# Habitat fragmentation matters more than habitat loss: the case of host–parasite interactions.

Antoine Perrin, Aurélie Khimoun, Anthony Ollivier, Yves Richard, Antón Pérez-Rodríguez, Bruno Faivre, Stéphane Garnier

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Antoine Perrin, Aurélie Khimoun, Anthony Ollivier, Yves Richard, Antón Pérez-Rodríguez, et al. Habitat fragmentation matters more than habitat loss: the case of host–parasite interactions. Molecular Ecology, 2023, 32 (4), pp.951-969. 10.1111/mec.16807. hal-04067514

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MOLECULAR ECOLOGY

Habitat fragmentation matters more than habitat loss: the case of host-parasite interactions

<table><tr><td>Journal:</td><td>Molecular Ecology</td></tr><tr><td>Manuscript ID</td><td>Draft</td></tr><tr><td>Manuscript Type:</td><td>Original Article</td></tr><tr><td>Date Submitted by the Author:</td><td>n/a</td></tr><tr><td>Complete List of Authors:</td><td>Perrin, Antoine; Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté<br/>Khimoun, Aurélie; Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté<br/>Ollivier, Anthony; Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté<br/>Richard, Yves; Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté<br/>Pérez-Rodríguez, Antón; Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté<br/>Faivre, Bruno; Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté<br/>Garnier, Stéphane; Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté</td></tr><tr><td>Keywords:</td><td>Landscape change, tropical forest, habitat amount, spatial configuration, meteorological conditions, avian malaria</td></tr></table>
1 Habitat fragmentation matters more than habitat loss: the case of host-parasite

2 interactions

3

4 Running Title: Habitat fragmentation and biotic interactions

5

6 Antoine Perrin¹, Aurélie Khimoun¹, Anthony Ollivier¹, Yves Richard¹, Antón Pérez-

7 Rodríguez¹, Bruno Faivre¹, Stéphane Garnier¹

8

9 ¹Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté, 6 Boulevard

10 Gabriel, 21000 Dijon, France

11

12 Corresponding author: Antoine Perrin,

13 UMR CNRS 6282-Biogéosciences,

14 6 bd Gabriel, 21000 Dijon, France

15 +33 635467978

16 perrin-antoine1@hotmail.fr
18

19 ABSTRACT

20

21 Ecologists agree that habitat loss has a large and negative effect on biodiversity. However,
22 whether or not habitat fragmentation has a lower effect and whether this effect is positive or
23 negative for biodiversity is still highly debated. Here, we assessed the relative influence of
24 tropical forest loss and fragmentation on the prevalence of vector-borne blood parasites of the
25 genera *Plasmodium* and *Haemoproteus* in six forest bird species. We also determined whether
26 these processes were associated with an increase or a decrease of prevalence. We sampled
27 more than 4,000 individual birds from 58 forest sites in Guadeloupe and Martinique.

28 Considering 34 host-parasite combinations independently and a fine characterization of
29 habitat amount and its spatial configuration, we used partial least square regressions to
30 disentangle the relative effects of forest loss, forest fragmentation, landscape heterogeneity
31 and local weather conditions on spatial variability of parasite prevalence. Then we tested for
32 the magnitude and the sign of the effect of each environmental descriptor. Strikingly, we
33 showed that forest fragmentation explains twice as much proportion of the variance in
34 prevalence as habitat loss or landscape heterogeneity. Besides, habitat fragmentation led to a
35 global increase in prevalence in Guadeloupe but its effect was variable in Martinique. Both
36 habitat loss and landscape heterogeneity showed taxon-specific effects. Our results suggest
37 that habitat loss and fragmentation may have contrasting effect between tropical and
38 temperate regions and that inter-specific interactions may not respond the same way as more
39 commonly used biodiversity metrics such as abundance and diversity.

40

41 KEYWORDS
42 Landscape change, tropical forest, habitat amount, spatial configuration, meteorological
43 conditions, avian malaria

44

45 INTRODUCTION

46

47 Most of the earth's ecosystems have been deeply modified by human activities, resulting in a
48 consistent decline of biodiversity at various scales and disturbance of ecosystem functioning
49 that are predicted to continue (Foley et al., 2005; Haddad et al., 2015; Newbold et al., 2016;
50 Pereira et al., 2010). This observation has motivated massive research on the effects of land
51 use changes on biodiversity and on the ways to control these effects. In this context, habitat
52 loss and fragmentation are core processes that became major topics in ecology and
53 conservation (Fardila, Kelly, Moore, & McCarthy, 2017; Hadley & Betts, 2016). It is widely
54 accepted that habitat loss (i.e., a decrease in a habitat area) has to be distinguished from
55 habitat fragmentation (i.e., the breaking apart of habitat leading for instance to higher number
56 of patches, more isolated patches, and lengthier edges), especially since the seminal paper
57 from Fahrig (2003). Although literature highlights a consensual picture depicting habitat loss
58 as a main cause of biodiversity erosion (Betts et al., 2017; Fahrig, 2003; W. F. Laurance,
59 2010; Pimm & Raven, 2000), the effect of habitat fragmentation remains a controversial
60 question that generated intense and stimulating debates (Miller-Rushing et al., 2019). Whether
61 or not habitat fragmentation has a negligible effect compared with habitat loss and whether
62 habitat fragmentation has a positive or negative effect on biodiversity are the main questions
tackled by numerous studies, especially in recent inspiring syntheses or meta-analyses
63 (Didham, Kapos, & Ewers, 2012; Fahrig, 2013, 2017; Fahrig et al., 2019; Fletcher et al.,
64 2018; Haddad et al., 2015; Saura, 2021).

65
Isolating independent effects of habitat loss and habitat fragmentation is a challenging purpose that may be pointless if these effects are in fact interdependent (Didham et al., 2012). Yet, discerning the relative effects of these two processes is a critical issue for at least two reasons. First, separating the effects of these two processes is crucial to identify and quantify the underlying mechanisms threatening species and ecosystems (Lindenmayer & Fischer, 2007). Second, it is helpful for decision makers to prioritize protection and management strategies of lands exposed to human pressures (Miller-Rushing et al., 2019). For instance, how much fragmentation affects biodiversity independently from habitat loss determines our ability to mitigate the negative consequences of habitat loss by manipulating spatial configuration of the remaining habitat patches. This is important because land managers have the capacity to alter both habitat area and its spatial configuration, and because such strategies are already implemented in environmental policies in many countries (Fahrig, 2020; Hadley & Betts, 2016).

So far, the effects of habitat loss and fragmentation have been mostly assessed from species distribution, as more than half of the studies used measures of abundance, occurrence or changes in range size (Fardila et al., 2017). It is striking that biotic interactions are the least studied biological response, though they are frequently involved in explanations offered by the authors reporting significant effects of habitat fragmentation (Fahrig, 2017; Fardila et al., 2017). In addition, both theoretical consideration and empirical studies suggested that considering interactions between species is a more powerful approach to study how environmental changes drive ecological communities than considering species composition (Bordes et al., 2015; Poisot, Guéveneux-Julien, Fortin, Gravel, & Legendre, 2017; Sabatino, Maceira, & Aizen, 2010; Tylianakis, Tscharntke, & Lewis, 2007).

