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19 results for “biodiversity trends”
Long‐term trends in gastropod abundance and biodiversity: Disentangling effects of press versus pulse disturbances
<p><strong>Aim</strong>: Climate-induced pulse (e.g., hurricanes) and press (e.g., global warming) disturbances represent threats to populations, communities, and the ecosystem services that they provide. We leveraged three decades of annual data on tropical gastropods to quantify the effects of major hurricanes, associated secondary succession, and global warming on abundance, biodiversity, and species composition.</p> <p><strong>Location</strong>: Luquillo Mountains, Puerto Rico.</p> <p><strong>Methods</strong>: Gastropod abundance, biodiversity, and composition were estimated annually for each of 27 years in a tropical montane forest that experienced three major hurricanes (Hugo, Georges, and Maria). Generalized linear mixed-effects, linear mixed-effects, and linear models evaluated population- and community-level responses to year, ambient temperature, understory temperature, hurricane, and time since hurricane. Variation partitioning determined the unique and shared variation in biotic responses associated with temperature, disturbance, and succession.</p> <p><strong>Results</strong>: Rather than declining, gastropod abundances generally increased through time, whereas the responses of biodiversity were weak and scale dependent. Hurricanes and associated secondary succession, rather than ambient atmospheric temperature, molded long-term trends in abundances and biodiversity.</p> <p><strong>Main conclusions</strong>: Global warming over the past 30 years has not progressed sufficiently to elicit significant responses by gastropods in the Luquillo Mountains. Rather, effects from pulse disturbances (i.e., hurricanes) and secondary succession currently drive long-term variation in abundance and biodiversity. Gastropods evince high resilience in this tropical ecosystem. Historical exposure to recurrent hurricanes likely imbued the fauna with broad niches that make them resistant to current levels of global warming. We predict that biotic resiliency will be challenged once changes in temperature exceed interannual and inter-habitat differences that typify this hurricane-mediated system, or combine with an increased frequency of hurricanes and droughts to alter associations among environmental characteristics that define the fundamental niches of species. Only then might significant declines in abundance or the appearance of novel communities characterize the gastropod fauna in the Luquillo Mountains.</p>
Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Long‐term trends in gastropod abundance and biodiversity: Disentangling effects of press versus pulse disturbances
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Data for: Revealing hidden sources of uncertainty in biodiversity trend assessments
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Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900-2050. Data and Code. Project BES-SIM 1.
<p>This archive contains all scripts and data used for analysis and figures for the paper <strong>Pereira et al. (2024). Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900-2050. Science. </strong>The paper is the result of the BES SIM 1 project. </p>
The undetectability of global biodiversity trends using local species richness
<p>Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e., time between original survey and re-survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility), and spatial sampling biases (i.e., site-selection biases). We use PREDICTS model-based estimates as a proxy for the real-world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30-year period, the number of sites for detecting trends doubled for a decade, increased 10-fold within three years, and yearly trends were undetectable. Measurement errors had a non-linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change.</p>
Geographical trends of soil-associated biodiversity changes due to tree plantations in South America: biome and climate constraints revealed through meta-analysis
<p><strong>Aim</strong></p> <p>Evaluate the interaction between climate and biome structure when explaining changes in species richness of soil-associated communities due to tree plantations developed in different biomes. Compare the response of plants, soil invertebrates, and soil microorganisms, and test whether they should be considered sensitive-coupled biotas. Location Continental South America.</p> <p><strong>Time period</strong></p> <p>1996–2023 </p> <p><strong>Major taxa studied </strong></p> <p>Plants, soil invertebrates and soil microorganisms </p> <p><strong>Methods </strong></p> <p>Through a meta-analysis, the change in species richness (i.e., response ratio) associated with tree plantations was evaluated in 127 points of study across South America, considering soil-associated communities of plants, invertebrates and microorganisms. The influence of biome structure (open vs. closed habitats) on the response ratio and its interaction with the actual evapotranspiration (AET) and temperature seasonality was evaluated. Differentiated responses of different taxa were tested by comparing models with and without an interaction term referring to the taxon studied. The regional agricultural cover and plantation age were considered as anthropogenic variables.