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• W2808096154 abstract "Conservation in developing countries faces two difficulties related to their particular socioeconomic and biological context. First, developing countries lack funding to study how the huge numbers of species they harbour are affected by humans (Myers, Mittermeier, Mittermeier, da Fonseca, & Kent, 2000). Second, threats to diversity in these regions are distinctive and still little understood, such as extraction of non-timber forest products, fuelwood collection, subsistence hunting and extensive grazing (Martorell & Peters, 2005; Peres, 2000; Singh, 1998). Because such threats involve gradual, low intensity, yet constant damage on ecosystems, they have been catalogued under the concept of chronic anthropogenic disturbance (CAD; Singh, 1998). In the long term, CAD increases environmental stress, causes severe land degradation, changes community composition and provokes regime shifts that are hard to detect (Hughes, Linares, Dakos, van de Leemput, & van Nes, 2013; Leal, Andersen, & Leal, 2015; Ribeiro et al., 2016; Singh, 1998; Villarreal-Barajas & Martorell, 2009; Watt, 1998). Because of the impossibility to examine how every species responds to poorly studied human activities, we have resorted to the notion that species belonging to a taxonomic group (e.g., family) have similar behaviours and thus may be conserved under the same conditions. Thus, management plans are commonly developed for whole taxonomic groups (Possingham et al., 2002). This has been the case of orchids (Pillon & Chase, 2007), cacti (Ortega-Baes et al., 2010) and cetaceans (Wade, 1998). If such generalizations were valid, policy-making would be facilitated because the data available for a few species could be extrapolated to other taxa. The assumption that related species may be conserved or managed under the same conditions may hold if there is a strong phylogenetic signal. Phylogenetic signal is the tendency of related species to resemble each other (Blomberg & Garland, 2002; Losos, 2008). Related species may have similar niches (Losos, 2008) and life histories (Brunbjerg, Borchsenius, Eiserhardt, Ejrnaes, & Svenning, 2012; Heppell, Caswell, & Crowder, 2000; Mandujano, Verhulst, Carrillo-Angeles, & Golubov, 2007; Silvertown, Franco, & Menges, 1996), both of which determine the population's vulnerabilities, and therefore, the way in which they may be managed (Caswell, 2000; Silvertown et al., 1996). Indirect evidence for phylogenetic signal in species’ response to human impacts comes from studies where the phylogenetic diversity of disturbed communities has been analysed. A community composed solely of close relatives will have a low phylogenetic diversity (Webb, Ackerly, McPeek, & Donoghue, 2002). This will be the case when the species in a community have phylogenetically conserved environmental requirements (Webb et al., 2002), so low phylogenetic diversity in a perturbed community may be the result of related species sharing a preference for disturbed sites. Reduced phylogenetic diversity as a consequence of disturbance has been observed in coastal dunes (Brunbjerg et al., 2012), tropical forests (Andrade et al., 2015; Ribeiro et al., 2016; Santos et al., 2014) and urban environments (Knapp, Kühn, Schweiger, & Klotz, 2008). There is also evidence to the contrary, i.e., that close relatives might not respond in a similar way to anthropic activities. Arroyo-Rodríguez et al. (2012) found no reduction in phylogenetic diversity with fragmentation in tropical rainforests. A study involving nine species in the genus Mammillaria (Cactaceae) found that some species increase in density with CAD while others show the opposite pattern, suggesting that species should be managed in different ways (Martorell & Peters, 2009). Among turtles, taxonomic relatedness is not necessarily correlated with the appropriate management strategy (Heppell et al., 2000). However, in the latter studies there was also evidence for some degree of similarity among congenerics. Two important issues should be considered when extrapolating data from few species to whole taxa for conservation purposes. First, we can generalize safely only if phylogenetic signal is strong, so we need measurements of its intensity. So far, phylogenetic signal has been measured in the life-history attributes of ruderal plants that may have an anthropogenic affinity (Brunbjerg et al., 2012), and in the extinction risk of vertebrates (Fritz & Purvis, 2010). In both cases, phylogenetic signal was weak. Direct, quantitative measurements of phylogenetic signal in species response to human impacts are still lacking. Second, stakeholders are unlikely to make decisions on how to manage species based on the phylogenetic distance between them, but rather on the taxonomic groups they belong to (Possingham et al., 2002). Current taxonomy reflects phylogenetic relatedness accurately (Apg, 2016), so a strong phylogenetic signal should result in a