Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

AbstractThe prevalence of disease-driven mass mortality events is increasing, but our understanding of spatial variation in their magnitude, timing, and triggers are often poorly resolved. Here, we use a novel range-wide dataset comprised of 48,810 surveys to quantify how Sea Star Wasting Disease affected Pycnopodia helianthoides, the sunflower sea star, across its range from Baja California, Mexico to the Aleutian Islands, USA. We found that the outbreak occurred more rapidly, killed a greater percentage of the population, and left fewer survivors in the southern half of the species’ range. Pycnopodia now appears to be functionally extinct (> 99.2% declines) from Baja California, Mexico to Cape Flattery, Washington, USA and exhibited severe declines (> 87.8%) from the Salish Sea to the Gulf of Alaska. The importance of temperature in predicting Pycnopodia distribution rose 450% after the outbreak, suggesting these latitudinal gradients may stem from an interaction between disease severity and warmer waters. We found no evidence of population recovery in the years since the outbreak. Natural recovery in the southern half of the range is unlikely over the short-term and assisted recovery will likely be required for recovery in the southern half of the range on ecologically-relevant time scales. MethodsThirty research groups from Canada, the United States, Mexico, including First Nations, shared 34 datasets containing field surveys of Pycnopodia (Table S1). The data included 48,810 surveys from 1967 to 2020 derived from trawls, remotely operated vehicles, SCUBA dives, and intertidal surveys. We compiled survey data into a standardized format that included at minimum the coordinates, date, depth, area surveyed, and occurrence of Pycnopodia for each survey. When datasets contained more than one survey at a site in the same day (e.g. multiple transects), we divided the total Pycnopodia count in all surveys by the total survey area and averaged the latitude, longitude, and depth as necessary. Using breaks in data coverage, political boundaries, and biogeographic breaks we assigned each survey to one of twelve regions: Aleutian Islands, west Gulf of Alaska (GOA), east Gulf of Alaska, southeast Alaska, British Columbia (excluding the Salish Sea), Salish Sea (including the Puget Sound), Washington outer coast (excluding the Puget Sound), Oregon, northern California, central California, southern California, and the Pacific coast of Baja California (Fig. S1; see Supplementary Material). Usage notesDocumentation, data, and code accompanying Hamilton et al., 2021 Pycnopodia Rangewide Assessment paper. Data MasterPycno_ToShare: Dec_lat = latitude in decimal degrees. Numeric. Dec_lon = longitude in decimal degrees. Numeric. Depth = depth in meters. Numeric. Pres_abs = presence or absence of Pycnopodia on that survey. Binary. Presence = 1, absence = 0 Density_m2 = density in meters squared if available for that set of surveys. Numeric. NA = no density data available for that survey. Source = shorthand name of the group that shared the data with us and the type of data (e.g. trawl, dive). To get further info on who that dataset, group, and group contact, see Table S1. Character. Note: When datasets contained more than one survey at a site in the same day (e.g. multiple transects), we divided the total Pycnopodia count in all surveys by the total survey area and averaged the latitude, longitude, and depth as necessary in order to minimize the impacts of pseudoreplication on the dataset. Used in MaxentSWD_Final and Density-Inc_Models_Figs_Tables_ToShare. CrashEventsForRPlot: Crash Dates were determined trends in Pycnopodia occurrence (site-level presence or absence) to estimate ‘crash date’, defined as the date when the occurrence rate of Pycnopodia in a region decreased by 75% from pre-outbreak levels. Used in OutbreakTimelineFigs_ToShare.R EpidemicPhases: See manuscript methods for information on how the column ‘EpidemicPhases’ was created. “Start-End” specifies whether that date was the start or the end of that epidemic phase for that region. Used in OutbreakTimelineFigs_ToShare.R Incidence_2012-2019: Columns G-J were calculated by fitting a logistic regression model to the occurrence of Pycnopodia over time for each region. We fit a logistic regression model to the occurrence of Pycnopodia from 1/1/2012 to 12/31/2019 to model the shape of the population decline for each region (Fig. 1a). From these models, we 1) estimated regional Pycnopodia occurrence rates on 1/1/2012 and 12/31/2019, 2) calculated the predicted occurrence value corresponding to a 75% decline in starting versus ending occurrence in each region, and 3) solved the inverse logistic equations for the date at which this occurrence value was predicted. All other columns are identifying information derived from MasterPycno_ToShare. Used in OutbreakTimelineFigs_ToShare.R MasterPycno_021821_SpatialJoin: Used to make Fig 5 for the remnant population analysis....

