Data and R coding used to perform analyses and generate results in the manuscript "The demographic causes of population change vary across four decades in a long-lived shorebird" published in the journal Ecology

Original and derived data products referenced in the original manuscript are provided in the data package.

Description of the data and file structure

Original data:

Table_1_source_papers.csv: Papers that met review criteria and which are summarized in Table 1 of the manuscript.

1. ID: The paper identification number
2. Topic: The broad topic (i.e., each row of Table 1)
3. Authors: The names of the authors of the paper
4. Article Title: The title of the paper
5. Source Title: The name of the journal in which the paper was published
6. Abstract: The paper's abstract, retrieved from the Web of Science search
7. study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.
8. pathogen_broad: Broad classification of the type of pathogen discussed in the paper.
9. transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.
10. pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.
11. country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).

Derived data:

change_livestock_country.csv: A dataframe containing values used to generate Figure 4a in the manuscript.

1. County Name: The name of the county in Kenya
2. Sheep and goats 1980: The estimated number of sheep and goats in 1980
3. Sheep and goats 2016: The estimated number of sheep and goats in 2016
4. pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016
5. Cattle 1980: The estimated number of cattle in 1980
6. Cattle 2016: The estimated number of cattle in 2016
7. pct_change_cattle: The percent change in cattle numbers from 1980 to 2016
8. Camel 1980: The estimated number of camels in 1980
9. Camel 2016: The estimated number of camels in 2016
10. pct_change_camel: The percent change in camel numbers from 1980 to 2016
11. human_pop 1980: The estimated human population in the county in 1980
12. human_pop 2016: The estimated human population in the county in 1980
13. pct_change_human: The percent change in the human population from 1980 to 2016
14. area_sq_km: The land area of the county
15. change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016
16. change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016
17. change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016

country_avg_schist_wormy_world.csv: A dataframe containing values used to generate Figure 3 in the manuscript.

• Country: The country in which the schistosome prevalence studies were performed.
• Latitude: The latitute in decimal degrees
• Longitude: The longitute in decimal degrees
• Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.

kenya_precip_change_1951_2020.csv: A dataframe containing values used to generate Figure 4b in the manuscript.

• Precipitation (mm): Binned annual precipitation values
• 1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period
• 1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period
• 1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period

Sharing/Access information

Data were derived from the following sources:

• Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249

• London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0

• World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections

We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates, indicating the appropriate location and spatial scale of a management action. The data presented here reflect the results from developing a hierarchical monitoring framework and then applying these methods to Greater Sage-grouse in Nevada and Wyoming, US. When using these data for evaluating population changes or when identifying a spatially balanced sampling protocol, all cluster levels are designed to work together and therefore we recommend evaluating multiple cluster levels prior to selecting a single cluster level, if a single scale is desired, when analyzing population growth rates or other analyses, as these data are intended for multi-scale efforts. In other words, let your data decide which scale(s) are appropriate for the given species. These cluster levels are specific to Greater Sage-grouse but they may be appropriate for other sagebrush obligate species, but the user will need to make this determination. The products from this study aim to support multiple research and management needs. However, these data represent an interim data product because there may be errors associated with clusters along the edges of the state boundaries (due to the lack of lek data in neighboring states). We are planning to release new data that we will develop for the Greater sage-grouse range. We recommend using the new data products once available instead of these data products. These data will remain online as they are associated with the following citation, which provides a detailed explanation of the methods used to develop these data: O’Donnell, Michael S., David R. Edmunds, Cameron L. Aldridge, Julie A. Heinrichs, Peter S. Coates, Brian G. Prochazka, and Steve E. Hanser. 2018. Designing hierarchically nested and biologically relevant monitoring frameworks to study populations across scales. Ecosphere

Demographic data for introduced crab from multiple bays along the Central California coast, shallow subtidal (<3 m depth), in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trappings of invasive European green crabs to gather demographic data from several bays in northern California: Bodega Harbor, Tomales Bay, Bolinas Lagoon, San Francisco Bay, and Elkhorn Slough. All sites were accessed by foot via shore entry. At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. Traps arrays were set with fish and minnow traps alternating and with each 20 m apart. Traps were retrieved 24 hours later and traps were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, and injuries were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, and frozen overnight prior to disposal.\u00a0

