Canran Liu, Graeme Newell, Matt White, Josephine Machunter
Species distribution models (SDMs) underpin a wide range of decisions concerning biodiversity. Although SDMs can be built using presence-only data, rigorous evaluation of these models remains challenging. One evaluation method is the Boyce index (BI), which uses the relative frequencies between presence sites and background sites within a series of bins or moving windows spanning the entire range of predicted values from the SDM. Obtaining accurate estimates of the BI using these methods relies upon having a large number of presences, which is often not feasible, particularly for rare or restricted species that are often the focus of modelling. Wider application of the BI requires a method that can accurately and reliably estimate the BI using small numbers of presence records. In this study, we investigated the effectiveness of five statistical smoothing methods (i.e. thin plate regression splines, cubic regression splines, B-splines, P-splines and adaptive smoothers) and the mean of these five methods (denoted as ‘mean') to estimate the BI. We simulated 600 species with varying prevalence and built distribution models using random forest and Maxent methods. For training data, we used two levels for the number of presences (NPtrain: 20 and 500), along with 2 × NPtrain and 10000 random points (i.e. random background sites) for each modelling method. We used the number of presences at four levels (NPbi: 1000, 200, 50 and 10) to investigate its effect, together with 5000 random points to calculate the BI. Our results indicate that the BI estimates from the binning and moving window methods are severely affected by the decrease of NPbi, but all the estimates of the BI from smoothing-based methods were almost always unbiased for realistic situations. Hence, we recommend these methods for estimating the BI for evaluating SDMs when verified absence data are unavailable.
{"title":"Improving the estimation of the Boyce index using statistical smoothing methods for evaluating species distribution models with presence-only data","authors":"Canran Liu, Graeme Newell, Matt White, Josephine Machunter","doi":"10.1111/ecog.07218","DOIUrl":"https://doi.org/10.1111/ecog.07218","url":null,"abstract":"Species distribution models (SDMs) underpin a wide range of decisions concerning biodiversity. Although SDMs can be built using presence-only data, rigorous evaluation of these models remains challenging. One evaluation method is the Boyce index (BI), which uses the relative frequencies between presence sites and background sites within a series of bins or moving windows spanning the entire range of predicted values from the SDM. Obtaining accurate estimates of the BI using these methods relies upon having a large number of presences, which is often not feasible, particularly for rare or restricted species that are often the focus of modelling. Wider application of the BI requires a method that can accurately and reliably estimate the BI using small numbers of presence records. In this study, we investigated the effectiveness of five statistical smoothing methods (i.e. thin plate regression splines, cubic regression splines, B-splines, P-splines and adaptive smoothers) and the mean of these five methods (denoted as ‘mean') to estimate the BI. We simulated 600 species with varying prevalence and built distribution models using random forest and Maxent methods. For training data, we used two levels for the number of presences (NP<sub>train</sub>: 20 and 500), along with 2 × NP<sub>train</sub> and 10000 random points (i.e. random background sites) for each modelling method. We used the number of presences at four levels (NP<sub>bi</sub>: 1000, 200, 50 and 10) to investigate its effect, together with 5000 random points to calculate the BI. Our results indicate that the BI estimates from the binning and moving window methods are severely affected by the decrease of NP<sub>bi</sub>, but all the estimates of the BI from smoothing-based methods were almost always unbiased for realistic situations. Hence, we recommend these methods for estimating the BI for evaluating SDMs when verified absence data are unavailable.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"231 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anticipating the effects of global change on biodiversity has become a global challenge requiring new methods. Approaches like species distribution models have limitations which have fueled the development of joint species distribution models (JSDMs). However, JSDMs rely on systematic surveys community data, and no assessment has been made of their suitability with unstructured opportunistic databases data. We used hierarchical modeling of species communities (HMSC) to test JSDMs performance when using opportunistic databases. Using artificial data that mimic the limitations of such databases by subsampling complete co-occurrence matrices (i.e. original data), we analysed how the completeness of opportunistic databases affects JSDMs regarding 1) the role of independent variables on species occurrence, 2) residual species co-occurrence (as a proxy of biotic interactions) and 3) species distributions. Moreover, we illustrate how to evaluate completeness at the pixel level of real data with a study case of forest tree species in Europe, and evaluate the role of data completeness in model estimation. Our results with artificial data demonstrate that decreasing the completion percentage (the rate of original data presences represented in the subsampled matrices) increases false negatives and negative co-occurrence probabilities, resulting in a loss of ecological information. However, HMSC tolerates different levels of degradation depending on the model aspect being considered. Models with 50% of missing data are valid for estimating species niches and distribution, but interaction matrices require databases with at least 75% of completion data. Furthermore, HMSC's predictions often resemble the original community data (without false negatives) even more than the subsampled data (with false negatives) in the training dataset. These findings were confirmed with the real study case. We conclude that opportunistic databases are a valuable resource for JSDMs, but require an analysis of data completeness for the target taxa in the study area at the spatial resolution of interest.