Parasites are a major component of ecological communities comprising approximately 40% of the species described worldwide (Dobson, Lafferty, Kuris, Hechinger, & Jetz, 2008),
and more than 75% of links in natural food webs probably involve parasites (Lafferty, Dobson, & Kuris, 2006). Parasites can regulate hosts population by increasing their mortality and reducing their fecundity (Hudson, Rizzoli, Grenfell, Heesterbeek, & Dobson, 2002; Thomas, Renaud, & Guégan, 2005). Hence, they may exacerbate the threat posed to species that are of conservation concern because these species often suffer from chronic stress due to resource limitations or reduced genetic diversity (Collinge, 2009). In addition, numerous studies revealed that anthropogenic landscape changes can affect host-pathogen interactions and lead to emergence of infectious diseases in humans, domestic animals, and wildlife (Despommier, Ellis, & Wilcox, 2007; Gibb et al., 2020; Morand & Lajaunie, 2018; Patz, Graczyk, Geller, & Vittor, 2000). Although landscape changes have complex and variable effects on disease dynamics, the main trend described is an increase in pathogen transmission and prevalence associated with habitat loss and fragmentation, agricultural development, irrigation and urbanization (Brearley et al., 2013; Gottdenker, Streicker, Faust, & Carroll, 2014; White, 2015). However, very few studies have attempted to assess the relative effects of habitat loss and fragmentation on wildlife diseases, while this issue is critical for both conserving wildlife and anticipating emergence of infectious diseases in human-modified landscapes (Brearley et al., 2013).

Forest loss and fragmentation are a global concern because 70% of the remaining forest in the world lies within 1 km of the forest edge (Haddad et al., 2015). While tropical forests are often biodiversity hotspots, they have been suffering from an increasing rate of loss and fragmentation for a few decades, and this trend is expected to continue (Hansen et al., 2013; W. F. Laurance, 2010; Millennium Ecosystem Assessment, 2005; Taubert et al., 2018). In this study, we assessed the relative effects of tropical forest loss and fragmentation on haemosporidian parasite prevalence in several bird species, and we tested whether these two processes were associated with an increase or a decrease in prevalence. Haemosporidian
parasites from the genus *Plasmodium* and *Haemoproteus* are among the most studied vector-borne diseases in wildlife, as avian malaria is a key model in the development of knowledge of human malaria parasites (Rivero & Gandon, 2018) and has deleterious effects on survival and reproduction of birds (Knowles, Palinauskas, & Sheldon, 2010; Krams et al., 2013; Martínez-de la Puente et al., 2010; Marzal, Lope, Navarro, & Møller, 2005).

We aimed to characterize a global response of parasite prevalence to forest loss and fragmentation over multiple host-parasite interactions (i.e., combinations of one avian host species and one haemosporidian lineage) in two islands corresponding to similar environmental contexts. Hardly any study dealing with the effect of "habitat fragmentation" in the broad sense on host-parasite interactions isolated the effect of habitat loss and habitat fragmentation. In addition, the majority of studies rely on coarse description of landscapes [e.g., altered/non altered habitat or landscape, use of a single metric to quantify either habitat amount or its spatial configuration (Brearley et al., 2013; Fardila et al., 2017)]. Here, we assessed variation in prevalence in various landscapes that we described in detail using a series of landscape metrics (Pérez-Rodríguez et al., 2018). This approach allowed the statistical control for forest amount before assessing the role of its spatial configuration, as only a precise documenting of such pattern may allow further studies to elucidate ecological mechanisms producing this pattern (Fahrig et al., 2019; Haddad et al., 2015).

In this study, we assessed how spatial variation in parasite prevalence is shaped by landscape composition and configuration. More specifically, we compared the relative influence of (i) landscape structure and other environmental factors (meteorology and topography) known to affect our host-parasite system, and (ii) forest loss and forest fragmentation. Then, we tested whether each component of landscape structure has an overall positive or negative effect on parasite prevalence.

140
141 MATERIALS AND METHODS

142

143 Bird species studied and field sampling

144 Haemosporidian parasites were screened in six bird species. Four species are common in
145 Guadeloupe and Martinique (the Bananaquit, *Coereba flaveola*, the Lesser Antillean
146 bullfinch, *Loxigilla noctis*, the Caribbean Elaenia, *Elaenia martinica*, the Black-whiskered
147 Vireo, *Vireo altiloquus*) and two species occur in Guadeloupe only (the Forest Thrush, *Turdus
148 lherminieri* and the Plumbeous Warbler *Setophaga plumbea*). Although they are all forest
149 species, their ecological niches are a bit different (del Hoyo, Elliott, Sargatal, Christie, & de
150 Juana, 2014; Khimoun et al., 2016). The Bananaquit occurs on a wide range of habitats such
151 as forests, shrublands and vegetated human environments, either dry or humid, although it
152 tends to be scarce or absent in extensive humid forest (Hilty & Christie, 2013). The Lesser
153 Antillean bullfinch, the Caribbean Elaenia and the Black-whiskered Vireo are almost
154 ubiquitous as they are found in woodland of most types, including tall undergrowth and
155 vegetated gardens (Brewer, 2010; Hosner, 2004; Rising & Jaramillo, 2011). The Forest
156 Thrush and the Plumbeous Warbler are the most specialized species as they occur mainly in
157 primary and secondary forests (Collar, 2005; Curson, 2010; Khimoun et al., 2016).

158 Guadeloupe and Martinique are rather small islands (1,450 km² and 1,128 km²,
159 respectively) located in the Lesser Antilles. Guadeloupe consisting of two zones, Basse-Terre
160 on the west side (BT, about 850 km²) and Grande-Terre on the east side (GT, 600 km²),
161 connected by an urbanized isthmus (Figure 1). Forest is covering about half of these islands
162 (IGN, 2018), but there is substantial spatial heterogeneity (Figure 1). For instance, 60% of BT
163 is covered by a forest which is mostly continuously distributed, whereas only 22% of GT is
164 covered by forest (IGN, 2014). In Martinique, large forest patches cover the mountainous area
165 in the north whereas forest fragments of various sizes are scattered in lowlands in the south.
Guadeloupe and Martinique belong to the most populated islands of the Lesser Antilles (Desse, 2004), and anthropogenic activities have been the main source of forest loss and fragmentation in these islands, especially during the 18th and 19th century because of the development of sugar cane and banana plantations (Conseil Régional de Guadeloupe, 2013; Parc Naturel Régional de la Martinique, 2012). Although the forest covers regained ground at some places during the 20th century, the balance remains negative mainly because of the development of agriculture and urbanization (IGN, 2014). However, the pace of deforestation has decreased in the last two decades (IGN 2014).

Field sampling occurred between March and July from 2012 to 2014. We captured birds with mist nets in 36 forest sites in Guadeloupe and 22 in Martinique covering varied environmental conditions (according to weather and landscape structure; Figure 1). Each bird was ringed and about 20µl of blood was collected from the brachial vein using sterile needles and heparinized tubes. Blood samples were stored in 500µl of QLB (Queen's Lysis Buffer, Seutin, White, & Boag, 1991) until molecular analyses.