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Demographic data for introduced crab from multiple bays in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 15 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701751.1 Easternmost_Easting=-121.738422 geospatial_lat_max=38.316968 geospatial_lat_min=36.823953 geospatial_lat_units=degrees_north geospatial_lon_max=-121.738422 geospatial_lon_min=-123.058725 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701751 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. instruments_0_dataset_instrument_nid=701774 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=folding Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701751 Northernmost_Northing=38.316968 param_mapping={'701751': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701751/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=36.823953 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-123.058725 xml_source=osprey2erddap.update_xml() v1.3

Life Table Data: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per year for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Matrix Model Data: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resources in this dataset:Resource Title: Matrix model data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

Successful wildlife conservation in an era of global change requires understanding determinants of species population growth. However, when populations are faced with novel stressors, factors associated with healthy populations can change, necessitating shifting conservation strategies. For example, emerging infectious diseases can cause conditions previously beneficial to host populations to increase disease impacts. Here, we paired a population dataset of 265 colonies of the federally endangered Indiana bat (Myotis sodalis) with 50.7 logger-years of environmental data to explore factors that affected colony response to white-nose syndrome (WNS), an emerging fungal disease. We found variation in colony responses to WNS, ranging from extirpation to stabilization. The severity of WNS impacts was associated with hibernaculum temperature, as colonies of cold hibernacula declined more severely than those in relatively warm hibernacula, an association that arose following pathogen emergence...., , , # Data from: Drivers of population dynamics of at-risk populations change with pathogen arrival

https://doi.org/10.5061/dryad.3xsj3txqb

This dataset contains population census data from 265 colonies of the Indiana bat (Myotis sodalis) impacted by white-nose syndrome. It additionally contains data on the temperature and humidity conditions of their hibernacula, information used to explore dynamic associations between environmental conditions and population response to pathogen invasion.Â

Description of the data and file structure

The data used in the study is provided in a single .csv file entitled "data.csv." It contains yearly census and population growth data for each of the 265 Indiana bat colonies. Below is a description of the data contained in each column:

• Site: a unique name randomly assigned to each hibernaculum containing an Indiana bat colony.Â
• State: U.S. state of colony.
• County: county of colony.
• Wyear: year census...