{"title":"Should we exploit opportunistic databases with joint species distribution models? Artificial and real data suggest it depends on the sampling completeness","authors":"Daniel Romera-Romera, Diego Nieto-Lugilde","doi":"10.1111/ecog.07340","DOIUrl":"https://doi.org/10.1111/ecog.07340","url":null,"abstract":"Anticipating the effects of global change on biodiversity has become a global challenge requiring new methods. Approaches like species distribution models have limitations which have fueled the development of joint species distribution models (JSDMs). However, JSDMs rely on systematic surveys community data, and no assessment has been made of their suitability with unstructured opportunistic databases data. We used hierarchical modeling of species communities (HMSC) to test JSDMs performance when using opportunistic databases. Using artificial data that mimic the limitations of such databases by subsampling complete co-occurrence matrices (i.e. original data), we analysed how the completeness of opportunistic databases affects JSDMs regarding 1) the role of independent variables on species occurrence, 2) residual species co-occurrence (as a proxy of biotic interactions) and 3) species distributions. Moreover, we illustrate how to evaluate completeness at the pixel level of real data with a study case of forest tree species in Europe, and evaluate the role of data completeness in model estimation. Our results with artificial data demonstrate that decreasing the completion percentage (the rate of original data presences represented in the subsampled matrices) increases false negatives and negative co-occurrence probabilities, resulting in a loss of ecological information. However, HMSC tolerates different levels of degradation depending on the model aspect being considered. Models with 50% of missing data are valid for estimating species niches and distribution, but interaction matrices require databases with at least 75% of completion data. Furthermore, HMSC's predictions often resemble the original community data (without false negatives) even more than the subsampled data (with false negatives) in the training dataset. These findings were confirmed with the real study case. We conclude that opportunistic databases are a valuable resource for JSDMs, but require an analysis of data completeness for the target taxa in the study area at the spatial resolution of interest.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"93 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Norma Alicia Hernández Hernández, Ángel Luis Robles Fernández, Nathan Upham
Genetic variation among populations is reflected in biogeographic patterns for many species, but general rules of spatial genetic variation have not been established. In this paper, we establish a theoretical framework based on projecting environmental Grinellian niches back through time to relate the present geographic distribution of population genetic structure to a given species' historical evolutionary context. Thanks to advances in next-generation sequencing technologies, as well as more accurate climate models and the amassing of information stored in biological collections, it is possible to implement this theoretical framework directly. We develop a case study of the tassel-eared squirrel Sciurus aberti to jointly analyze spatial, environmental, and genetic data to predict the historical endemic area of this species. Our results reveal that in cases of genetic isolation by geographic distance, the prevalence of environmental suitability over time corresponds to the genetic fixation index (Fst) of populations with respect to a source population. Populations closer to the historical endemic area show higher genetic diversity and a lower Fst value. This empirical example relates back to the theoretical framework, allowing two further advances: 1) a layer of biogeographic explanation for the results obtained from population genomic methods; and 2) predictive maps of this genetic structure to support biodiversity conservation efforts. Overall, this work advances a perspective that integrates population genetics with historical patterns of species distribution. The limitations posed in the theoretical framework should be considered before implementing the suitability prevalence area (SPA) in a general way over different taxa. Otherwise, the predictability of the genetic diversity of populations as a product of environmental stability over time may not be adequate.