## 180 Characterization of environmental conditions

### 182 Landscape structure

We followed the approach described in Pérez-Rodríguez et al. (2018) to quantify the landscape (especially the forest cover) structure, though we adopted a slightly different way of using it in the analyses. Using FRAGSTATS 4.2 (McGarigal & Marks, 1995), we calculated 14 structural metrics from the CORINE land cover 2012 raster map (http://www.statistiques.developpement-durable.gouv.fr), encompassing 11 different land categories (i.e., habitats; see Table 1 and Supplementary Material Table A1 in Pérez-Rodríguez et al., 2018). These metrics describe the landscape structure resulting from forest loss and fragmentation at three levels: patch (i.e., the forest fragment in which birds were
sampled), class (i.e., the set of forest patches within a given buffer area), and landscape (i.e., the set of patches of all land categories within a given buffer area). For class- and landscape-level metrics, we considered different scales of effect using three buffer sizes (500, 1000 and 2000 m of radius) around each sampling site to take into account the range of potential dispersal abilities (between several hundred meters and a few kilometers) of the malaria vectors active in the study area (Lapointe, 2008; Sedda et al., 2012). Finally, metrics at the patch and class levels were calculated both before and after removing an arbitrary 50 m-wide outer fringe of the forest fragment to consider total and core forest respectively. At the end, we obtained 54 habitat structure variables.

We classified all patch and class metrics *a priori* as measures of either *forest amount* or *forest spatial configuration* (respectively *HL* and *F* in Table 1 in Pérez-Rodríguez et al., 2018). These two sets of metrics reflect the result of forest loss and forest fragmentation respectively, because these islands were previously entirely covered by forest (Joseph, 2006). The number of metrics in these two sets were heterogeneous as *forest amount* and *forest spatial configuration* were described by 8 and 28 variables respectively. Indeed, spatial configuration of a given habitat is much more complex to quantify than its area, and we aimed to capture its different dimensions.

The correlation frequently observed between metrics of habitat amount and configuration may limit our ability to assess the relative influence of habitat loss and fragmentation (Fahrig, 2003; McGarigal & Cushman, 2002). Therefore, we used the residuals of the linear regression of each configuration metric on the corresponding forest amount metric in the subsequent analyses, in order to control for the effect of forest amount on forest spatial configuration. By "corresponding forest amount metric", we mean patch area and class area for configuration metrics calculated at the patch and class levels respectively, using the
same buffer size and either total or core forest. For example, the number of core forest patches in a buffer with a radius of 500m was corrected by the total area of core forest in this buffer.

In addition to these two sets of metrics describing forest amount and forest configuration, we included a third set of metrics (hereafter called *landscape features*) measured at the landscape level that could not be considered as reflecting either forest loss or fragmentation because they rely on all land categories within a given buffer. Nevertheless, they are associated with the structural heterogeneity of the landscape, and considering these metrics allows to take into account the critical effect of the matrix composition and configuration (Arroyo-Rodríguez et al., 2020; Fahrig, 2013; Fletcher et al., 2018).

*Meteorological conditions*

Although several studies showed that avian haemosporidian prevalence is associated with temperature and precipitation, they often rely on climatic condition descriptors (Cornuault et al., 2013; Loiseau et al., 2013; Pérez-Rodríguez, Fernández-González, Hera, & Pérez-Tris, 2013; Sehgal et al., 2011). However, prevalence of these parasites is influenced by the abundance of the insect vectors (Martínez-Abraín, Esparza, & Oro, 2004), which in turn depends more on meteorological events than long-term climatic trends (Ruiz et al., 2010). Therefore, we produced meteorological indicators for each sampling site.

Temperature and rainfall data were extracted from time series provided by the Météo-France Station Network (MFSN) at a daily scale, for the period 2012-2014. Rainfall values were available for 132 raingauges, whereas minimal and maximal temperatures were available for 72 stations. However, there were many series with missing values, and only those with at least 1000 values over the 1096 days (i.e., the three sampling years) were retained, so that this study is based on 61 rainfall stations in Guadeloupe, and 53 in Martinique, as well as 15 thermal stations in Guadeloupe, and 27 in Martinique. Four
240 indicators were calculated from minimal and maximal daily temperatures and fifteen
241 indicators were calculated from rainfall daily values (Supplementary Material S1). Then, a
242 continuous layer for each indicator was produced at a resolution of 250m using an
243 interpolation method base on the influence of topographical factors (Castel, Lecomte,
244 Richard, Lejeune-Hénaut, & Larmure, 2017; Joly, Bois, & Zaksek, 2012). This method
245 combines a multiple regression, then a kriging of the residues, and it jointly uses the spatial
246 autocorrelation of the indicators and their correlation with both geographical (latitude and
247 longitude) and relief (altitude) variables. These three topographical descriptors were extracted
248 from the 250 m resolution IGN (Institut Géographique National) digital terrain model.
249 Finally, we calculated the average of each meteorological indicator in each buffer with the
250 software QGIS. We split meteorological indicators in two groups reflecting rainfall and
251 temperature.
252

252

253 Laboratory analysis

254 Total DNA extraction was performed on 240µl of QLB-stored blood, according to a standard
255 phenol-chloroform protocol optimized from Hillis et al. (1996), initiated by a first step of
256 digestion with 10µl of proteinase K (overnight incubation at 55°C). We run a nested PCR
257 protocol to detect haemosporidian parasites from genus *Plasmodium* and *Haemoproteus*
258 (Waldenström, Bensch, Hasselquist, & Ostman, 2004). This PCR amplified a 479 bp fragment
259 of the parasite cytochrome b gene. Negative or ambiguous results on 2% agarose gels were
260 confirmed with a second PCR run. Positive samples were sequenced on an ABI Prism 3730
261 capillary robot (Applied Biosystems, USA), using the forward primer HaemF. A second
262 sequence using the primer HaemR2 was generated when clear editing of the first sequence
263 was not possible for the whole sequence. Sequences were edited manually using Geneious
264 8.1.3 (Kearse et al., 2012) and all retrieved haplotypes were identified according to the
265 MalAvi database (Bensch et al., 2009). We added the new sequences to the database.

266

267 Statistical analysis

268 We used Partial Least Squares Regression (PLSR) to test for the effect of environmental
269 conditions on parasite prevalence. PLSR is a convenient analysis to deal with numerous
270 predictors which are potentially collinear (Carrascal, Galván, & Gordo, 2009). Contrary to the
271 principal component analysis, which produces factors that maximize the explained variance
272 across the predictors alone, the PLS analysis forced the factors to maximize the explained
273 variance of the response variable (Carrascal et al., 2009). To take into account a potential
274 effect of topography, we included latitude, longitude and altitude in the analyses. Hence,
275 analyses considered the six following sets of predictors: forest amount, forest spatial
276 configuration, landscape features, rainfall, temperature, and topography. We wanted to
277 assess the relative influence of these types of predictors on parasite prevalence. As the number
278 of predictors in each group was very heterogeneous (between 3 and 45; Supplementary
279 Material S1; Table 1 in Pérez-Rodríguez et al., 2018), we first performed a PLSR using
280 metrics within each set independently. We retained the first factor of each of these six PLSR
281 to produce a single synthetic predictor for each type of environmental condition maximizing
282 the covariance between this predictor and the prevalence. Then, we used these six synthetic
283 predictors in a PLSR that also included the proportion of male in each site, to control for the
284 effect of the sex-ratio on the haemosporidian prevalence (Illera, López, García-Padilla, &
285 Moreno, 2017). We calculated the predictive power (Q2) and the explicative power (R²) of
286 each PLSR. We considered the predictive power of PLSR insufficient if Q2 < 0.0975 (Abdi,
287 Esposito-Vinzi, Russolillo, Saporta, & Trinchera, 2016). For each host species, we only used
288 sites with a sample of at least 10 individuals and we only used parasite lineages with a global
prevalence higher than 2,5% in a territory (Table 1). All analyzes were performed with the R package *plsdepot* (Sanchez, 2012) independently for each of the 34 host-parasite-territory combinations that were retained.