Despite illegal killing (poaching) being the major cause of death among large carnivores globally, little is known about the effect of implementing lethal management policies on poaching. Two opposing hypotheses have been proposed in the literature: implementing lethal management may decrease poaching incidence (‘killing for tolerance’) or increase it (‘facilitated killing’). Here, we propose a test of two opposed hypotheses that poaching (reported and unreported) of Mexican grey wolves (Canis lupus baileyi) in Arizona and New Mexico, USA, responded to changes in policy that relaxed protections to allow more wolf-killing. We employ advanced biostatistical survival and competing-risk methods to data on individual resightings, mortality, and disappearances of collared Mexican wolves. We aim to provide recommendations for improving the effectiveness of US policy on environmental crimes, endangered species, and protections for wild animals. Our results have implications beyond the USA or wolves because the methods promise to transform understanding, scientific methods, and management interventions of processes and patterns in human-caused mortality among wild animals subject to high rates of poaching.

Methods We analyzed data acquired from the USFWS Mexican Wolf Recovery Program (MWRP, 'Survival2016-FOIA Request_To be Released copy.csv') and their Office of Law Enforcement (OLE, 'Final FWSLE FOIA Release copy.xlsx') in separate but overlapping datasets on marked (hereafter collared), monitored Mexican wolves in the wild. The MWRP survival data include the monitoring history for all collared and monitored adult Mexican wolves in the wild since the beginning of the recovery program, 29 March 1998 - 31 December 2016; n=279 (monitored wolf pups were excluded from this dataset).

We processed the original data following commands provided in a STATA .do file within the Supplementary Materials of the published article (to be used with the formatted for processing 'MXWolfSurvival2016_FOIA_Master_ORIGINAL copy.xlsx').

This study analyzes whether femicide in Mexico has increased more severely than other life and bodily integrity crimes (e.g., homicide, culpable homicide, injuries, malicious injuries, abortion, and other crimes that threaten life). To achieve this, the Executive Secretariat of the National Public Security System database was cleaned and the number of femicides per 100,000 inhabitants was calculated, for the period from January 2016 to March 2022 in all states of Mexico. Through descriptive statistics, non-parametric analysis of means, and hypothesis tests, we demonstrate that the states with the highest number of femicides are the Estado de Mexico (State of Mexico), Ciudad de Mexico (Mexico City), and Veracruz; moreover, the number of femicides exhibits a growing trend while the total number of life and bodily integrity crimes does not. Finally, we forecast the number of femicides for the next five months. To our knowledge, there is no other article that analyzes the growth trend of femicide compared to other crimes. Visualizing and understanding that femicide is on the rise compared with other types of crimes can help the government and legislators generate policies that are consistent with the magnitude of the problem.

This study analyzes whether femicide in Mexico has increased more severely than other life and bodily integrity crimes (e.g., homicide, culpable homicide, injuries, malicious injuries, abortion, and other crimes that threaten life). To achieve this, the Executive Secretariat of the National Public Security System database was cleaned and the number of femicides per 100,000 inhabitants was calculated, for the period from January 2016 to March 2022 in all states of Mexico. Through descriptive statistics, non-parametric analysis of means, and hypothesis tests, we demonstrate that the states with the highest number of femicides are the Estado de Mexico (State of Mexico), Ciudad de Mexico (Mexico City), and Veracruz; moreover, the number of femicides exhibits a growing trend while the total number of life and bodily integrity crimes does not. Finally, we forecast the number of femicides for the next five months. To our knowledge, there is no other article that analyzes the growth trend of femicide compared to other crimes. Visualizing and understanding that femicide is on the rise compared with other types of crimes can help the government and legislators generate policies that are consistent with the magnitude of the problem.