{"title":"Environmental suitability throughout the late quaternary explains population genetic diversity","authors":"Norma Alicia Hernández Hernández, Ángel Luis Robles Fernández, Nathan Upham","doi":"10.1111/ecog.07202","DOIUrl":"https://doi.org/10.1111/ecog.07202","url":null,"abstract":"Genetic variation among populations is reflected in biogeographic patterns for many species, but general rules of spatial genetic variation have not been established. In this paper, we establish a theoretical framework based on projecting environmental Grinellian niches back through time to relate the present geographic distribution of population genetic structure to a given species' historical evolutionary context. Thanks to advances in next-generation sequencing technologies, as well as more accurate climate models and the amassing of information stored in biological collections, it is possible to implement this theoretical framework directly. We develop a case study of the tassel-eared squirrel <i>Sciurus aberti</i> to jointly analyze spatial, environmental, and genetic data to predict the historical endemic area of this species. Our results reveal that in cases of genetic isolation by geographic distance, the prevalence of environmental suitability over time corresponds to the genetic fixation index (<i>F</i><sub>st</sub>) of populations with respect to a source population. Populations closer to the historical endemic area show higher genetic diversity and a lower <i>F</i><sub>st</sub> value. This empirical example relates back to the theoretical framework, allowing two further advances: 1) a layer of biogeographic explanation for the results obtained from population genomic methods; and 2) predictive maps of this genetic structure to support biodiversity conservation efforts. Overall, this work advances a perspective that integrates population genetics with historical patterns of species distribution. The limitations posed in the theoretical framework should be considered before implementing the suitability prevalence area (SPA) in a general way over different taxa. Otherwise, the predictability of the genetic diversity of populations as a product of environmental stability over time may not be adequate.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"64 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142405059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Henry H. Hansen, Eva Bergman, Keller Kopf, Max Lindmark
Climate change-induced extreme weather and related drought and flood conditions are heterogeneous across space and time. The variability in location, timing, and magnitude of rainfall can alter how species respond to the drought and flood disturbances. To further complicate this matter, when droughts end they are often followed by extreme flooding, which are rarely considered as a disturbance (Humphries et al. 2024), let alone assessed with its own heterogeneity. Consequently, it is difficult to quantify impacts on ecological communities across large spatiotemporal scales without considering flood-drought disturbance characteristics in sequence (Burton et al. 2020). We hypothesized that native organisms have evolved resistance to withstand repeated cycles of drought-flood disturbances, and that established non-native species have adapted to persist in novel conditions. To test this, we fit spatiotemporal models of species occurrence with local rainfall patterns as covariates in the drought and flood impacted Murray-Darling basin in Australia during the decade long Millenium Drought, and its recovery period. During these drought conditions, river-floodplain organisms in the Murray-Darling became localized in refugia that limited longitudinal and lateral connectivity (Bond et al. 2008), and following flooding the same organisms were exposed to dispersal and recruitment opportunities (Humphries et al. 2020), as well as to hypoxic blackwater events that lead to the mortality of aquatic organisms (Small et al. 2014). At the basin-scale we found that the range size of most native and non-native fishes were highly resistant to the extreme drought and post-flood conditions. At local scales, species richness, or detection, actually increased under drought conditions. Both findings highlight the resistance of species to climate change driven extreme weather, which opens new questions on community adaptations.