We compared the relative explanatory capacity of the environmental synthetic predictors using mixed-effects models comparisons with the R package *lme4* (Bates, Mächler, Bolker, & Walker, 2015). We considered each PLSR (i.e., host-parasite-territory combination) as a statistical individual. The dependent variable was the explanatory capacity associated with a synthetic predictor (or group of predictors by summing corresponding explanatory capacities), calculated as the product of the percentage of variance explained by the first factor of the PLSR by the square weight of the corresponding predictor (measuring its relative contribution to this first factor; Carrascal et al., 2009). We included territory (Guadeloupe or Martinique), environmental synthetic predictor and their interaction as fixed effects in our model. We also included three random effects (avian host species, parasite lineage and host-parasite-territory combination) to take into account the non-independence of explanatory capacity from the same host or parasites species and from the same host-parasite-territory combination. We used this approach to compare the relative influence of 1) landscape structure *versus* meteorology and topography (grouping environmental synthetic predictors in two levels: *forest amount, forest spatial configuration and landscape features* on one side and *rainfall, temperature and topography* on the other side), and 2) the three components of landscape structure (considering the three sets *forest amount, forest spatial configuration and landscape features*).

Finally, we used mixed-effect models to test whether each component of landscape structure has an overall positive or negative effect on parasite prevalence (over the 34 host-parasite-territory combinations), taking into account the magnitude of this effect. The dependent variable was the explanatory capacity (measuring the magnitude of the effect)
associated with the considered synthetic predictor described previously, and we allocated a positive or negative sign to it, according to whether the synthetic predictor mainly depicted a positive or negative effect of habitat loss, habitat fragmentation or landscape heterogeneity on parasite prevalence (Pérez-Rodríguez et al., 2018). Our three models (one per component of landscape structure) included one fixed effect (territory) and two random effects (avian host species and parasite lineage).

## 321 RESULTS

### 322 Parasite prevalence variation

The number of birds sampled ranged from 284 to 563 (from 20 to 31 sampling sites), depending on bird species and territory (Table 1; Supplementary Material S2). Parasite screening was achieved in 4222 individual birds. The global prevalence at the territory scale was heterogeneous among the six species: 79% in Guadeloupe and 56% in Martinique for the Bananaquit; 68% for the Forest Thrush; 62% in Guadeloupe and 47% in Martinique for the Black-whiskered Vireo; 36% in Guadeloupe and 78% in Martinique for the Lesser Antillean bullfinch; 36% for the Plumbeous Warbler and 6% in Guadeloupe and 8% in Martinique for the Caribbean Elaenia. For each host species, there was a substantial variation in prevalence among sampling sites. This prevalence ranged between 17% and 100% in the Bananaquit, 23% and 95% in the Forest Thrush, 30% and 90% in the Black-whiskered Vireo, 3% and 86% in the Plumbeous Warbler, 4% and 97% in the Lesser Antillean bullfinch, and 0% and 28% in the Caribbean Elaenia. Furthermore, parasite communities were heterogeneous among bird species (Table 1). As a whole, we detected 46 parasite lineages (23 lineages of *Haemoproteus* sp. and 23 lineages of *Plasmodium* sp.), and the number of lineages per host species ranged from nine in the Caribbean Elaenia and the Lesser Antillean bullfinch to 25 in the Black-
whiskered Vireo. Only 12 haemosporidian lineages were shared by at least two host species (Table 1).

340

341 Relative explanatory capacity of the environmental synthetic predictors

All the 34 PLSR produced a single significant PLS factor with sufficient predictive capacity according to Abdi et al. (2016). This first factor explained between 27% and 85% of the spatial variability of prevalence depending on host-parasite-territory combination (Figure 2).

Landscape structure explained between 13 and 50% of variation in parasite prevalence, while meteorology and topography explained four to 49% of variation in parasite prevalence. Concerning meteorology, we did not find any clear effect (in terms of either magnitude or sign) of a particular predictor of rainfall or temperature. The explanatory capacity of prevalence variations was higher for landscape structure than for meteorology and topography ($\chi^2 = 7.38, ddl = 1, p = 0.007$; Figure 3). There was no interaction between the type of synthetic predictor and territory ($\chi^2 = 0.002, ddl = 1, p = 0.97$), and this explanatory capacity was not different between Guadeloupe and Martinique ($\chi^2 = 0.81, ddl = 1, p = 0.37$).

When focusing on the three components of landscape structure, there was no interaction between component and territory ($\chi^2 = 0.94, ddl = 2, p=0.62$), and explanatory capacities of components were the same in both territories ($\chi^2 = 0.36, ddl = 1, p=0.55$). However, we observed a significant difference in explanatory capacities between the three components ($\chi^2 = 33.28, ddl = 2, p < 0.0001$; Figure 3). Indeed, forest spatial configuration had a greater (about two-fold) explanatory capacity ($R^2(\%) = 14.06 \pm 1.04$ SE) than forest amount and landscape features ($R^2(\%) = 6.09 \pm 1.04$ SE and $R^2(\%) = 7.47 \pm 1.04$ SE, respectively), whereas there was no difference between these last two components of landscape structure (Figure 3).

362
363 **Has habitat fragmentation a positive or negative effect on parasite prevalence?**

364 The sign of the effect on parasite prevalence was variable whether considering forest amount,
365 forest spatial configuration or landscape features and it was not consistent neither within host
366 species nor within parasite lineage (Table 2). For instance, forest loss, forest fragmentation
367 and landscape heterogeneity all increase prevalence of H-FRAG13 whereas they all decrease
368 prevalence of H-FRAG14 and H-VIOLI05 in the Black-whiskered Vireo in Martinique. When
369 considering the same parasite lineage P-PADOM11, its prevalence decreased with forest loss,
370 forest fragmentation and landscape heterogeneity in the Bananaquit, whereas it increased in
371 the Forest Thrush and the Plumbeous Warbler.

372 More globally, the effect of forest spatial configuration was different between the two
373 territories ($\chi^2$ = 7.57, ddl = 1, p=0.006). Forest fragmentation globally increases parasite
374 prevalence in Guadeloupe, whereas its effect was not significantly different from 0 in
375 Martinique (Figure 3). On the contrary, the mean effect of forest amount on parasite
376 prevalence was not different between the two territories ($\chi^2$ = 0.06, ddl = 1, p=0.81), and this
377 effect was not different from zero (Figure 3), indicating that the relationship between forest
378 loss and parasite prevalence was globally neither positive nor negative. Finally, we observed
379 no difference between the two territories for the sign of the relationship between landscape
380 features and parasite prevalence ($\chi^2$ = 0.67, ddl = 1, p=0.41). In addition, landscape
381 heterogeneity is globally associated with neither an increase nor a decrease of parasite
382 prevalence (Figure 3).

