100.220100.273 5.0705.0175.070 0.0390.0410.039 species~NDVImspecies~CHspecies~CH 222 100.283100.273100.283 5.0805.0705.080 0.0390.0390.039 species~NDVImspecies~NDVIm+COspecies~CH+ CO 323 100.550100.283100.550 5.3475.0805.347 0.0340.0390.034 species~NDVIm+COspecies~lianaspecies~liana 232 100.933100.550100.933 5.7305.3475.730 0.0280.0340.028 species~CO*BAspecies~CO*BAspecies~liana 323 100.940100.933100.940 5.7375.7305.737 0.0280.0280.028 species~CO*LAIspecies~CO*LAIspecies~CO*BA 333 102.080100.940102.080 6.8775.7376.877 0.0160.0280.016 species~CO + LAI + BA + CH + NDVIm + liana 7 119.090 23.887 0.000 species~CO+LAI+BA+CH+NDVIm+species~CO*LAI liana 37 102.080119.090 23.8876.877 0.0160.000 species~CO+LAI+BA+CH+NDVIm+liana 7 119.090 23.887 0.000

FigureFigure 5. Percentage 5. Percentage of canopy of canopy openness openness (log) explaining(log) explai variationning variation in bird speciesin bird richness.species richness. Confidence intervalsFigureConfidence at 5. 95% Percentage intervals shaded at area,of 95% canopyR shaded2 = 0.38. openness area, R2 = (log)0.38. explaining variation in bird species richness. Confidence intervals at 95% shaded area, R2 = 0.38.

Forests 2020,, 11,, x 629 FOR PEER REVIEW 99 of of 14 13

3.3. Bird Species Assemblage and Forest Age Class 3.3. Bird Species Assemblage and Forest Age Class Forest age was not a force driving bird species assemblage; instead, we observed a high overlap of speciesForest among age was different not a force age drivingclasses (Figure bird species 6). Se assemblage;veral species instead, that were we present observed in ayoung high overlapforests wereof species also present among in di ffoldererent successional age classes (Figurestages 6(Figur). Severale 6). Still, species some that species were presentshowed in a youngpreference forests for awere particular also present age class. in older For example, successional Oreothlypis stages(Figure peregrina6). (Wilson) Still, some (Tennessee species showed warbler) a preferencewas absent for in old-growtha particular forest age class. (60 years) For example,and TyrannusOreothlypis melancholicus peregrina (Vieillot)(Wilson) (tropical (Tennessee kingbird) warbler) was only was present absent inin young old-growth forests forest (20 (60years). years) Four and birdTyrannus species melancholicus only occurred(Vieillot) in older (tropical secondary kingbird) forests was such only as Micrasturpresent in youngsemitorquatus forests (20(Vieillot) years). Four(collared bird speciesforest-falcon) only occurred and Amazilia in older secondarysaucerrottei forests (Delattre such as& Bourcier)Micrastur (steely-vented semitorquatus (Vieillot) hummingbird) (collared in forest-falcon) 40–60-year-old and forestAmazilia sites, saucerrottei Ortalis vetula(Delattre (Wagler) & Bourcier) (plain chachalaca)(steely-vented in hummingbird)40 year old sites, in 40–60-year-oldand Myiodynastes forest luteiventris sites, Ortalis (Sclater) vetula (sulphur-bellied(Wagler) (plain chachalaca)flycatcher) in in 6040 year old sites. sites, and Myiodynastes luteiventris (Sclater) (sulphur-bellied flycatcher) in 60 year old sites.