{"title":"Resistance of Australian fish communities to drought and flood: implications for climate change and adaptations","authors":"Henry H. Hansen, Eva Bergman, Keller Kopf, Max Lindmark","doi":"10.1111/ecog.07442","DOIUrl":"https://doi.org/10.1111/ecog.07442","url":null,"abstract":"<b>Climate change-induced extreme weather and related drought and flood conditions are heterogeneous across space and time. The variability in location, timing, and magnitude of rainfall can alter how species respond to the drought and flood disturbances. To further complicate this matter, when droughts end they are often followed by extreme flooding, which are rarely considered as a disturbance (Humphries et al. 2024), let alone assessed with its own heterogeneity. Consequently, it is difficult to quantify impacts on ecological communities across large spatiotemporal scales without considering flood-drought disturbance characteristics in sequence (Burton et al. 2020). We hypothesized that native organisms have evolved resistance to withstand repeated cycles of drought-flood disturbances, and that established non-native species have adapted to persist in novel conditions. To test this, we fit spatiotemporal models of species occurrence with local rainfall patterns as covariates in the drought and flood impacted Murray-Darling basin in Australia during the decade long Millenium Drought, and its recovery period. During these drought conditions, river-floodplain organisms in the Murray-Darling became localized in refugia that limited longitudinal and lateral connectivity (Bond et al. 2008), and following flooding the same organisms were exposed to dispersal and recruitment opportunities (Humphries et al. 2020), as well as to hypoxic blackwater events that lead to the mortality of aquatic organisms (Small et al. 2014). At the basin-scale we found that the range size of most native and non-native fishes were highly resistant to the extreme drought and post-flood conditions. At local scales, species richness, or detection, actually increased under drought conditions. Both findings highlight the resistance of species to climate change driven extreme weather, which opens new questions on community adaptations.</b>","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"2 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jean-François Guégan, Timothée Poisot, Barbara A. Han, Jesús Olivero
<p>Over the past 15 years, disease ecology has become a discipline in its own right. It is fundamentally based on training in ecology and evolution, with solid theoretical foundations and skills in computational biology and statistics, and it differs from a medical approach to the interpretation of disease. It is concerned with how species interactions, including host–pathogen relationships and environmental conditions (e.g. temperature and rainfall), affect patterns and processes of disease presence and spread, how pathogens impact host individuals, populations, communities, and ultimately ecosystem function (Ostfeld et al. <span>2008</span>). Initially rooted in parasite ecology, particularly among researchers working on transmission cycles and host–disease population dynamics, disease ecology mainly focuses on parasitic and infectious diseases but is not exclusive to them (Ostfeld <span>2018</span>). A booming subfield currently concerns research linking different areas such as infectious transmission, agriculture development, and development aid policies, notably in the world's poorest countries (Ngonghala et al. <span>2014</span>). Unlike ecologists, disease ecologists focus on understanding the causes and consequences of the maintenance and transmission of pathogens in animal species, including humans, plants, and communities of species. It has become much more widespread in studies among wild animal species, also in their contacts with domestic species, e.g. livestock, and their interactions with human populations, and much less so in plant diseases and their transmission, which in some respects are the focus of more plant-pathology molecular-orientated research (Guégan et al. <span>2023a</span>). We cannot say that the development of disease ecology has involved the gradual integration of several distinct lines of inquiry because it is the heir of ecology. It has an ecosystem-based approach and takes into account natural complexity (Johnson et al. <span>2015</span>, Hassell et al. <span>2021</span>, Petrone et al. <span>2023</span>); it develops experimental methods in the laboratory or mesocosms and has an essential background in statistical and mathematical analysis. The spatial scales of disease ecology study are experimental or local and, depending on the questions posed, can extend to the most global scales (Guernier et al. <span>2004</span>, Jones et al. <span>2008</span>, Allen et al. <span>2017</span>, Carlson et al. <span>2022</span>). In the temporal domain, these can be daily or weekly studies or multi-decadal investigations, such as in disease population dynamics (Keeling and Rohani <span>2007</span>). By definition, disease ecology is concerned with understanding patterns and processes on large spatial and temporal scales. It integrates different levels of life organization, i.e. from genes to the global ecosystem, which is not the case, or only to a limited extent, of medical and veterinary approaches (Ezenwa et al. <span>20