large homogeneity within taxonomic groups, making generalizations valid. Nevertheless, this needs to be tested. In this study, we measure phylogenetic signal in the response to chronic anthropogenic disturbance. We also determine which taxonomic level (order, family or genus), if any, may be appropriate for designing taxon-specific management plans because its species display similar responses to human impacts. Finally, we address the community-level consequences of phylogenetic signal by analysing the effects of human activities on phylogenetic diversity. To determine the generality of our results, we studied a semi-arid grassland and a tropical dry forest in Mexico. Our general approach was the following: We calculated disturbance responses for each species using abundance data and disturbance measurements in several sites, obtaining six disturbance-response indices. These were used to determine whether species in different taxonomic groups (orders, families, genera) show similar responses to disturbance by means of nested ANOVA, and if there was a phylogenetic signal using Pagel's lambda (Figure 1). The semi-arid grassland is in Concepción Buenavista, Oaxaca, southern Mexico, at an elevation of 2,275 m a.s.l. Annual precipitation is 530.3 mm, and mean temperature is 16.3°C. Soil is shallow (<20 cm) and eroded (Villarreal-Barajas & Martorell, 2009). Data come from 21 0.5 ha sites. In each site, eight 1 × 1 m quadrats were randomly chosen, and in each one we randomly selected 20 0.1 × 0.1 m squares. In the latter, we quantified the abundance of all vascular plants. Most plants in this community are small (<5 cm tall). Up to 135 individuals and 25 species may be found per dm2, making this the ideal sampling scale. The tropical dry forest is located in La Cañada, Oaxaca. Its elevation ranges from 580 m a.s.l. to 850 m a.s.l. The weather stations in the area report ranges of 473 to 515 mm in annual precipitation and is 25.2 to 26.2°C in mean temperature (SMN, 2016). Data for this community had been previously recorded by E. Hernández-Calvillo (unpublished), and comprise vascular plant abundances for 38 sites with three transects each, measuring 50 × 10 m for trees and 50 × 4 m for shrubs and small succulents. Chronic anthropogenic disturbance (CAD) acts on natural communities gradually, so CAD gradients spanning relatively pristine communities to heavily degraded sites were observed. CAD intensity was measured at each of the 59 sites following the method proposed by Martorell and Peters (2005, 2009). The CAD index is based on 14 metrics that measure disturbance values for human activities, livestock and land degradation, the sum of which provides an estimate of total CAD. The index runs from zero in pristine sites to slightly over 100 in extremely degraded communities where even soil has been almost completely lost. Because the index includes land degradation, environmental stress is also incorporated. Taxonomic classification followed from Apg (2016) and the Angiosperm Phylogeny Website v. 13 (Stevens, 2012). We lack a molecular phylogeny for the species in the tropical dry forest because suitable biological material and DNA sequences were unavailable. We instead constructed our tree based on published phylogenies (see Appendix S1). This provides state-of-the-art data on tree topology but not on branch lengths, which were set to a value of one. This is not much of a problem as branch lengths have only a minor effect on phylogenetic signal estimates (Molina-Venegas & Rodríguez, 2017; Münkemüller et al., 2012). The grassland species’ phylogeny was estimated using MrBayes from rbcL and matK DNA fragments obtained from plant tissue (Ronquist et al., 2012, see Appendix S2 for lab techniques). Disturbance response analysis (Martorell & Peters, 2009) is based on the observed changes in species density over disturbance gradients. We used generalized additive models (GAMs) for this purpose. GAMs provide a function that relates abundance to CAD without specifying any a priori shape for the curve (Crawley, 2007). We determined that the Tweedie distribution was appropriate for modelling the error distribution by means of AICs. Model fitting was performed using the package mgcv (Wood, 2004) for r (R Development Core Team, 2016). Disturbance response analysis was performed only for those species that occurred at least in six sites (41 tropical forest and 58 grassland species). To assess if the species within a given taxon have similar responses to CAD, and to determine within which taxonomic level (species, genus, family, order) these similarities occur, we used nested ANOVA (Zar, 2010). In this analysis, lower taxa are nested in the higher ones, so we can estimate the amount of variation that is explained by each level. If the higher levels such as order or family explain most of the variation, then the species within them are similar in their response, and conservation recommendations could be based on higher taxonomic levels. In contrast, if