FigureFigure 6. 6. NonmetricNonmetric Multidimensional Multidimensional Scaling Scaling (NMSD) (NMSD) of of bi birdrd species species presence presence at at the the different different forest forest ageage classes classes (20, 30, 40, 60 years old). nn == 1616 sites. sites. Forest Forest age age ordiellipses ordiellipses represent represent 95% 95% CI. CI. Ordiellipses Ordiellipses overlappingoverlapping show show non-differences non-differences on species compositions at different different forest ages. Species Species names names are are representedrepresented by four letters code (see Table 1 1 for for full full scientific scientific names). names). 4. Discussion 4. Discussion 4.1. Dry Forest Structural Characteristics and Forest Age 4.1. Dry Forest Structural Characteristics and Forest Age Similar structural characteristics were found among the different forest age classes, indicating highSimilar variation structural in structure characteristics within forests were offound a similar amon age.g the Similaritiesdifferent forest in forestage classes, structure indicating among highforests variation age classes in structure could be within for two forests reasons. of a similar First, forest age. Similarities regeneration in forest processes structure at the among SSR may forests not agebe a classes deterministic could be phenomenon, for two reasons. and theyFirst, showed forest regeneration a continuous processes re-growth at instead the SSR [52 may]. As not a result, be a deterministica continuous transitionphenomenon, is observed and they between showed age a classescontinuous in a successionalre-growth instead gradient. [52]. The As intensitya result, ofa continuouscattle ranching transition can influence is observed the successionalbetween age processclasses andin a thesuccessional forest recovery gradient. [2,3 The]. Hence, intensity forest of cattlerecovery ranching for sites can under influence intense the cattle successional ranching inproces the pasts and can the take forest longer recovery than forests [2,3]. withHence, moderate forest recoveryuse. However, for sites young under forests intense of 20cattl yearse ranching old tend in to the have past sites can with take morelonger open than canopies, forests with and moderate the oldest use.age classHowever, of 60 yearsyoung tend forests to have of 20 closed years canopies old tend and to high have values sites ofwith NDVI more and open PAI canopies, (Figure3). and Second, the oldesttree phenology age class of and 60 the years presence tend to of have leaves closed on treescanopies as the and proportion high values of deciduousof NDVI and trees PAI decreases (Figure 3).when Second, TDFs tree become phenology older [and25]. Sincethe presence this study of leaves was conducted on trees as during the proportion the peak ofof leafingdeciduous intensity, trees decreaseswhere trees when have TDFs full leaves become at theolder SSR [25]. [53 ],Since values this of study PAI and was NDVI conducted were high.during Consequently, the peak of leafing clearer intensity,differences where in PAI trees and NDVI have valuesfull leaves among at forest the classesSSR [53], may values not be of as noticeablePAI and NDVI during were this time high. of Consequently,the year as they clearer are observed differences during in thePAI peak and of NDVI deciduousness, values among particularly forest betweenclasses may young not and be oldas noticeableage classes. during In addition, this time since of this the study year addressedas they are for observed differences during in forest the structuralpeak of deciduousness, characteristics particularly between young and old age classes. In addition, since this study addressed for

Forests 2020, 11, 629 10 of 13 among forests that have recovered from cattle ranching, the number of forest sites per class was limited (i.e., small sample size) to a section of SSR where this activity intensively occurred in the past.

4.2. Bird Species Richness and Forest Age and Structural Characteristics Avian species richness was expected to increase as stands aged [11,14,23,24], since older forests offer a higher forest structural complexity to birds that would support more species. Instead, results showed that 20-year-old forests had the highest richness. Since forest age classes were poorly described by forest structure, 20-year-old stands presumably provide at least some of the resources needed by most species of birds. On the other hand, the species richness accumulation curve for this age class was the only one reaching an asymptote, suggesting that more bird species could be detected in the other age classes. When analyzing avian species richness and forest structure, a negative relationship was established between CO and bird species richness. High values of CO indicate forest sites with larger open canopies, and, therefore, with a low forest structural complexity (e.g., fewer or none nesting sites, breeding and roosting perches). We argue this translates into little or no availability of stems, twigs, and branches that birds use as perches, nesting and roosting sites, and structure for protection [54]. In addition, open habitats tend to have a lower richness and abundance of prey (e.g., arthropods), an important food source for species that inhabit the understory, than more structurally complex TDFs along a succession [55,56]. However, forest sites with open canopies were observed in each age class, indicating that openness is not necessarily driven by age.