Bumble bees (Bombus) are vitally important pollinators of wild plants and agricultural crops worldwide. Fragmentary observations, however, have suggested population declines in several North American species. Despite rising concern over these observations in the United States, highlighted in a recent National Academy of Sciences report, a national assessment of the geographic scope and possible causal factors of bumble bee decline is lacking. Here, we report results of a 3-y interdisciplinary study of changing distributions, population genetic structure, and levels of pathogen infection in bumble bee populations across the United States. We compare current and historical distributions of eight species, compiling a database of >73,000 museum records for comparison with data from intensive nationwide surveys of >16,000 specimens. We show that the relative abundances of four species have declined by up to 96% and that their surveyed geographic ranges have contracted by 23–87%, some within the last 20 y. We also show that declining populations have significantly higher infection levels of the microsporidian pathogen Nosema bombi and lower genetic diversity compared with co-occurring populations of the stable (nondeclining) species. Higher pathogen prevalence and reduced genetic diversity are, thus, realistic predictors of these alarming patterns of decline in North America, although cause and effect remain uncertain. Bumble bees (Bombus) are integral wild pollinators within native plant communities throughout temperate ecosystems, and recent domestication has boosted their economic importance in crop pollination to a level surpassed only by the honey bee. Their robust size, long tongues, and buzz-pollination behavior (high-frequency buzzing to release pollen from flowers) significantly increase the efficiency of pollen transfer in multibillion dollar crops such as tomatoes and berries. Disturbing reports of bumble bee population declines in Europe have recently spilled over into North America, fueling environmental and economic concerns of global decline. However, the evidence for large-scale range reductions across North America is lacking. Many reports of decline are unpublished, and the few published studies are limited to independent local surveys in northern California/southern Oregon, Ontario, Canada, and Illinois. Furthermore, causal factors leading to the alleged decline of bumble bee populations in North America remain speculative. One compelling but untested hypothesis for the cause of decline in the United States entails the spread of a putatively introduced pathogen, Nosema bombi, which is an obligate intracellular microsporidian parasite found commonly in bumble bees throughout Europe but largely unstudied in North America. Pathogenic effects of N. bombi may vary depending on the host species and reproductive caste and include reductions in colony growth and individual life span and fitness. Population genetic factors could also play a role in Bombus population decline. For instance, small effective population sizes and reduced gene flow among fragmented habitats can result in losses of genetic diversity with negative consequences, and the detrimental impacts of these genetic factors can be especially intensified in bees. Population genetic studies of Bombus are rare worldwide. A single study in the United States identified lower genetic diversity and elevated genetic differentiation (FST) among Illinois populations of the putatively declining B. pensylvanicus relative to those of a codistributed stable species. Similar patterns have been observed in comparative studies of some European species, but most investigations have been geographically restricted and based on limited sampling within and among populations. Although the investigations to date have provided important information on the increasing rarity of some bumble bee species in local populations, the different survey protocols and limited geographic scope of these studies cannot fully capture the general patterns necessary to evaluate the underlying processes or overall gravity of declines. Furthermore, valid tests of the N. bombi hypothesis and its risk to populations across North America call for data on its geographic distribution and infection prevalence among species. Likewise, testing the general importance of population genetic factors in bumble bee decline requires genetic comparisons derived from sampling of multiple stable and declining populations on a large geographic scale. From such range-wide comparisons, we provide incontrovertible evidence that multiple Bombus species have experienced sharp population declines at the national level. We also show that declining populations are associated with both high N. bombi infection levels and low genetic diversity. This data was used in the paper "Patterns of widespread decline in North American bumble bees" published in the Proceedings of the National Academy of United States of America. For more information about this dataset contact: Sydney A. Cameron: scameron@life.illinois.edu James Strange: James.Strange@ars.usda.gov Resources in this dataset:Resource Title: Data from: Patterns of Widespread Decline in North American Bumble Bees (Data Dictionary). File Name: meta.xmlResource Description: This is an XML data dictionary for Data from: Patterns of Widespread Decline in North American Bumble Bees.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: occurrence.csvResource Description: File modified to remove fields with no recorded values.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: dwca-usda-ars-patternsofwidespreaddecline-bumblebees-v1.1.zipResource Description: Data from: Patterns of Widespread Decline in North American Bumble Bees -- this is a Darwin Core Archive file. The Darwin Core Archive is a zip file that contains three documents.

The occurrence data is stored in the occurrence.txt file. The metadata that describes the columns of this document is called meta.xml. This document is also the data dictionary for this dataset. The metadata that describes the dataset, including author and contact information for this dataset is called eml.xml.

Find the data files at https://bison.usgs.gov/ipt/resource?r=usda-ars-patternsofwidespreaddecline-bumblebees

1. The climate on our planet is changing and the range distributions of organisms are shifting in response. In aquatic environments, species might not be able to redistribute poleward or into deeper water when temperatures rise because of barriers, reduced light availability, altered water chemistry, or any combination of these. How species respond to climate change may depend on physiological adaptability, but also on the population dynamics of the species.

2. Density dependence is a ubiquitous force that governs population dynamics and regulates population growth, yet its connections to the impacts of climate change remain little known, especially in marine studies. Reductions in density below an environmental carrying capacity may cause compensatory increases in demographic parameters and population growth rate, hence masking the impacts of climate change on populations. On the other hand, climate-driven deterioration of conditions may reduce environmental carrying capacities, making compensation less likely and populations more susceptible to the effects of stochastic processes.

3. Here we investigate the effects of climate change on Baltic blue mussels using a 17-year data set on population density. Using a Bayesian modelling framework, we investigate the impacts of climate change, assess the magnitude and effects of density dependence, and project the likelihood of population decline by the year 2030.

