DOI: 10.5061/dryad.msbcc2fvt
title: Trade-offs between biodiversity and agriculture are moving targets in dynamic landscapes

description: Understanding how biodiversity responds to intensifying agriculture is
 critical to mitigating the trade-offs between them. These trade-offs are
 particularly strong in tropical and subtropical deforestation frontiers,
 yet it remains unclear how changing landscape context in such frontiers
 alters agriculture-biodiversity trade-offs. We focus on the Argentinean
 Chaco, a global deforestation hotspot, to explore how landscape context
 shapes trade-off curves between agricultural intensity and avian
 biodiversity. We use a space-for-time approach and integrate a large field
 dataset of bird communities (197 species, 234 survey plots), three
 agricultural intensity metrics (meat yield, energy yield and profit), and
 a range of environmental covariates in a hierarchical Bayesian occupancy
 framework. Woodland extent in the landscape consistently determines how
 individual bird species, and the bird community as a whole, respond to
 agricultural intensity. Many species switch in their fundamental response,
 from decreasing occupancy with increased agricultural intensity when
 woodland extent in the landscape is low (loser species), to increasing
 occupancy with increased agricultural intensity when woodland extent is
 high (winner species). This suggests that landscape context strongly
 mediates who wins and loses along agricultural intensity gradients.
 Likewise, where landscapes change, such as in deforestation frontiers, the
 very nature of the agriculture-biodiversity trade-offs can change as
 landscapes transformation progresses. Synthesis and applications. Schemes
 to mitigate agriculture-biodiversity trade-offs, such as land sparing or
 sharing, must consider landscape context. Strategies that are identified
 based on a snapshot of data risk failure in dynamic landscapes,
 particularly where agricultural expansion continues to reduce natural
 habitats. Rather than a single, fixed strategy, adaptive management of
 agriculture-biodiversity trade-offs is needed in such situations. Here we
 provide a toolset for considering changing landscape contexts when
 exploring such trade-offs. This can help to better align agriculture and
 biodiversity in tropical and subtropical deforestation frontiers.
description: <b><i>Bird
 sampling</i></b> We combined data from three
 extensive bird surveys in the study region (Decarre, 2015; Macchi et al.,
 2013; Mastrangelo & Gavin, 2012, Fig. 1). Birds were surveyed at
 234 sites between 2009 and 2013 (see Appendix
 S1 in supporting
 Information). Each site was sampled using point counts, where
 all individual birds were identified to the species level. Sites were
 arranged along a gradient of remaining natural vegetation. This allowed us
 to apply a space-for-time approach in which landscapes with different
 shares of remaining natural vegetation can be interpreted as temporal
 stages along a deforestation trajectory. This is justified for the Chaco,
 as the region was dominated by natural vegetation until the 1990s, but has
 since turned into a global deforestation hotspot (Baumann et al., 2017;
 Kuemmerle et al., 2017). Bird sampling sites were placed in
 all the main land systems in the region: 79 were located in natural
 woodlands, 16 in natural grasslands, 32 in subsistence ranching systems,
 39 in silvopastoral systems, 27 in pasture systems, and 41 in croplands.
 Sites located in the same type of land system were spatially separated by
 at least 0.5 km (Fig. S1). Sampled land systems were distributed across
 the study area, and despite some were clustered regionally, land systems
 were generally well interspersed (see Methods in Decarre, 2015; Macchi et
 al., 2013; Mastrangelo & Gavin, 2012). The unbalanced number of
 sample units roughly represents the distribution of these land systems in
 the Dry Chaco (Baumann et al., 2017). It is important to note that
 currently in the Dry Chaco natural grasslands are very scarce and only a
 few species occurred exclusively there in our study. Bird assemblages of
 woodlands and natural grasslands were thus considered as baseline
 communities, as they both share the condition of being largely undisturbed
 by human activities. Species accumulation curves suggest that all land
 systems and their associated bird assemblages were adequately represented
 in our dataset (Fig. S2).   <b><i>Agricultural
 intensity metrics</i></b> Adequately addressing agricultural
 intensity and its impact on the environment requires considering the
 different dimensions of intensification (Kehoe et al., 2017). We measured
 agricultural intensity at all bird sampling sites using three metrics (1)
 <i>meat yield</i> [kg / ha*year]; (2) <i>energy
 yield</i> [GJ / ha*year], and (3) <i>profit</i> [United
 State dollars USD / ha*year]. While meat and energy yields are descriptors
 of agricultural production, profit is the variable more directly related
 to the farmers’ expectations and decision making (Jobbágy & Sala,
 2014). We
 collected data on forage production along rainfall gradients for each land
 system, and estimated <i>meat yield</i> (secondary production)
 considering parameters of livestock systems destined to meat production in
 the Dry Chaco (Murray, Baldi, von Bernard, Viglizzo, & Jobbágy,
 2016). In the case of croplands, we considered soybean production, which
 was transformed to pork live weight [kg / ha*year] using a specific 5:1
 conversion ratio (Smil, 2013). Next, we converted meat from livestock
 systems and grain from croplands into <i>energy yield