most of the variation is explained by the species level, then the response to CAD is uncorrelated with taxonomy and conservation should be considered at the species level. We analysed each disturbance response index separately. To determine if the amount of variation in indices explained by each taxonomic level differed from that expected by chance we performed randomization tests. We randomly assigned the value of each index to each species without changing their taxonomic relationships and repeated the ANOVA. This procedure destroys phylogenetic signal because species responses do not depend on ancestry but on chance. For each of the 1,000 randomizations we recorded the amount of variation explained by each taxonomic level. We then counted the number of randomizations in which, for a given taxonomic level, the amount of variance explained was smaller or larger than the observed one. To calculate a p value, we then took the smaller of these two figures, divided it by 1,000 and multiplied it by two for a two-tailed test. In terms of decision-making, it is the cumulative amount of variance that matters. If, for example, management plans are made for genera, then the variation explained by families and orders is necessarily accounted for because all the species in the same genus necessarily belong to the same family and order. Thus, we repeated the randomization test described above, but using the cumulative variances. To measure phylogenetic signal in response to CAD we used Pagel's λ (Freckleton, Harvey, & Pagel, 2002; Pagel, 1999). If λ = 1, species resemble each other as much as we would expect under Brownian evolution. A λ = 0 indicates that traits are independent from phylogeny (Freckleton et al., 2002; Pagel, 1999). We used Geiger (Harmon, Weir, Brock, Glor, & Challenger, 2008) for r to estimate λ through maximum likelihood assuming Brownian evolution and measurement error in disturbance response analysis indices. As before, we used a randomization test to assess if λ was significantly greater than zero. This was done by randomly assigning the disturbance response analysis index values to the tips of the tree. p-values were estimated from the number of randomizations for which λ was larger than the observed one, as this is a one-tailed test. To measure phylogenetic diversity we calculated the mean phylogenetic distance (MPD). This index is calculated by averaging the branch lengths between every possible pair of species. MPD was calculated for presence–absence data, and also weighted by species abundances. MPD was estimated using package Picante (Kembel et al., 2010). To assess if MPD changes with CAD we used ordinary regression analyses. Chronic Anthropogenic Disturbance (CAD) values in the tropical dry forest ranged from 3.17 to 52.54, whereas in the grassland these values ranged from 21.07 to 110.7. In many species we observed monotonic relationships between CAD and abundance, which results in some indices having values either of zero or equal to the maximum disturbance observed (Figure 2b,c). 29 of the 47 tropical dry forest species had = 0 and 20 of them had a D* = 0. Of the 58 semi-arid grassland species, 45 and 41 species had D* and values of zero, respectively. 24 species in the tropical dry forest and eight species in the semi-arid grassland had the maximum observed CAD as their value. The rest of the indices showed greater variability (see Appendix S3). Family was the taxonomic level that significantly explained more variation than expected by chance in the tropical dry forest, while in the semi-arid grassland it was order. When cumulative variation was analysed, the genus level explained more than 80% of the variation and, in the grassland, it even explained 100% of the variation for some of the CAD indices. In the tropical dry forest, the indices for which family explained more variation than expected by chance were (an index for which order and species explained significantly less variance than expected) and I. Genus explained a significantly large (90.92%) fraction of the variation in sensitivity (v) (Figure 3 left panel). Family and genus also explained more cumulative variation than expected by chance. This was the case of family for I and pressure (), and genus for (Figure 3 right panel). In the semi-arid grassland, the indices for which order explained more variation than expected by chance were and . For most indices, lower taxonomic levels explained significantly low amounts of variation. This was the case for , I, and at the genus level, and for at the species level (Figure 4 left panel). In the case of cumulative variation, family explained more variation than expected by chance for and , whereas genus explained 100% of variation for (Figure 4 right panel). Results from the taxonomic and phylogenetic analyses did not show a complete agreement. In general, a larger number of significant patterns were detected in the taxonomic analyses, and only a subset of these were detected by λ. In the