383

384 DISCUSSION

385

386 Parasite prevalence was highly variable both between avian host species and between
387 sampling sites for the same host species. Although meteorology and topography were
significant predictors, landscape structure explained on average a higher proportion of the spatial variability in the prevalence of haemosporidians. Strikingly, forest fragmentation had a much larger (about two-fold) influence on parasite prevalence than forest loss and landscape heterogeneity. Finally, whether each of these landscape structure's component was associated with an increase or a decrease in prevalence was highly dependent on the combination of the the territory, the host species and the haemosporidian lineage.

Measuring the relative contribution of landscape structure and meteorology in spatial variation of prevalence allows assessing the potential efficiency of landscape planning in controlling disease dynamics in wildlife, though both factors are not always independent (Afrane, Little, Lawson, Githeko, & Yan, 2008; Lindblade, Walker, Onapa, Katungu, & Wilson, 2000). Although numerous studies have shown substantial influence of either meteorological conditions or landscape composition and configuration on parasite prevalence (Garamszegi, 2011; Gottdenker et al., 2014; Patz et al., 2000; Patz & Reisen, 2001), few studies evaluated their role simultaneously. Some studies reported an effect of both factors (Gonzalez-Quevedo, Davies, & Richardson, 2014; Prist et al., 2016; van Ravensway et al., 2012), while other studies described a major influence of either meteorology (Pérez-Rodríguez et al., 2013; Simon et al., 2014) or landscape properties (Pérez-Rodríguez et al., 2018). In addition to a limited number of studies, general conclusions are difficult to establish because most of these studies used a coarse description of the landscape based on few descriptors, and because part of these studies used long term climatic trends while others used short term meteorological data. However, these two kinds of variables probably have different explanatory power. Indeed, Pérez-Rodríguez et al. (2018) detected none or only a minor influence of climatic variables (WorldClim dataset; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005) on the prevalence of haemosporidians in the Lesser Antillean bullfinch. In this study, we reanalyzed the same prevalence data in this species but we used fine scale
413 meteorological indicators that allowed us to highlight a significant contribution of temperature
414 and precipitations in prevalence variations. Because haemosporidian parasites are not directly
415 exposed to such environmental conditions, meteorological indicators probably explain
416 variation in prevalence through their effect on the main parasite vectors, which are
417 mosquitoes for *Plasmodium* spp. and biting midges and louse-flies for *Haemoproteus* spp.
418 (Santiago-Alarcon, Havelka, Schaefer, & Segelbacher, 2012). Indeed, both abundance and
419 activity of these dipteran vectors are known to be sensitive to weather conditions
420 (Garamszegi, 2011; Li et al., 2019; Minakawa, Omukunda, Zhou, Githeko, & Yan, 2006;
421 Paaijmans et al., 2010; Poh et al., 2019; Venter, Boikanyo, & de Beer, 2019; Zuliani et al.,
422 2015). The fact that vector species have not all the same ecological optimum may explain
423 why we did not observe any general trend associated with rainfall and temperature (Calvete et
424 al., 2008).

425 A majority of papers asserted that habitat fragmentation has a much weaker effect on
426 biodiversity than habitat loss (Cushman & McGarigal, 2003; De Camargo, Boucher-Lalonde,
427 & Currie, 2018; Fahrig, 2003, 2013; Ferraz et al., 2007; Trzcinski, Fahrig, & Merriam, 1999;
428 Uezu & Metzger, 2011). However, our result contrast sharply with these studies as forest
429 fragmentation explained twice as much proportion of the variance in prevalence as habitat
430 loss or landscape heterogeneity. We already reported similar results in a previous work, but
431 they were limited to a single host species (Pérez-Rodríguez et al., 2018). Here, many more
432 host species-parasite lineage combinations were included, which allowed us to test
433 statistically for the relative influence of landscape structure components. Although it has been
434 rightly pointed out that correcting habitat fragmentation metrics for habitat amount may bias
435 the evaluation of their relative influence (Didham et al., 2012; Smith, Koper, Francis, &
436 Fahrig, 2009), this approach makes our results conservative because it should have
437 underestimated the role of habitat fragmentation compared with habitat loss. In addition, we
repeated the analyses without performing this statistical correction and we observed similar results (Supplementary Material S3).

A first explanation for this unusual result stems from the contrast between studies conducted in temperate and tropical regions. Indeed, the majority of studies depicting minor effect of habitat fragmentation have been performed in temperate regions, whereas much less attention has been paid to tropical ecosystems (Fahrig, 2003; Fardila et al., 2017). However, tropical species possess different life histories and peculiar adaptations that make them more sensitive to habitat fragmentation than their temperate counterparts (Cerezo, Perelman, & Robbins, 2010; Harris & Reed, 2002; Lindell et al., 2007; Stutchbury & Morton, 2001). In addition, historical exposure to high disturbances in temperate regions is expected to have filtered out species that would be most susceptible to present disturbances (Balmford, 1996). This extinction filter hypothesis has recently been proposed to reconcile the debate about the relative importance of habitat loss and fragmentation, as studies reporting strong effect of habitat fragmentation are often from the tropics where broad-scale disturbance is rarer (Betts et al., 2019). Although the meta-analysis performed by Fahrig (2017) found no difference in the proportion of negative and positive effects of habitat fragmentation between temperate and tropical regions, no such approach has been used to compare the relative influence of habitat loss and fragmentation (no matter the direction of the effect) between these two types of ecosystems. Clearly, we need more empirical studies in tropical regions designed to quantify precisely the relative effect of habitat loss and fragmentation before we can perform this kind of meta-analysis.

In addition to a possible effect of the climatic region, a second explanation for our results lies in the type of biological response considered. Whereas species distributions and diversity are by far the most common measurements, biotic interactions represent only 11.3% of studies of habitat loss and fragmentation (Fardila et al., 2017). More specifically, we found
only five studies that assessed the relative effect of habitat loss and fragmentation on host-parasite interactions. Although this very low number prevents building a general trend, we noted that all of them reported a significant effect of habitat fragmentation, while half of them detected no effect of habitat loss (Pérez-Rodríguez et al., 2018; Roland, 1993; Simpson et al., 2019; Steckel et al., 2014; With & Pavuk, 2019). Moreover, Steckel et al. (2014) reported that abundance and richness of cavity-nesting bees and wasps were mainly influenced by landscape composition whereas their parasitism rate and the richness of antagonists were mainly driven by landscape configuration. Other studies also suggested that species interaction may respond differently to the same factor than species diversity (Bordes et al., 2015; Doré, Fontaine, & Thébault, in press; Fontúrbel et al., 2015; Tylianakis et al., 2007). Although precise mechanisms remain to be investigated, we may hypothesize in our case study that the edge effect associated with habitat fragmentation determines the exchange surface between the inside and the outside of forest habitat patches, therefore influencing the complex interactions and feedbacks between communities of hosts, parasites and vectors between forest habitat and the landscape matrix.