{"title":"Disease ecology and pathogeography: Changing the focus to better interpret and anticipate complex environment–host–pathogen interactions","authors":"Jean-François Guégan, Timothée Poisot, Barbara A. Han, Jesús Olivero","doi":"10.1111/ecog.07684","DOIUrl":"10.1111/ecog.07684","url":null,"abstract":"<p>Over the past 15 years, disease ecology has become a discipline in its own right. It is fundamentally based on training in ecology and evolution, with solid theoretical foundations and skills in computational biology and statistics, and it differs from a medical approach to the interpretation of disease. It is concerned with how species interactions, including host–pathogen relationships and environmental conditions (e.g. temperature and rainfall), affect patterns and processes of disease presence and spread, how pathogens impact host individuals, populations, communities, and ultimately ecosystem function (Ostfeld et al. <span>2008</span>). Initially rooted in parasite ecology, particularly among researchers working on transmission cycles and host–disease population dynamics, disease ecology mainly focuses on parasitic and infectious diseases but is not exclusive to them (Ostfeld <span>2018</span>). A booming subfield currently concerns research linking different areas such as infectious transmission, agriculture development, and development aid policies, notably in the world's poorest countries (Ngonghala et al. <span>2014</span>). Unlike ecologists, disease ecologists focus on understanding the causes and consequences of the maintenance and transmission of pathogens in animal species, including humans, plants, and communities of species. It has become much more widespread in studies among wild animal species, also in their contacts with domestic species, e.g. livestock, and their interactions with human populations, and much less so in plant diseases and their transmission, which in some respects are the focus of more plant-pathology molecular-orientated research (Guégan et al. <span>2023a</span>). We cannot say that the development of disease ecology has involved the gradual integration of several distinct lines of inquiry because it is the heir of ecology. It has an ecosystem-based approach and takes into account natural complexity (Johnson et al. <span>2015</span>, Hassell et al. <span>2021</span>, Petrone et al. <span>2023</span>); it develops experimental methods in the laboratory or mesocosms and has an essential background in statistical and mathematical analysis. The spatial scales of disease ecology study are experimental or local and, depending on the questions posed, can extend to the most global scales (Guernier et al. <span>2004</span>, Jones et al. <span>2008</span>, Allen et al. <span>2017</span>, Carlson et al. <span>2022</span>). In the temporal domain, these can be daily or weekly studies or multi-decadal investigations, such as in disease population dynamics (Keeling and Rohani <span>2007</span>). By definition, disease ecology is concerned with understanding patterns and processes on large spatial and temporal scales. It integrates different levels of life organization, i.e. from genes to the global ecosystem, which is not the case, or only to a limited extent, of medical and veterinary approaches (Ezenwa et al. <span>20","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"2024 10","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ecog.07684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142385435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Suárez, Paula Arribas, Amrita Srivathsan, Rudolf Meier, Brent C. Emerson
The open ecosystem (e.g. grasslands, prairies, shrublands) tends to be ecologically less stable than closed one (i.e. forests) and encompassess higher spatial heterogeneity in terms of environmental diversity. Such differences are expected to differentially constrain the diversity and structure of the communities that inhabit each of them, but identifying the specific processes driving contrasting biodiversity patterns between open and closed systems is challenging. In order to understand how environmental variability might structure spider assemblages, both between and within open and closed ecosystems, we implement a high throughput multiplex barcode sequencing approach to generate a dataset for 8585 specimens representing 168 species, across the open ecosystem within the Canary Islands. Combining these with spider sequences from the closed ecosystem within the same islands, we show that spider communities in the open ecosystem show higher species richness, higher beta diversity, and higher proportions of rare species but proportionately lower numbers of endemic species than communities in the closed ecosystem. We furthermore assess if environmental heterogeneity and habitat stability are the major drivers of such differences by assessing spatial genetic structuring and the influence of bioclimatic variables. Our results point to environmental heterogeneity rather than stability as a major driver of spatial patterns between open and closed ecosystems.