tropical dry forest we found phylogenetic signal (λ = 1) only for sensitivity (v), although it was marginally significant (p = .056). In the semi-arid grassland, phylogenetic signal was detected in three of the four indices for which the taxonomic tests found departures from randomness: (λ = 0.52, p = .031), (λ = 0.89, p = .018) and I (λ = 0.33, p = .043). We did not find a significant relationship between phylogenetic distance and CAD in the tropical dry forest either for un-weighted distance (r = 0.05, p = .15) or abundance-weighted distance (r = 0.02, p = .31). In the semi-arid grassland, mean phylogenetic distance decreased with CAD (r = 0.22, p = .03; Figure 5). However, with abundance-weighted distance we did not observe this pattern (r = 0.02, p = .47). Given the general trend for higher taxonomic levels to explain a large portion of the variation in the responses to CAD, and the presence of phylogenetic signal in some of these responses, our results support the idea that related species respond similarly to CAD. However, phylogenetic signal in some CAD responses was weak or undetectable, and the amount of variation explained by higher taxonomic levels was not large enough (frequently <60% for the family level) to make reliable generalizations, suggesting that only close relatives are likely to be sufficiently similar to be managed in the same way. Species in the same genera are good candidates for this, as the variation explained by the genus level was >90%. Phylogenetic signal may be responsible for the reduction in phylogenetic diversity with disturbance. This is because if there is phylogenetic signal in response to CAD, then disturbance may filter out related species intolerant to CAD thus reducing phylogenetic diversity in a community. Such ecological filtering processes may imply a loss of functions in some disturbed communities. As expected if related species respond similarly to disturbance, higher taxonomic levels explained more variation in the response to CAD than expected by chance. Consequently, the unexplained fraction of the variation remaining for lower taxa was depleted, so, in some cases, lower taxonomic levels explained less variation than expected by chance. In other words, the response to CAD was similar in the species from the same genus, but this was due mostly to the fact that they belonged to the same order or family. Caution must be exerted when interpreting nested ANOVA results. Based on variance explained by the non-cumulative analysis, our results indicated that orders and genera were the taxonomic levels where responses to CAD varied the most among taxa (Figures 3 and 4). However, these results are affected by the particular taxonomic structure of the communities. At both sites, many orders and genera were represented by a single species, so ANOVA automatically imputed variation between species to their respective upper taxa. The null model, which retains the taxonomic structure of the observed communities and thus is also affected by single species, also indicates that genera and orders are the levels that explain more variation. Thus, rather than drawing conclusions based on amounts of explained variation, we need to focus on taxonomic levels that explain more variation than expected by chance, i.e., family in the tropical forest and order in the grassland. The fact that orders and families explain more variation than expected by chance poses interesting questions on the evolutionary history of tolerance to grazing, but does not mean that species are similar enough to be managed in the same way. When we look at cumulative variation at both sites, order explains 10%–50% of the variation, and family 50%–70%. In contrast, genus explain 80%–100% of the cumulative variation in CAD responses. Thus, management plans designed for one species may be extrapolated safely to others in the same genus, but not so confidently to confamilial species, and certainly not to species in the same order. Care must be taken, as valid extrapolations between congeners is by no means certain: for example, most plants in the genus Mammillaria (Cactaceae) are ruderals, a small fraction of them are hindered by even small amounts of CAD (Martorell & Peters, 2009). Non-zero λ values were estimated only for indices where nested ANOVA found non-random patterns, indicating some congruence between both analyses. However, only in a few cases were such λ values significant. This would suggest that either ANOVAs tend to produce spurious results, or that tests using Pagel's λ lacked statistical power. It is well known that detecting phylogenetic signal can be difficult when distant species are included in the analyses (as in our case) because distant taxa can converge given sufficiently large evolutionary times (Cadotte, Davies, & Peres-Neto, 2017). This is a problem because all species pairs (including distant ones that are ecologically similar) are compared when estimating λ (Freckleton et al., 2002). ANOVA could be more robust