Many empirical studies revealed either positive (Chasar et al., 2009; Ferreira Junior et al., 2017; Vaz, D'Andrea, & Jansen, 2007), negative (Bonneaud et al., 2009; Evans et al., 2009; S. G. W. Laurance et al., 2013) or even no effect (Cristóbal-Azkarate et al., 2010; Rivero de Aguilar et al., 2018; Sebaio, Braga, Branquinho, Fecchio, & Marini, 2012) of "habitat fragmentation" in the broad sense on parasite prevalence. However, inferring a global trend from these results is challenging because almost all of them were based on qualitative and heterogeneous assessments of habitat modification (e.g., large versus small patches, disturbed versus undisturbed habitat), or they did not isolate the effect of habitat loss and habitat fragmentation. In addition, most of them were based on a limited number of host-parasite combinations. Based on a large number of such combinations and a quantitative
description of both habitat loss and habitat fragmentation, our results suggest that anthropogenic land use changes may have taxon-specific effects, even in the same environmental context. Indeed, both habitat loss and fragmentation led to changes in prevalence in opposite directions for two parasites lineages in the same host species or for the same parasite lineage in two host species. For instance, prevalence in the Forest Thrush decreased for Plasmodium TUMIG03 whereas it increased for Plasmodium TUAMA01, and prevalence of Plasmodium PADOM11 decreased in the Bananaquit whereas it increased in the Plumbeous Warbler. Such inconsistent responses of host-parasite interactions have been reported in other biological systems (Carbayo, Martín, & Civantos, 2019; Chapman, Speirs, Gillespie, Holland, & Austad, 2006; Forti et al., 2020; Lachish, Knowles, Alves, Wood, & Sheldon, 2011). Although literature clearly shows a detrimental effect of habitat loss on biodiversity metrics such as abundance and diversity (Fahrig, 2003; Haddad et al., 2015), the globally null effect of habitat loss on parasite prevalence in our study reinforce the idea that inter-specific interactions may not respond as other biodiversity components.

Numerous non-exclusive mechanisms may explain this pattern of inconsistent variation in parasite prevalence in response to habitat loss and fragmentation. Different components of avian hosts' biology may be affected, and the same avian trait may not respond the same direction. In a review about the link between stress and immunity, Martin (2009) reported that short-term (a few hours or days) stressors enhance immune functions whereas long-term (a few weeks or months) stressors such as habitat modification have an immunosuppressive effect. Although such chronic physiological stress resulting from food shortage in small forest patches has already been demonstrated (Rode, Chapman, McDowell, & Stickler, 2006; Zanette, Doyle, & Tremont, 2000), other studies reported that some forest bird species are able to gain additional food resources from the landscape matrix (Laube, Breitbach, & Böhning-Gaese, 2008). In addition, hosts' genetic diversity may also influence
their ability to cope with parasites (Ekroth, Rafaluk-Mohr, & King, 2019; Hale & Briskie, 2007; Loiseau et al., 2011; Spielman, Brook, & Frankham, 2004). Recent studies performed in Guadeloupe showed that some bird species involved in the present study are strongly and negatively affected by habitat loss and fragmentation in terms of genetic diversity while some species seems not affected (Khimoun et al., 2016, 2017). As a whole, these studies suggest that habitat loss and fragmentation may cause a decrease in immune ability in some host species while it may enhance immunity in other host species, hence driving prevalence variation in opposite directions depending on host species.

Because internal parasites are not directly exposed to forest loss and fragmentation, these factors may also influence prevalence of haemosporidians through their effect on vectors of these parasites. Although we do not know the specific identity of the vectors in our study system, several species of mosquitoes and biting midges have been described in Guadeloupe and Martinique, among which some have been reported to be vectors of avian haemosporidians (Belkin & Heinemann, 1976; Grogan Jr, Spinelli, Ronderos, & Cazorla, 2013; Schaffner, 2003). Forest loss and fragmentation and the associated land-use changes can modify numerous environmental conditions that are crucial for insect communities such as vegetation structure and composition or microclimate, which subsequently impact their density, diversity or feeding behavior (Kilpatrick, Kramer, Jones, Marra, & Daszak, 2006; Lindblade et al., 2000; Norris, 2004; Patz et al., 2000; Vittor et al., 2006, 2009). However, even closely related vector species may have different habitat requirements and feeding behaviors (Becker et al., 2010; Ferraguti et al., 2016; Johnson, Gómez, & Pinedo-Vasquez, 2008). For instance, Meyer Steiger et al. (2016) observed that some mosquito species were more abundant in forest interiors while other species were more abundant in adjacent grasslands. In addition, while it is still unclear whether blood feeding pattern (i.e., identity and frequency of avian host exploited) of mosquitoes fits with host community structure and/or
inherent mosquito choice, several studies showed that these insects have clear feeding preferences for specific host species (Chaves, Harrington, Keogh, Nguyen, & Kitron, 2010; Lyimo & Ferguson, 2009; Rizzoli et al., 2015). Finally, there are growing evidence that haemosporidian lineages may be transmitted by a limited number of vector species (Gutiérrez-López, Bourret, & Loiseau, 2020). All these specificities suggest that habitat loss and fragmentation may affect parasite prevalence in a direction that depends on the combination of both host and parasite species, as well as the vector identity.

In conclusion, the fine characterization of habitat loss and fragmentation that we used clearly shows that spatial configuration of habitat patches has to be taken into account in planning landscape for biodiversity conservation purposes. However, the specificity of the response (positive or negative) of parasite prevalence with regard to the host-parasite combination makes general recommendations about the optimal anthropogenic landscape difficult to provide at this point. The potential complexity in translating the sign of a statistical relationships between variables (here, landscape structure and parasite prevalence) into a positive or negative effect from a conservation point of view is exacerbated when considering antagonistic interspecific interactions (Miller-Rushing et al., 2019). In our study system, an increase in prevalence due to habitat fragmentation would be considered negative for one species (i.e., the avian host) but positive for the other one (i.e., the parasite lineage). In addition, we would need some knowledge about parasite virulence that is known to be highly variable depending on both haemosporidian lineages and host species (Palinauskas, Valkiūnas, Bolshakov, & Bensch, 2008; Valkiūnas, 2004). Finally, the conservation paradigm of eradication stemming from the negative impacts of parasites on their hosts needs to be revised, because parasites can play a positive role in their host ecology and evolution such as regulating populations of other parasites potentially more virulent, maintaining genetic
562 diversity of their hosts and promoting host species co-existence (Dougherty et al., 2016;
563 Spurgin & Richardson, 2010; Wood & Johnson, 2015).

564
565 ACKNOWLEDGMENTS

566
567 We thank E. Arnoux, M. Bely, M. L. Carpentier, F. Cavallo, S. Chauvin, B. Condé, M.
568 Delpuech, G. Leblond, M. Leblond, E. Lefol, M. Loubon, V. Rocheteau, F. Théron for their
569 contribution to fieldwork; E. Arnoux, D. Apollo, J. Pernin, N. Debrosse for their contribution
570 to labwork; H. Gadenne and C. Eraud for their help in calculating landscape metrics with
571 FRAGSTAT; N. Navarro for helpful discussions about data analyses; and Météo-France,
572 especially A. Auffray and A. Tamburini, who provided the temperature and precipitation data,
573 as part of an agreement with the University of Burgundy. Bird sampling has been done under
574 permission granted by several organizations: the Direction de l'Environnement, de
575 l'Aménagement et du Logement (DEAL) from Guadeloupe, the DEAL from Martinique, the
576 Parc National de la Guadeloupe, the Direction Régionale de l'Office National des Forêts
577 (ONF) de Guadeloupe, and the Direction Régionale de l'ONF de Martinique. This study is
578 part of the project FRAG&BINV and CANON (coordinated by SG), that were supported by
579 the ERA-Net Net- Biome 2010 UE Research Network, the Agence Nationale de la Recherche
580 (France), the Fundação para a Ciência e a Tecnologia (Portugal), the Conseil Régional de la
581 Martinique (France), the Conseil Régional de Bourgogne (France), and the French
582 "Investissements d'Avenir" program, project ISITE-BFC (contract ANR-15-IDEX-0003).