4. Our findings show negative impacts of warmer and less saline waters, both outcomes of climate change. We also show that density-dependence increases the likelihood of population decline by subjecting the population to the detrimental effects of stochastic processes (i.e., low densities where random bad years can cause local extinction, negating the possibility for random good years to offset bad years).

5. We highlight the importance of understanding, and accounting for both density dependence and climate variation when predicting the impact of climate change on keystone species, such as the Baltic blue mussel. 08-Oct-2020

Unobserved individual and population level impacts of fishing gear entanglements on North Atlantic right whales

https://doi.org/10.5061/dryad.cz8w9gjfn

Description of the data and file structure

North Atlantic right whale sightings histories, visual health assessments, and entanglement assessments

Files and variables

File: Model_data_and_constants.RData

Description: Contains two lists (constants and hormone_data), which are used to fit a hidden Markov model to estimate North Atlantic right whale survival, reproductive, health, and entanglement state dynamics.

constants contains the following variables:

nind - number of individual right whales in the dataset

n.occasions - number of sampling occasions in the time series

n.prim.per.year - number of sampling occasions within each year

f.primary - vector; occasion of first sighting for each individual

f.year - vector; year of first sighting for each indivi...

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

Climate change affects insects in several ways, including phenological shifts that may cause asynchrony between herbivore insects and their host plants. Insect larvae typically have limited movement capacity and are consequently dependent on the microhabitat conditions of their immediate surroundings. Based on intensive field monitoring over two springs and on larger-scale metapopulation-level survey over the same years, we used Bayesian spatial regression modelling to study the effects of weather and microclimatic field conditions on the development and survival of post-diapause larvae of the Glanville fritillary butterfly (Melitaea cinxia) on its northern range edge. Moreover, we assessed whether the observed variation in growth and survival in a spring characterized by exceptionally warm weather early in the season translated into population dynamic effects on the metapopulation scale. While similar weather conditions enhanced larval survival and growth rate in the spring, microclimatic conditions affected survival and growth contrastingly due to the phenological asynchrony between larvae and their host plants in microclimates that supported fastest growth. In the warmest microclimates, larvae reached temperatures over 20°C above ambient leading to increased feeding, which was not supported by the more slowly growing host plants. At the metapopulation level, population growth rate was highest in local populations with heterogeneous microhabitats. We demonstrate how exceptionally warm weather early in the spring caused a phenological asynchrony between butterfly larvae and their host plants. Choice of warmest microhabitats for oviposition is adaptive under predominant conditions, but it may become maladaptive if early spring temperatures rise. Such conditions may lead to larvae breaking diapause earlier without equally advancing host plant growth. Microclimatic variability within and among populations is likely to have a crucial buffering effect against climate change in many insects.

Methods These datasets are a combination of fine-scale field monitoring data and metapopulation-level field survey data. Monitoring data consist of butterfly larval survival, growth, body surface temperature, and activity under variable weather and microclimatic conditions, as well as the growth of larval host plants under similar environmental conditions. For larval survival and growth, as well as host plant growth, the repeated measurements over the study season were averaged to gain a single value of each variable for each larval group and host plant plot. Larval temperature and activity datasets are time series with several measurements covering one day.

Survey data consist of two separate datasets on local population growth rates from one autumn to the following under variable weather and microclimatic conditions. Microclimatic conditions were recorded separately for each larval group in the field and averaged for local populations.

Rows with missing values were removed from all datasets.