under such situations because comparisons are conducted only within taxonomic groups. Nevertheless, little is known about the effects of evolutionary depth on nested ANOVAs, so it is difficult to conclude if these are more reliable than λ or it is the other way around. Our results represent some pros and cons for making decisions when only taxonomic data are available. It seems that nested ANOVAS are powerful and will detect similarities between species when they occur, but it may also happen that some of the patterns detected do not arise from phylogenetic signal. Finding phylogenetic signal in species’ response to anthropogenic disturbance may seem odd because CAD represents a relatively recent event in their evolutionary history, and lineages probably would not have had enough time to develop adaptations for these disturbances. Previous studies have shown rapid responses of plants to anthropogenic environments, as is the case for the evolution of resistance to herbicides (Powles & Yu, 2010; Warwick, 1991). Nevertheless, if there indeed were adaptations to CAD, we would expect such recent evolutionary changes to be reflected among populations of the same species or between species in the same genus. However, we observed that orders and families, but not genera, are the taxonomic levels that explain more variation than expected by chance, suggesting that the shared responses to CAD between species are ancient preadaptations rather than recent evolutionary responses. Preadaptations may arise if adaptations to natural disturbance are also advantageous in the face of anthropogenic disturbance (Brunbjerg et al., 2012; Coughenour, 1985; Martorell & Peters, 2009). For instance, Poaceae may have evolved traits to resist naturally occurring herbivores (Coughenour, 1985) which may also be useful in coping with modern livestock pressure. Environmental conditions may also be responsible for the observed phylogenetic signal. For instance, many of the tropical dry forest species with high values (i.e., require disturbance to invade the community) occur in desert scrubs and tolerate aridity. This may be a preadaptation for the dry conditions observed in disturbed tropical dry forests (Lebrija-Trejos, Pérez-García, Meave, Poorter, & Bongers, 2011). Aridity may also play a role in the grassland, where we found phylogenetic signal in (Figure 6). In this system, high CAD levels cause erosion, and the remaining thin soils cannot retain water (Villarreal-Barajas & Martorell, 2009) imposing an intolerable limitation for most of the species. Many of the species with high values were either succulent or C4 plants that tolerate hydric stress (Sage, 2004), many of which are related—they are Poaceae, Cactaceae and Portulacaceae. Phylogenetic distance decreases significantly when anthropogenic disturbance increases in costal dunes (Brunbjerg et al., 2012), urban environments (Knapp et al., 2008), lakes (Helmus et al., 2010), tropical dry forests (Ribeiro et al., 2016), and tropical rainforests (Santos et al., 2014). Some authors have even described disturbance as the main factor that structures communities phylogenetically (Brunbjerg et al., 2012). We found a similar trend in our grassland but not in the tropical dry forest. One of the most popular explanations for communities with low phylogenetic diversity is environmental filtering (Knapp et al., 2008; Webb et al., 2002; but see Godoy, Kraft & Levine, 2014). As already mentioned, communities with high CAD are dominated by taxa that tolerate hydric stress or are adapted to recurrent grazing by natural herbivores. In the grassland, clades that are more sensitive to drought or that are heavily affected by livestock grazing may be filtered out from the community, driving the reduction in phylogenetic diversity. In the tropical dry forest, CAD intensity was much milder than in the grassland. For this reason, it may not be strong enough to function as an environmental filter and the most disturbed sites retain roughly the same phylogenetic diversity as pristine ones. Other studies in tropical forests report only marginal reductions in phylogenetic diversity with anthropogenic disturbances (Ribeiro et al., 2016). This may be the case in our system as well, as phylogenetic signal also seemed to be mild and thus we would not expect entire groups to be driven to extinction by CAD. Because of phylogenetic-related responses, CAD may favour entire taxa, whereas it may eradicate others from the community. This may or may not result in a loss of species richness (e.g., Knapp et al., 2008; Ribeiro, Arroyo-Rodríguez, Santos, Tabarelli, & Leal, 2015), but it is usually reflected in changes in community composition (e.g., Ribeiro et al., 2016) and a loss of phylogenetic diversity (Helmus et al., 2010; Knapp et al., 2008; Ribeiro et al., 2016). This process may have serious consequences even if species richness is unchanged, because phylogenetically distant species increase the functional diversity of the community (Maherali & Klironomos, 2007; Srivastava, Cadotte, Macdonald, Marushia, & Mirotchnick, 2012). While the random extinction of some species does not necessarily mean a loss of community functions, the extinction of an entire lineage due to phylogenetic signal in its response to CAD may represent a loss of a functional group, destabilizing the community (Laliberté et al., 2010). In the grassland, rare species seem to make a large contribution to phylogenetic diversity. When data were weighed by abundance, rare species lost importance in the calculations and no change in phylogenetic diversity with disturbance was found. This suggests that reduced phylogenetic diversity was related to the disappearance of rare species in disturbed sites. If rare species are key for ecosystem functioning, as it has been proposed (Lyons, Brigham, Traut, & Schwartz, 2005; Mouillot et al., 2013), then their loss may hinder important ecosystem functions. Phylogenetic signal in the response to disturbance does not imply that we can conserve all the species within a clade using the same strategy. What matters most is the strength of the signal, because it determines the validity of extrapolations to whole clades. As in previous works (Brunbjerg et al., 2012; Fritz & Purvis, 2010) we found only weak phylogenetic signal in the responses to CAD, casting doubts on the validity of generalizations. As mentioned earlier, it seems that only within genera phylogenetic signal is sufficiently strong for valid extrapolation, and even then there may be exceptions. Thus, management plans for whole genera may be an aid in establishing a conservation strategy when no information is available on a species or if urgent decision-making is required; however, caution is recommended and such a strategy should only be applied pending detailed monitoring. Our results show that there is a basis for generalizations in management for entire taxa, as it has been done in the past, but also casts doubts on the validity of the extrapolation. In many cases, the generalizations have been made to families or even orders (Ortega-Baes et al., 2010; Pillon & Chase, 2007; Wade, 1998), for which the margin of error seems too high. Furthermore, the taxonomic level that explains more variation may depend on the community studied: in our grassland it was order, but in the tropical dry forest it was the family. For the time being, we would suggest that generalizations should be limited to species in the same genus. Another problem with generalizations is that phylogenetic signal was found only for few indices. For example, it is not necessarily appropriate to prescribe high stocking rates to preserve a taxon that has large values if there is no phylogenetic signal in (I) values. Such strategy would be problematic because species with small (I) values would not tolerate the high grazing intensities. Therefore, extrapolation to entire taxa may only be valid when there is strong phylogenetic signal in many disturbance-response traits. A detailed understanding of the phylogenetic signal in responses to human activities may provide key information in conservation biology. Detecting phylogenetic signal is a first step towards identifying clades that are favoured or hindered by disturbance, and why. This was for instance the case of groups of species in this study that cope with aridity and tolerate high CAD intensities, and thus can be conserved in the presence of livestock. Quantitative measurements of phylogenetic signal are also required to determine whether extrapolations are safe or should be taken with a grain of salt. Understanding phylogenetic signal may also provide insights on how humans affect communities and ecosystem function to make informed decisions for effective management. PAPIIT-UNAM projects IN225511 and IN220514 funded this research. D. García-Meza, J. Belmont, L.I. Cabrera, F. Herce, M. Martínez-López, L.F.V.V. Boullosa and V. Zepeda helped in the field and lab. S. Magallón, P. Dávila and S. Solórzano provided valuable comments. This work is presented in partial fulfilment towards AMB doctoral degree in the Posgrado en Ciencias Biológicas at UNAM. Agradecemos a la comunidad de Concepción Buenavista por su amistad y apoyo. A.M.-B. and C.M. conceived the research, with suggestions from H.P. and G.A.S. G.A.S. designed the protocols for producing molecular data and reconstructing phylogenies. A.M.-B. performed all the analyses. A.M.B. and C.M. wrote the first drafts and all authors contributed to the final version. Data available from the Dryad Digital Repository https://doi.org/10.5061/dryad.6ck1816 (Martínez-Blancas, Paz, Salazar, & Martorell, 2018). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article." @default.
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• W2808096154 title "Related plant species respond similarly to chronic anthropogenic disturbance: Implications for conservation decision-making" @default.
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