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1055 Waldenström, J., Bensch, S., Hasselquist, D., & Ostman, O. (2004). A new nested polymerase

1056 chain reaction method very efficient in detecting *Plasmodium* and *Haemoproteus*

1057 infections from avian blood. *The Journal of Parasitology*, 90(1), 191-194. doi:

1058 10.1645/GE-3221RN

1059 White, A. (2015). *Linking human-disturbed landscapes with pathogen prevalence in wildlife:*

1060 *A meta-analysis*. Colorado State University, Fort Collins, Colorado.

1061 With, K. A., & Pavuk, D. M. (2019). Habitat configuration matters when evaluating habitat-

1062 area effects on host-parasitoid interactions. *Ecosphere*, 10(2), e02604. doi:

1063 https://doi.org/10.1002/ecs2.2604

1064 Wood, C. L., & Johnson, P. T. (2015). A world without parasites: Exploring the hidden

1065 ecology of infection. *Frontiers in Ecology and the Environment*, 13(8), 425-434. doi:

1066 10.1890/140368

1067 Zanette, L., Doyle, P., & Tremont, S. M. (2000). Food Shortage in Small Fragments:

1068 Evidence from an Area-Sensitive Passerine. *Ecology*, 81(6), 1654-1666. doi:

1069 10.2307/177314

1070 Zuliani, A., Massolo, A., Lysyk, T., Johnson, G., Marshall, S., Berger, K., & Cork, S. C.

1071 (2015). Modelling the Northward Expansion of *Culicoides sonorensis* (Diptera: Ceratopogonidae) under Future Climate Scenarios. *PLOS ONE*, 10(8), e0130294. doi:

1072

1073 10.1371/journal.pone.0130294

1074

1075
1076 AUTHORS' CONTRIBUTIONS

1077
1078 SG, BF and AK designed the study and obtained the funding; all authors contributed to data collection; AP and SG analyzed the data and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