4.3. Bird Assemblage, Forest Age and Forest Structural Characteristics Although noticeable differences in bird assemblage between forests 20 and 60 years old were expected due to bird species that specialize in conditions with different structural complexity, our results did not show this. The bird assemblage was similar among the different forest age classes, indicating that none of the classes host a specific assemblage of birds. The high variability in forest structural characteristics within each age class has contributed to increase the variance, causing an overlap in structure between ages that may have contributed to the similarity between ages in bird assemblage. Thus, using age to understand biodiversity recovery in TDFs may not be the best approach. Bird habitat models used in much of North America for forestry planning often utilize age as a key predictor for understanding changes in bird populations caused by different land-use strategies. Our results suggest this approach should be used cautiously in TDFs where the sources of human disturbance are quite different. This finding also suggests that young forests of 20 years old have reached similar species composition to 60-year-old forests at the SSR. This can be explained by both the proximity to and the transition between ages classes that allow birds to move easily between differently aged forest patches to look for different resources (e.g., perches, nesting sites, and food) [13,52]. Even though some species were associated with forests of a specific age, it is difficult to determine whether there is a real preference for these, since forests of different ages have quite similar structures that vary significantly within and between forests of the same age. However, given the limitations of the sampling design, such as a limited number of forest sites, the proximity between some sites, and to some sampling sites not located in the center of each forest age patch, these results should be taken with caution.

5. Conclusions TDFs can recover forest structural characteristics in a few decades, a similar pattern shown by other forests in the tropics, such as wet forests [6,56–58]. TDFs bird species assemblage recovery showed a similar pattern found for tropical wet forests, where several studies reported similar bird assemblages between 20–40 years old forests and older secondary forests [59]. Forest structural recovery is key for fauna recovery, and this study found that 20-year-old TDFs provide equally suitable habitat for birds as 60-year-old forests, suggesting a rapid recovery of bird species assemblage. The structural characteristics of forests are similar across all forest ages, suggesting that forests of all Forests 2020, 11, 629 11 of 13 ages are an important source of perches for roosting and mating displays, material for nesting, and a direct or indirect source of food. Having a similar forest structure among age classes is critical to achieving similar species composition. This study provides key information on the influence of TDF recovery after severe human impact on the local avifauna. This is relevant information for a highly threatened habitat that should be considered in bird conservation efforts for neotropical dry forests.

Author Contributions: Conceptualization, B.H. and A.S.-A.; Methodology, B.H., N.V.S.; Validation, B.H., N.V.S., E.M.B. and A.S.-A.; Formal Analysis, B.H. and N.V.S.; Investigation, B.H. and N.V.S.; Writing—Original Draft Preparation, B.H.; Writing—Review and Editing, B.H., N.V.S., E.M.B. and A.S.-A.; Funding Acquisition, B.H. and A.S.-A. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Science and Engineering Research Council of Canada (NSERC). This work was part of the research project TROPI-DRY (Human, Ecological and Biophysical Dimensions of Tropical Dry Forests), which is a collaborative research network funded by the Inter-American Institute for Global Change Research (IAI) Collaborative Research Network Program (CRN3-025) and supported by the US National Science Foundation (grant GEO 0452325). B.H. was funded by Ministerio de Ciencia, Tecnología y Telecomunicaciones (MICITT) Costa Rica, and Consejo Nacional para Investigaciones Científicas y Tecnológicas (CONICIT) Costa Rica. N.V.S. was funded by McAfee Estate Scholarship in Zoology. Acknowledgments: We thank three anonymous reviewers for comments and suggestions that greatly improved the manuscript. We thank Wendy Boone for her comments on the manuscript and language editing. We thank the personnel from SSR at ACG, the Center for Earth Observation Sciences (CEOS), Department of Earth and Atmospheric Sciences, University of Alberta, and the University of Alberta for logistical support. Conflicts of Interest: The authors declare no conflicts of interest.

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