{"title":"Environmental heterogeneity, rather than stability, explains spider assemblage differences between ecosystems","authors":"Daniel Suárez, Paula Arribas, Amrita Srivathsan, Rudolf Meier, Brent C. Emerson","doi":"10.1111/ecog.07454","DOIUrl":"https://doi.org/10.1111/ecog.07454","url":null,"abstract":"The open ecosystem (e.g. grasslands, prairies, shrublands) tends to be ecologically less stable than closed one (i.e. forests) and encompassess higher spatial heterogeneity in terms of environmental diversity. Such differences are expected to differentially constrain the diversity and structure of the communities that inhabit each of them, but identifying the specific processes driving contrasting biodiversity patterns between open and closed systems is challenging. In order to understand how environmental variability might structure spider assemblages, both between and within open and closed ecosystems, we implement a high throughput multiplex barcode sequencing approach to generate a dataset for 8585 specimens representing 168 species, across the open ecosystem within the Canary Islands. Combining these with spider sequences from the closed ecosystem within the same islands, we show that spider communities in the open ecosystem show higher species richness, higher beta diversity, and higher proportions of rare species but proportionately lower numbers of endemic species than communities in the closed ecosystem. We furthermore assess if environmental heterogeneity and habitat stability are the major drivers of such differences by assessing spatial genetic structuring and the influence of bioclimatic variables. Our results point to environmental heterogeneity rather than stability as a major driver of spatial patterns between open and closed ecosystems.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"34 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benoit Durieu, Valentina Savaglia, Yannick Lara, Alexandre Lambion, Igor S. Pessi, Wim Vyverman, Elie Verleyen, Annick Wilmotte
The Antarctic terrestrial macrobiota are highly endemic and biogeographically structured, but whether this also holds true for microbial groups remains poorly understood. We studied the biogeographic patterns of Antarctic cyanobacteria from benthic microbial mats sampled in 84 lakes from two sub‐Antarctic islands, as well as from eight Antarctic Conservation Biogeographic Regions (ACBRs) which were previously defined based mainly on macroscopic taxa. Analysis of 16S rRNA gene sequences revealed that Antarctic and sub‐Antarctic lakes host significantly different cyanobacterial communities, yet that the bioregionalization pattern did not correspond to the division into ACBRs. Both Antarctic and sub‐Antarctic lakes contain a high number of potentially endemic taxa (41% of the total diversity), of which 33.3% attain a relative abundance of < 1%. Our findings highlight the uniqueness of Antarctic microbiota and the need for increased protection of inland waters in both Antarctica and the sub‐Antarctic islands.
{"title":"(Sub‐)Antarctic endemic cyanobacteria from benthic mats are rare and have restricted geographic distributions","authors":"Benoit Durieu, Valentina Savaglia, Yannick Lara, Alexandre Lambion, Igor S. Pessi, Wim Vyverman, Elie Verleyen, Annick Wilmotte","doi":"10.1111/ecog.07489","DOIUrl":"https://doi.org/10.1111/ecog.07489","url":null,"abstract":"The Antarctic terrestrial macrobiota are highly endemic and biogeographically structured, but whether this also holds true for microbial groups remains poorly understood. We studied the biogeographic patterns of Antarctic cyanobacteria from benthic microbial mats sampled in 84 lakes from two sub‐Antarctic islands, as well as from eight Antarctic Conservation Biogeographic Regions (ACBRs) which were previously defined based mainly on macroscopic taxa. Analysis of 16S rRNA gene sequences revealed that Antarctic and sub‐Antarctic lakes host significantly different cyanobacterial communities, yet that the bioregionalization pattern did not correspond to the division into ACBRs. Both Antarctic and sub‐Antarctic lakes contain a high number of potentially endemic taxa (41% of the total diversity), of which 33.3% attain a relative abundance of < 1%. Our findings highlight the uniqueness of Antarctic microbiota and the need for increased protection of inland waters in both Antarctica and the sub‐Antarctic islands.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"35 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In an era of ongoing biodiversity, it is critical to map biodiversity patterns in space and time for better-informing conservation and management. Species distribution models (SDMs) are widely applied in various types of such biodiversity assessments. Cross-validation represents a prevalent approach to assess the discrimination capacity of a target SDM algorithm and determine its optimal parameters. Several alternative cross-validation methods exist; however, the influence of choosing a specific cross-validation method on SDM performance and predictions remains unresolved. Here, we tested the performance of random versus spatial cross-validation methods for SDM using goatfishes (Actinopteri: Syngnathiformes: Mullidae) as a case study, which are recognized as indicator species for coastal waters. Our results showed that the random versus spatial cross-validation methods resulted in different optimal model parameterizations in 57 out of 60 modeled species. Significant difference existed in predictive performance between the random and spatial cross-validation methods, and the two cross-validation methods yielded different projected present-day spatial distribution and future projection patterns of goatfishes under climate change exposure. Despite the disparity in species distributions, both approaches consistently suggested the Indo-Australian Archipelago as the hotspot of goatfish species richness and also as the most vulnerable area to climate change. Our findings highlight that the choice of cross-validation method is an overlooked source of uncertainty in SDM studies. Meanwhile, the consistency in richness predictions highlights the usefulness of SDMs in marine conservation. These findings emphasize that we should pay special attention to the selection of cross-validation methods in SDM studies.