 </i>following standard conversion metrics (USDA, 2011). Finally, we
 applied a net-return econometric model (Murray et al., 2016) to translate
 meat production per land system to <i>profit</i>, considering
 the corresponding production and transportation costs related to each land
 system (for a full description of the estimation of the agricultural
 intensity metrics see Appendix S2). The agricultural intensity
 gradient showed marked differences in the yield values when production
 increased from natural to intermediate and on to highly-intensified
 production systems. For all the intensity metrics, croplands were the
 highest-yielding system (mean values of <i>meat</i>: 260.2 ±
 51.3 standard deviation (SD) [kg / ha*year]; <i>energy</i>:
 33.7 ± 19.6 SD [GJ / ha*year]; and <i>profit</i>: 189.9 ± 55.2
 SD [USD / ha*year]), followed by silvopasture and pasture, and lastly the
 subsistence ranching system. Natural woodland and grassland without
 livestock were assumed to have zero yields in all metrics (Fig. 1 and
 Table S1).   <b><i>Landscape
 composition and environmental conditions</i></b>
 We used covariates that
 reflected variation in landscape composition and environmental conditions
 within our study region. Considering the importance of habitat
 availability for determining species’ occurrence (Fahrig, 2013), we
 calculated the <i>woodland extent</i> within buffers of 6 km
 and 10 km around each sampling site (i.e. landscape spatial scale of c.100
 km<sup>2</sup> and c.320 km<sup>2</sup>
 respectively). To do this, we used forest cover maps from the Global
 Forest Change datasets (30 m resolution, Hansen et al., 2013) for the bird
 sampling corresponding year. Water availability is one of the
 main environmental constraints for agriculture in the Chaco (Houspanossian
 et al., 2016). Thus, we calculated mean annual rainfall (hereafter:
 <i>rainfall</i>) and an aridity index (hereafter:
 <i>aridity</i>) for all bird sampling sites based on weather
 stations data by the National Institute of Agricultural Technology (INTA).
 We interpolated mean annual rainfall using geo-statistics (semi-variograms
 and kriging). The aridity index was calculated dividing rainfall by
 evapotranspiration. Our covariates were generally only weakly correlated
 (Fig. S3). <b><i>Occupancy
 modelling</i></b> To assess the trade-off between
 avian biodiversity and agricultural intensity, we fitted trade-off curves
 between the two (Phalan et al., 2011). As a proxy for biodiversity, we
 estimated the probability of occupancy per species using a Bayesian
 framework (MacKenzie et al., 2006). Yet, occupancy models do not account
 for species’ abundance as occupancy is solely based on whether or not a
 species is detected at a site. Occupancy models have some important
 advantages in this context. First, occupancy models account for imperfect
 detection, as some bird species may be more common or easier to detect
 than others. Second, occupancy models control for different sampling
 effort, observer identity, and sampling period among surveys as part of
 the detectability model (MacKenzie et al., 2006). Third, occupancy models
 allow for the consideration of multiple covariates when assessing the
 biodiversity vs. agricultural intensity relationship (Kéry &
 Royle, 2016). Finally, a Bayesian modelling framework allows for the
 simultaneous assessment of agriculture-biodiversity curves for the entire
 community and for each individual species. We employed multi-species
 occupancy models under a Bayesian framework (Kéry & Royle, 2016).
 To build the detection history, we used spatial replicates per site (9 for
 Macchi et al., 2013, 6 for Mastrangelo & Gavin, 2012, and 4 for
 Decarre, 2015). The detectability model included the covariates
 <i>survey </i>(i.e., data source)<i>,</i> and
 <i>openness </i>(i.e., categorical variable for the habitat
 type at the sampling site: woodland or open vegetation). We assessed
 <i>bird occupancy </i>as the response variable with seven
 possible covariates: three agricultural intensity metrics (<i>meat
 yield, energy yield,</i> or <i>profit</i>), two
 landscape composition covariates (<i>woodland extent </i>for 6
 km or 10 km buffers)<i>, </i>and two environmental
 covariates<i> </i>(<i>rainfall </i>or
 <i>aridity</i>). In addition, each model included the
 interaction between the respective agricultural intensity metric and
 either <i>woodland extent</i>, <i>rainfall</i> or
 <i>aridity</i>, leading to a total of 24 different model
 combinations (Table S2). The interaction term allowed us to understand how
 the response of bird occupancy to agricultural intensity (e.g.
 <i>meat</i> <i>yield</i>) varied in relation to
 our environmental covariates (e.g. <i>woodland</i>
 <i>extent</i>). We fitted the models using only uncorrelated
 covariates (Fig. S3), and considered a covariate to have a strong effect
 when the 95% credible interval of the parameter estimate (CRI) did not
 overlap zero. We compared all models using Watanabe-Akaike information
 criterion (WAIC; Watanabe, 2010), which is a useful model selection
 criterion for hierarchical models (Broms, Hooten, & Fitzpatrick,
 2016). Once we identified the best fitting model for the entire bird
 community, we examined this model in detail regarding responses at the
 species level. Specifically, we assessed the response curves for the 37
 most common bird species (naïve occupancy of at least 10%) that showed a
 strong effect of the <i>meat yield*woodland extent
 </i>interaction. More details on the occupancy modelling are
 provided in the Supporting Information, Appendix S3.