1081
1082 DATA ACCESSIBILITY STATEMENT

1083
1084 Data will be deposited on Dryad Digital Repository

1085
1086 TABLES AND FIGURES
<table><caption>**Table 1: Frequency of haemosporidian lineages in the different avian host species.** The table shows the number of individual birds that are parasitized by each haemosporidian lineage [the first letter corresponding to genus *Haemoproteus* (H) or *Plasmodium* (P)]. This number is also provided in brackets after the exclusion of sampling sites with less than 10 birds. The grey color indicates the combinations of host-parasite used in PLS analyses. The total number of sampled birds is indicated under each territory (GD for Guadeloupe and MA for Martinique) for each species.</caption><thead><tr><th rowspan="3">Haemosporidian lineage<br>(Genebank accession number)</th><th colspan="2">Bananaquit</th><th colspan="2">Lesser Antillean<br>Bullfinch</th><th colspan="2">Black-whiskered<br>Vireo</th><th colspan="2">Caribbean Elaenia</th><th colspan="2">Forest Thrush</th><th colspan="2">Plumbeous<br>Warbler</th></tr><tr><th>GD</th><th>MA</th><th>GD</th><th>MA</th><th>GD</th><th>MA</th><th>GD</th><th>MA</th><th>GD</th><th>MA</th><th>GD</th><th>MA</th></tr><tr><th>383 (335)</th><th>452 (434)</th><th>524 (488)</th><th>563 (554)</th><th>450 (417)</th><th>284 (240)</th><th>391 (321)</th><th>322 (311)</th><th>438 (398)</th><th>415 (381)</th><th></th><th></th></tr></thead><tbody><tr><td>H-COFLA06 (...)</td><td>3 (3)</td><td>54 (45)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG01 (...)</td><td>5 (5)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG03 (...)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG08 (KX887467)</td><td>89 (81)</td><td>22 (22)</td><td>3 (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG09 (...)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG09 (...)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG12 (...)</td><td></td><td></td><td></td><td></td><td>7 (7)</td><td>5 (5)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG13 (...)</td><td></td><td></td><td></td><td></td><td>119 (106)</td><td>37 (28)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG14 (...)</td><td></td><td></td><td></td><td></td><td>57 (53)</td><td>25 (22)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG16 (...)</td><td></td><td></td><td></td><td></td><td>5 (5)</td><td>6 (5)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG21 (...)</td><td></td><td></td><td></td><td></td><td>1 (1)</td><td>2 (1)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG22 (...)</td><td></td><td></td><td></td><td></td><td>1 (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG25 (...)</td><td></td><td></td><td></td><td></td><td>2 (2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG26 (...)</td><td></td><td></td><td></td><td></td><td></td><td>1 (1)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG27 (...)</td><td></td><td></td><td></td><td></td><td>8 (8)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-FRAG28 (...)</td><td></td><td></td><td></td><td></td><td>1 (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-LOXPOR01 (GQ141592)</td><td>263 (229)</td><td>115 (114)</td><td>148 (136)</td><td>426 (418)</td><td>1 (1)</td><td></td><td>1 (1)</td><td></td><td></td><td></td><td>1 (1)</td><td></td></tr><tr><td>H-MAFUS02 (...)</td><td></td><td></td><td>1 (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1 (1)</td><td></td></tr><tr><td>H-VIALT01 (...)</td><td></td><td></td><td></td><td></td><td>8 (8)</td><td>5 (5)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-VIOLI04 (...)</td><td></td><td></td><td></td><td></td><td></td><td>1 (0)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-VIOLI05 (...)</td><td></td><td></td><td></td><td></td><td>15 (15)</td><td>18 (16)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>H-VIOLI06 (...)</td><td></td><td></td><td></td><td></td><td>77 (70)</td><td>31 (27)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tbody><tr><td>H-VIOLI08 (...)</td><td></td><td></td><td>1 (1)</td><td>5 (4)</td><td></td><td></td></tr><tr><td>P-CATUST05 (...)</td><td></td><td></td><td></td><td></td><td></td><td>2 (2)</td></tr><tr><td>P-CINRUF01 (...)</td><td>1 (0)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>P-DENPET03 (...)</td><td></td><td></td><td>5 (5)</td><td>1 (1)</td><td></td><td></td></tr><tr><td>P-DONANA05 (...)</td><td></td><td></td><td></td><td></td><td></td><td>2 (2)</td></tr><tr><td>P-FRAG02 (...)</td><td></td><td></td><td>1 (1)</td><td>7 (5)</td><td>1 (1)</td><td></td></tr><tr><td>P-FRAG04 (...)</td><td></td><td>1 (1)</td><td></td><td></td><td></td><td></td></tr><tr><td>P-FRAG06 (...)</td><td></td><td></td><td></td><td></td><td></td><td>37 (34)</td></tr><tr><td>P-FRAG07 (...)</td><td>1 (1)</td><td></td><td></td><td></td><td></td><td>2 (2)</td></tr><tr><td>P-FRAG15 (...)</td><td></td><td></td><td></td><td>2 (1)</td><td>4 (4)</td><td></td></tr><tr><td>P-FRAG17 (...)</td><td></td><td></td><td></td><td>3 (3)</td><td></td><td></td></tr><tr><td>P-FRAG18 (...)</td><td></td><td></td><td></td><td>9 (9)</td><td>2 (1)</td><td></td></tr><tr><td>P-FRAG19 (...)</td><td></td><td></td><td></td><td>1 (1)</td><td></td><td></td></tr><tr><td>P-FRAG23 (...)</td><td></td><td></td><td></td><td></td><td>1 (1)</td><td></td></tr><tr><td>P-GRW06 (...)</td><td></td><td></td><td></td><td></td><td>2 (2)</td><td>14 (12)</td></tr><tr><td>P-ICTCAY01 (DQ659545)</td><td>75 (64)</td><td>156 (150)</td><td>39 (32)</td><td>21 (21)</td><td>3 (3)</td><td>3 (2)</td></tr><tr><td>P-MASTR01 (...)</td><td></td><td></td><td></td><td></td><td></td><td>2 (1)</td></tr><tr><td>P-PADOM09 (AF069611)</td><td>3 (2)</td><td>15 (15)</td><td>2 (1)</td><td>1 (1)</td><td>13 (13)</td><td>18 (18)</td></tr><tr><td>P-PADOM11 (HM146899)</td><td>11 (10)</td><td>24 (24)</td><td>14 (13)</td><td>3 (3)</td><td>4 (3)</td><td>2 (2)</td></tr><tr><td>P-SPMAG06 (...)</td><td></td><td></td><td></td><td></td><td></td><td>2 (2)</td></tr><tr><td>P-TFUS06 (...)</td><td></td><td></td><td></td><td>1 (1)</td><td></td><td></td></tr><tr><td>P-TUAMA01 (...)</td><td></td><td></td><td></td><td></td><td></td><td>41 (36)</td></tr><tr><td>P-TUMIG03 (...)</td><td></td><td></td><td>1 (1)</td><td></td><td></td><td>47 (47)</td></tr><tr><td>P-VIOLI03 (...)</td><td></td><td></td><td>24 (23)</td><td>9 (9)</td><td></td><td></td></tr><tr><td>undetermined</td><td>1 (0)</td><td>3 (3)</td><td>3 (3)</td><td>3 (3)</td><td>1 (1)</td><td>31 (26)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>29 (27)</td></tr></tbody></table>
<table><caption>Table 2: Magnitude of the relative predictive capacity (explained variance of prevalence in %) of the three component of landscape structure for each host-parasite-territory combination studied. The grey or white shadowing indicates respectively whether parasite prevalence grows or diminishes with habitat loss, habitat fragmentation or landscape features.</caption><thead><tr><th></th><th></th><th>Habitat loss</th><th>Habitat fragmentation</th><th>Landscape features</th></tr></thead><tbody><tr><td rowspan="4">Bananaquit</td><td>H-FRAG08</td><td>3</td><td>16</td><td>11</td></tr><tr><td>H-LOXPOR01</td><td>3</td><td>8</td><td>5</td></tr><tr><td>P-ICTCAY01</td><td>3</td><td>8</td><td>5</td></tr><tr><td>P-PADOM11</td><td>12</td><td>7</td><td>9</td></tr><tr><td rowspan="3">Lesser Antillean Bullfinch</td><td>H-LOXPOR01</td><td>4</td><td>40</td><td>6</td></tr><tr><td>P-ICTCAY01</td><td>4</td><td>20</td><td>8</td></tr><tr><td>P-PADOM11</td><td>1</td><td>17</td><td>16</td></tr><tr><td>Caribbean Elaenia</td><td>P-PADOM09</td><td>1</td><td>23</td><td>6</td></tr><tr><td rowspan="6">Guadeloupe</td><td>H-FRAG13</td><td>7</td><td>12</td><td>7</td></tr><tr><td>H-FRAG14</td><td>5</td><td>35</td><td>7</td></tr><tr><td>H-VIOLI05</td><td>3</td><td>17</td><td>3</td></tr><tr><td>H-VIOLI06</td><td>3</td><td>14</td><td>4</td></tr><tr><td>P-VIOLI03</td><td>2</td><td>10</td><td>3</td></tr><tr><td>P-ICTCAY01</td><td>13</td><td>7</td><td>7</td></tr><tr><td rowspan="5">Forest Thrush</td><td>P-ICTCAY01</td><td>7</td><td>9</td><td>6</td></tr><tr><td>P-GRW06</td><td>3</td><td>7</td><td>3</td></tr><tr><td>P-PADOM11</td><td>9</td><td>8</td><td>7</td></tr><tr><td>P-TUAMA01</td><td>6</td><td>6</td><td>6</td></tr><tr><td>P-TUMIG03</td><td>11</td><td>6</td><td>18</td></tr><tr><td rowspan="2">Plumbeous Warbler</td><td>P-PADOM11</td><td>5</td><td>17</td><td>9</td></tr><tr><td>P-PADOM11</td><td>6</td><td>13</td><td>5</td></tr><tr><td rowspan="6">Martinique</td><td>H-COFLA06</td><td>1</td><td>11</td><td>8</td></tr><tr><td>H-FRAG08</td><td>9</td><td>8</td><td>12</td></tr><tr><td>H-LOXPOR01</td><td>6</td><td>6</td><td>5</td></tr><tr><td>P-ICTCAY01</td><td>8</td><td>10</td><td>11</td></tr><tr><td>P-PADOM09</td><td>9</td><td>16</td><td>7</td></tr><tr><td>P-PADOM11</td><td>4</td><td>9</td><td>4</td></tr><tr><td rowspan="2">Lesser Antillean Bullfinch</td><td>H-LOXPOR01</td><td>2</td><td>14</td><td>2</td></tr><tr><td>P-ICTCAY01</td><td>8</td><td>24</td><td>8</td></tr><tr><td>Caribbean Elaenia</td><td>P-PADOM09</td><td>1</td><td>16</td><td>3</td></tr><tr><td rowspan="4">Black-whiskered Vireo</td><td>H-FRAG13</td><td>8</td><td>9</td><td>3</td></tr><tr><td>H-FRAG14</td><td>20</td><td>13</td><td>13</td></tr><tr><td>H-VIOLI05</td><td>16</td><td>21</td><td>10</td></tr><tr><td>H-VIOLI06</td><td>3</td><td>20</td><td>16</td></tr></tbody></table>
<figure><img src="image_2.png" /></figure>

**Figure 1: Sampling sites across Guadeloupe and Martinique.** The green and grey colors represent the forest and the matrix habitats according to the Corine Land Cover 2012, respectively. The black dots correspond to sampled sites where we sampled more than 10 birds in at least one species and the white dots represent sampled sites where we had never sampled more than ten birds for any of the species studied (and that were removed from PLSR analyses).

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<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 2: Explained variance of parasite prevalence for Partial Least Squares Regression (PLSR) models.** Each vertical bar represents the R² values, expressed as percentage of explained variance of PLSR models (partitioned among the different predictors), for each host-parasite combination in Guadeloupe and in Martinique.</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>**Figure 3:** Relative explanatory capacity (explained variance of prevalence in %) of (A) landscape structure *versus* meteorology and topography and (B) the three components of landscape structure. Overall signed effect of (C) each component of landscape structure on parasite prevalence in Guadeloupe and Martinique. Vertical bars represent 95% confidence intervals.</figcaption></figure>

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