{"title":"Cross-validation matters in species distribution models: a case study with goatfish species","authors":"Hongwei Huang, Zhixin Zhang, Ákos Bede-Fazekas, Stefano Mammola, Jiqi Gu, Jinxin Zhou, Junmei Qu, Qiang Lin","doi":"10.1111/ecog.07354","DOIUrl":"https://doi.org/10.1111/ecog.07354","url":null,"abstract":"In an era of ongoing biodiversity, it is critical to map biodiversity patterns in space and time for better-informing conservation and management. Species distribution models (SDMs) are widely applied in various types of such biodiversity assessments. Cross-validation represents a prevalent approach to assess the discrimination capacity of a target SDM algorithm and determine its optimal parameters. Several alternative cross-validation methods exist; however, the influence of choosing a specific cross-validation method on SDM performance and predictions remains unresolved. Here, we tested the performance of random versus spatial cross-validation methods for SDM using goatfishes (Actinopteri: Syngnathiformes: Mullidae) as a case study, which are recognized as indicator species for coastal waters. Our results showed that the random versus spatial cross-validation methods resulted in different optimal model parameterizations in 57 out of 60 modeled species. Significant difference existed in predictive performance between the random and spatial cross-validation methods, and the two cross-validation methods yielded different projected present-day spatial distribution and future projection patterns of goatfishes under climate change exposure. Despite the disparity in species distributions, both approaches consistently suggested the Indo-Australian Archipelago as the hotspot of goatfish species richness and also as the most vulnerable area to climate change. Our findings highlight that the choice of cross-validation method is an overlooked source of uncertainty in SDM studies. Meanwhile, the consistency in richness predictions highlights the usefulness of SDMs in marine conservation. These findings emphasize that we should pay special attention to the selection of cross-validation methods in SDM studies.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"41 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jakob Thyrring, Lloyd S. Peck, Mikael K. Sejr, Jan Marcin Węsławski, Christopher D. G. Harley, André Menegotto
The availability of online biodiversity data has increased in recent decades, aiding our understanding of diversity patterns and species richness–environment relationships across temporal and spatial scales. However, even the most exhaustive databases are prone to sampling biases, which create knowledge gaps in species distributions and increase uncertainty in model predictions. Regarding marine environments, intertidal zones are globally distributed and considered early warning systems for climate change impacts and species' range shifts. Owing to their relative accessibility, intertidal records should – supposedly – be less incomplete and biased compared to open-ocean and deep-sea areas. Yet, the extent and coverage of intertidal records available in global biodiversity databases remains unknown. In this study, we used a high-resolution worldwide tidal flat map to identify intertidal records of 11 563 benthic species from the OBIS (Ocean Biodiversity Information System) portal. Following a thorough data-cleaning process, we evaluated geographic patterns in observed species richness, site accessibility, sampling effort, and inventory completeness across latitudes. We demonstrate that observed species richness has mid-latitudinal peaks while the tropics accumulate species with missing records, similar to patterns described for the entire marine realm. These patterns correlate with disproportionate mid-latitude sampling efforts and poor tropical sampling coverage. Sixty-five percent of the mapped intertidal sites are located within 3 hours of a city, but sampling records remain almost absent along African Atlantic, South American Pacific, and Indo-Pacific coasts. Thus, even for the accessible and well-studied intertidal shorelines, database records are not free from geographical biases and their associated implications for biodiversity estimates. Our results highlight the need for a better data-sharing culture, and we hope to encourage initiatives promoting more and better-distributed research efforts on intertidal biodiversity, which could improve global scale detection and prediction of climate change impacts at regional and global scales.