</p> <p><strong>Results </strong></p> <p>Models containing the AET were better at explaining the trend of change in species richness than those with temperature seasonality. The response to the change in species richness was oppositely related to the AET in open and closed biomes. Plants presented a higher loss in species richness than soil invertebrates and microorganisms. The three taxa were positively associated with AET, while seasonality was not relevant in any case. Both anthropogenic variables significantly lessened the change in species richness in all models. </p> <p><strong>Main conclusions</strong></p> <p>The structural contrast between the anthropogenic habitat and the biome where it is developed is a key factor influencing the response of soil-associated communities to tree plantations. Nevertheless, its influence must be assessed together with climatic and anthropogenic variables given that their interaction can explain different geographical trends in the change in species richness across regions.</p>
The undetectability of global biodiversity trends using local species richness
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Geographical trends of soil-associated biodiversity changes due to tree plantations in South America: biome and climate constraints revealed through meta-analysis
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Data and code used for the paper "Global trends and biases in biodiversity conservation research"
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Data for: ' Idiosyncratic trends of woodland invertebrate biodiversity in Britain over 45 years'
<p>Data for our paper found at doi: 10.1111/icad.12685</p> <p>The zip folder contains R scripts and code to analyse the relationship between species 'woodland association estimates and their long-term distribution trends.</p> <p>Note: the raw occurrence data could not be shared due to existing data sharing agreement. Instead, we share the derived data on species' woodland association estimates.</p>
Data from: The Hawaiian freshwater algae biodiversity survey (2009-2014): systematic and biogeographic trends with an emphasis on the macroalgae
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Data from: Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring
1. Global concern about human impact on biological diversity has triggered an intense research agenda on drivers and consequences of biodiversity change in parallel with international policy seeking to conserve biodiversity and associated ecosystem functions. Quantifying the trends in biodiversity is far from trivial, however, as recently documented by meta-analyses which report little if any net change of local species richness through time. 2. Here, we summarize several limitations of species richness as a metric of biodiversity change and show that the expectation of directional species richness trends under changing conditions is invalid. Instead, we illustrate how a set of species turnover indices provide more information content regarding temporal trends in biodiversity, as they reflect how dominance and identity shift in communities over time. 3. We apply these metrics to three monitoring data sets representing different ecosystem types. In all data sets, nearly complete species turnover occurred, but this was disconnected from any species richness trends. Instead, turnover was strongly influenced by changes in species presence (identities) and dominance (abundances). We further show that these metrics can detect phases of strong compositional shifts in monitoring data and thus identify a different aspect of biodiversity change decoupled from species richness. 4. Synthesis and application: Temporal trends in species richness are insufficient to capture key changes in biodiversity in changing environments. In fact, reductions in environmental quality can lead to transient increases in species richness if immigration or extinction have different temporal dynamics. Thus, biodiversity monitoring programs need to go beyond analyses of trends in richness in favour of more meaningful assessments of biodiversity change.01-Jun-2017
Data from: Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring
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Abbreviations: ILK: indigenous and local knowledge; IPLCs: indigenous peoples and local communities; NBSAPs: national biodiversity strategies and action plans. a Strategic Plan for Biodiversity 2011–2020. Figure 6. Summary of progress towards the Aichi Targets. Scores are based on quantitative analysis of indicators, a systematic review of the literature, fifth National Reports to the CBD, and available information on countries' stated intentions to implement additional actions by 2020. Progress towards target elements is scored as "Good" (substantial positive trends at a global scale relating to in Summary for policymakers of the global assessment report on biodiversity and ecosystem services - unedited advance version
Abbreviations: ILK: indigenous and local knowledge; IPLCs: indigenous peoples and local communities; NBSAPs: national biodiversity strategies and action plans. a Strategic Plan for Biodiversity 2011–2020. Figure 6. Summary of progress towards the Aichi Targets. Scores are based on quantitative analysis of indicators, a systematic review of the literature, fifth National Reports to the CBD, and available information on countries' stated intentions to implement additional actions by 2020. Progress towards target elements is scored as "Good" (substantial positive trends at a global scale relating to
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