The Southern Resident killer whale population (Orcinus orca) was listed as endangered in 2005 and shows little sign of recovery. These fish eating whales feed primarily on endangered Chinook salmon. Population growth is constrained by low offspring production for the number of reproductive females in the population. Lack of prey, increased toxins and vessel disturbance have been listed as potential causes of the whale’s decline, but partitioning these pressures has been difficult. We validated and applied temporal measures of progesterone and testosterone metabolites to assess occurrence, stage and health of pregnancy from genotyped killer whale feces collected using detection dogs. Thyroid and glucocorticoid hormone metabolites were measured from these same samples to assess physiological stress. These methods enabled us to assess pregnancy occurrence and failure as well as how pregnancy success was temporally impacted by nutritional and other stressors, between 2008 and 2014. Up to 69% of all detectable pregnancies were unsuccessful; of these, up to 33% failed relatively late in gestation or immediately post-partum, when the cost is especially high. Low availability of Chinook salmon appears to be an important stressor among these fish-eating whales as well as a significant cause of late pregnancy failure, including unobserved perinatal loss. However, release of lipophilic toxicants during fat metabolism in the nutritionally deprived animals may also provide a contributor to these cumulative effects. Results point to the importance of promoting Chinook salmon recovery to enhance population growth of Southern Resident killer whales. The physiological measures used in this study can also be used to monitor the success of actions aimed at promoting adaptive management of this important apex predator to the Pacific Northwest.

Demographic data from introduced crab in Seadrift Lagoon (Central California coast, shallow subtidal (<3 m depth)) in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trapping of invasive European green crabs to gather demographic data in Seadrift Lagoon, Stinson Beach, CA (lat 37.907440 long -122.666169).\u00a0All sites were accessed by either kayak or by foot via shore entry.\u00a0At each of six sites, we placed 10 baited traps (folding Fukui fish traps) in shallow (<2 m) subtidal areas. Traps were retrieved 24 hours later and were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, injuries, and presence of marks were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, frozen overnight disposed of in commercial agricultural compost. \u00a0

For each date and site, crabs from all traps (e.g. 10 traps per site) are pooled for counting and measuring.
Traps Used for each date (some with macroalgae "Ulva"):
02/19/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
02/20/2015\u00a0\u00a0 \u00a010 baited traps + 5 with ulva
03/05/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva per site
03/06/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
03/24/2015\u00a0\u00a0 \u00a010 traps/site
04/08/2015\u00a0\u00a0 \u00a010 traps/site
04/15/2015\u00a0\u00a0 \u00a010 baited traps + 4 traps with ulva
04/24/2015\u00a0\u00a0 \u00a010 traps/site
05/27/2015\u00a0\u00a0 \u00a0site 1 & 5 had 10 traps, site 3 had 9 traps
06/23/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
06/24/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
07/21/2015\u00a0\u00a0 \u00a0traps per site: site 1=20, site 2=20, site 3=17, site 4=15, site 5=10, site 6=10, site 7=20
08/25/2017\u00a0\u00a0 \u00a010 traps/site
08/26/2015\u00a0\u00a0 \u00a010 traps/site
08/27/2015\u00a0\u00a0 \u00a010 traps/site
09/01/2015\u00a0\u00a0 \u00a010 traps/site
09/02/2015\u00a0\u00a0 \u00a010 traps/site
09/30/2015\u00a0\u00a0 \u00a010 traps/site
10/01/2015\u00a0\u00a0 \u00a010 traps/site
10/02/2015\u00a0\u00a0 \u00a010 traps/site
12/01/2015\u00a0\u00a0 \u00a010 traps/site
12/02/2015\u00a0\u00a0 \u00a010 traps/site
12/03/2015\u00a0\u00a0 \u00a010 traps/site

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Monthly trapping in Seadrift Lagoon in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 02 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701863.1 Easternmost_Easting=-122.6661694 geospatial_lat_max=37.90744 geospatial_lat_min=37.90744 geospatial_lat_units=degrees_north geospatial_lon_max=-122.6661694 geospatial_lon_min=-122.6661694 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701863 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of the six sites used for monthly trapping plus three additional sites, we placed 15 baited traps (folding Fukui fish traps) in shallow ( instruments_0_dataset_instrument_nid=701870 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701863 Northernmost_Northing=37.90744 param_mapping={'701863': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701863/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=37.90744 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=lagoon,latitude,longitude version=1 Westernmost_Easting=-122.6661694 xml_source=osprey2erddap.update_xml() v1.3