{"title":"Shallow coverage in shallow waters: the incompleteness of intertidal species inventories in biodiversity database records","authors":"Jakob Thyrring, Lloyd S. Peck, Mikael K. Sejr, Jan Marcin Węsławski, Christopher D. G. Harley, André Menegotto","doi":"10.1111/ecog.07006","DOIUrl":"10.1111/ecog.07006","url":null,"abstract":"<p>The availability of online biodiversity data has increased in recent decades, aiding our understanding of diversity patterns and species richness–environment relationships across temporal and spatial scales. However, even the most exhaustive databases are prone to sampling biases, which create knowledge gaps in species distributions and increase uncertainty in model predictions. Regarding marine environments, intertidal zones are globally distributed and considered early warning systems for climate change impacts and species' range shifts. Owing to their relative accessibility, intertidal records should – supposedly – be less incomplete and biased compared to open-ocean and deep-sea areas. Yet, the extent and coverage of intertidal records available in global biodiversity databases remains unknown. In this study, we used a high-resolution worldwide tidal flat map to identify intertidal records of 11 563 benthic species from the OBIS (Ocean Biodiversity Information System) portal. Following a thorough data-cleaning process, we evaluated geographic patterns in observed species richness, site accessibility, sampling effort, and inventory completeness across latitudes. We demonstrate that observed species richness has mid-latitudinal peaks while the tropics accumulate species with missing records, similar to patterns described for the entire marine realm. These patterns correlate with disproportionate mid-latitude sampling efforts and poor tropical sampling coverage. Sixty-five percent of the mapped intertidal sites are located within 3 hours of a city, but sampling records remain almost absent along African Atlantic, South American Pacific, and Indo-Pacific coasts. Thus, even for the accessible and well-studied intertidal shorelines, database records are not free from geographical biases and their associated implications for biodiversity estimates. Our results highlight the need for a better data-sharing culture, and we hope to encourage initiatives promoting more and better-distributed research efforts on intertidal biodiversity, which could improve global scale detection and prediction of climate change impacts at regional and global scales.</p>","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"2024 12","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ecog.07006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142166640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In migratory insects performing multigenerational migration, such as the painted lady butterfly Vanessa cardui, successive generations face a wide variety of predator communities and may be subject to different predation risks. Here, we analyze the pattern of wing damage of over 2000 butterflies to investigate, for the first time, the risk of predation of adult painted ladies across a latitudinal range of ca 3500 km extending from the northern Mediterranean through the Maghreb to sub‐Saharan West Africa. Large number of butterflies showed substantial wing damage attributable to failed attacks, with birds, mantids and lizards being the most likely predators. The risk of attack increased towards the equator, even after controlling for wing wear. In addition, there was a strong effect of butterfly size on predation risk, with larger butterflies facing a higher risk compared to their smaller counterparts, and clear evidence that females suffered more attacks than males. Although size is a major factor, latitude was a stronger predictor of predation risk across the migratory system, as evidenced by greater wing damage in butterflies at lower latitudes, even though their size notably decreased. These results raise an interesting evolutionary conflict, with a tradeoff between size and predation risk, as larger butterflies are likely to be more fecund and efficient in migratory flight but, at the same time, more vulnerable to predation.
{"title":"Predation risk in a migratory butterfly increases southward along a latitudinal gradient","authors":"Constanti Stefanescu, Clàudia Pla‐Narbona, Andreu Ubach, Crinan Jarrett, Justinn Renelies‐Hamilton, Pau Colom","doi":"10.1111/ecog.07308","DOIUrl":"https://doi.org/10.1111/ecog.07308","url":null,"abstract":"In migratory insects performing multigenerational migration, such as the painted lady butterfly <jats:italic>Vanessa cardui</jats:italic>, successive generations face a wide variety of predator communities and may be subject to different predation risks. Here, we analyze the pattern of wing damage of over 2000 butterflies to investigate, for the first time, the risk of predation of adult painted ladies across a latitudinal range of ca 3500 km extending from the northern Mediterranean through the Maghreb to sub‐Saharan West Africa. Large number of butterflies showed substantial wing damage attributable to failed attacks, with birds, mantids and lizards being the most likely predators. The risk of attack increased towards the equator, even after controlling for wing wear. In addition, there was a strong effect of butterfly size on predation risk, with larger butterflies facing a higher risk compared to their smaller counterparts, and clear evidence that females suffered more attacks than males. Although size is a major factor, latitude was a stronger predictor of predation risk across the migratory system, as evidenced by greater wing damage in butterflies at lower latitudes, even though their size notably decreased. These results raise an interesting evolutionary conflict, with a tradeoff between size and predation risk, as larger butterflies are likely to be more fecund and efficient in migratory flight but, at the same time, more vulnerable to predation.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"10 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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