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

1. A major challenge in biodiversity conservation is to facilitate viable populations of large apex predators in ecosystems where they were recently driven to ecological extinction due to resource conflict with humans. 2. Monetary compensation for losses of livestock due to predation is currently a key instrument to encourage human–carnivore coexistence. However, a lack of quantitative estimates of livestock losses due to predation leads to disagreement over the practise of compensation payments. This disagreement sustains the human–carnivore conflict. 3. The level of depredation on year-round, free-ranging, semi-domestic reindeer by large carnivores in Fennoscandia has been widely debated over several decades. In Norway, the reindeer herders claim that lynx and wolverine cause losses of tens of thousands of animals annually and cause negative population growth in herds. Conversely, previous research has suggested that monetary predator compensation can result in positive population growth in the husbandry, with cascading negative effects of high grazer densities on the biodiversity in tundra ecosystems. 4. We utilized a long-term, large-scale dataset to estimate the relative importance of lynx and wolverine predation and density-dependent and climatic food limitation on claims for losses, recruitment and population growth rates in Norwegian reindeer husbandry. 5. Claims of losses increased with increasing predator densities, but with no detectable effect on population growth rates. Density-dependent and climatic effects on claims of losses, recruitment and population growth rates, were much stronger than the effects of variation in lynx and wolverine densities. 6. Synthesis and applications. Our analysis provides a quantitative basis for predator compensation and estimation of the costs of reintroducing lynx and wolverine in areas with free-ranging semi-domestic reindeer. We outline a potential path for conflict management which involves adaptive monitoring programs, open access to data, herder involvement, and development of management strategy evaluation (MSE) models to disentangle complex responses including multiple stakeholders and individual harvester decisions.

Feed the Future (FtF) seeks to reduce poverty and undernutrition in 19 developing countries by focusing on accelerating growth of the agricultural sector, addressing the root causes of undernutrition, and reducing gender inequality. This dataset captures data in the geographic areas within Tajikistan known as Zones of Influence (ZOI) targeted by FtF interventions. These data cover the Tajikistan FtF population-based survey )PBS) and secondary sources that serve as the baseline values for the U.S. Government's FtF initiative led by USAID.

Codes used data from the file Covid_19. (R)

Fertility table of P. flavus LM population.

This dataset contains 735 radiocarbon date from coastal sites in Arctic Norway. The dates are used for palaeodemographic modeling, based on summed probability distribution methodology. Abstract: Synchronized demographic and behavioral patterns among distinct populations is a well-known, natural phenomenon. Intriguingly, similar patterns of synchrony occur among prehistoric human populations. However, the drivers of synchronous human ecodynamics are not well understood. Addressing this issue, we review the role of environmental variability in causing human demographic and adaptive responses. As a case study, we explore human ecodynamics of coastal hunter-gatherers in Holocene northern Europe, comparing population, economic and environmental dynamics in two separate areas (northern Norway and western Finland). Population trends are reconstructed using temporal frequency distributions of radiocarbon dated and shoreline dated archaeological sites. These are correlated to regional environmental proxies and proxies for maritime resource use. The results demonstrate remarkably synchronous patterns across population trajectories, marine resource exploitation, settlement pattern and technological responses. Crucially, the population dynamics strongly correspond to significant environmental changes. We evaluate competing hypotheses and suggest that the synchrony stems from similar responses to shared environmental variability. We take this to be a prehistoric human example of the “Moran effect”, positing similar responses of geographically distinct populations to shared environmental drivers. The results imply that intensified economies and social interaction networks have limited impact on long-term hunter-gatherer population trajectories beyond what is already proscribed by environmental drivers.

This survey was undertaken by Cefas as part of the Historic Arctic Survey Series;

Gadus morhua (Atlantic Cod) stocks in the Barents Sea are currently at levels not seen since the 1950s. Causes for the population increase last century, and understanding of whether such large numbers will be maintained in the future, are unclear. To explore this, we digitised and interrogated historical cod catch and diet datasets from the Barents Sea. Data includes temporal and spatial information, cod catch data and length distributions, and hydrographic data.

Survey took place between 12/08/1958 and 16/09/1958 on Ernest Holt

Equipment used during this survey :

• Otter Trawl 78ft Granton with shrimp netting cover

Survey operations were undertaken on 78 stations

20 different species were caught on this survey