Closed mark-recapture models to estimate species richness: An example using data on epigeal spiders

Species richness is a fundamental ecological property. The problem of estimating the number of species is quite similar to that of estimating the population size of a single species. Some authors of mark-recapture statistics have proposed using some of these methods to estimate species richness. This necessitates understanding how the recording probabilities of individuals differ from those of species. In particular, the species of a species pool are likely to exhibit a wide range of recording probabilities. Depending on sampling conditions, temporal or spatial variation in species detection probability may also occur, making model M _th estimators particularly useful. Empirical detection probabilities and estimates of species numbers using three coverage, one point, and two jackknife estimators are presented for series of spatial and temporal trapping occasions of epigeal spiders.     

Sustainable management of wildlife habitat and risk of extinction

Whether land management planning provides for sufficient habitat to sustain viable populations of indigenous wildlife is one of the greatest challenges confronting resource managers. Analyses of the effects of land management on natural resources often rely on qualitative assessments that focus on single species to reflect the risk of wildlife extinction across a planning area. We propose a conceptual framework for sustainable management of wildlife habitat that explicitly acknowledges the greater risk of an extinction event when considering the viability of multiple species, e.g., an indigenous vertebrate fauna. This concept is based on the principle that the likelihood of at least one event (i.e., species extinction) is the joint probability of the extinction probabilities of individual species, assuming independence among species’ responses to disturbance. We present an ecological rationale to support the view that, at a spatial scale of 10⁴-10⁶ ha (i.e., planning area) and a temporal scale of 10² years (i.e., planning horizon), wildlife species operating at varying ecological scales respond relatively independently to disturbances typically associated with land management. We use a hypothetical scenario of a wildlife viability assessment and Monte Carlo simulation to demonstrate that the probability of ‘any extinction’ is consistently higher than the probability of the ‘single most likely’ extinction, and that the difference between these values increases as more disturbance-sensitive species (i.e., species at risk) are analyzed. We conclude that risk assessments that rely upon the most sensitive single species may substantially underestimate the risk of wildlife extinction across a planning area. Furthermore, the selection of a planning alternative based on relative threat of local extinction of wildlife populations can vary depending on which paradigm is used to estimate risk to viability across the planning area.     

黄河故道砀山酥梨花期低温冷害风险的评估方法初探

低温冷害为砀山酥梨花期的主要气象灾害之一,其风险评估具有重要意义。运用数理统计方法研究分析1983-2006年砀山酥梨物候资料和同期气象资料,从酥梨始花期和整个花期的日最低气温的概率分布入手,建立了酥梨花期低温冷害风险评估模型。结果表明：（1）砀山酥梨花期内遭受某一级别的低温冷害的风险,可由始花期出现的概率与花期内逐日出现某一级别低温冷害的概率之和的乘积来表示,一般是酥梨始花期越早、花期历时越长,遇到某一级别低温冷害的风险越大。所得风险评估方程,可用于风险评估。（2）砀山酥梨开花始期具有正态分布特征,即始花期早和迟的出现频率较小,处于历年始花期均值附近的频率较大。（3）砀山酥梨花期遭受低温冷害的指标可分为花期内出现日最低气温≤7℃、≤5℃、≤3℃三个级别。（4）酥梨始花后到某一日遭受某一级别低温冷害的累积概率,除始花日出现≤3℃低温冷害的概率与始花期早晚呈线性关系外,其他日期的累计概率均与始花期的早晚呈指数关系。     

不同喷药时期对甘肃陇南小麦赤霉病的防治效果

为明确小麦不同生长期喷施430 g/L戊唑醇悬浮剂对小麦赤霉病防治效果的影响,于2021年在甘肃省陇南市徽县栗川镇范寺村进行了田间试验。结果表明,在小麦抽穗期、扬花期、灌浆期喷雾,田间防效分别为93.23%、84.39%、36.51%,折合产量分别为9 168.0、8 949.0、8 376.0 kg/hm2,较喷清水分别增产10.76%、8.12%、1.19%。说明小麦抽穗期到扬花期是甘肃陇南防治小麦赤霉病的关键时期,在该阶段进行杀菌剂喷雾防治,可有效控制小麦赤霉病的发生流行。     

Long-term dynamics and population viability in one of the last populations of the endangered Spiranthes spiralis (Orchidaceae) in the Netherlands

During the last decades, most orchid species in much of Western Europe have suffered significant declines and the long-term survival of the remaining populations remains to a large extent uncertain. In particular, populations at range margins may be more prone to extinction than more central populations, as the former tend to be small and isolated, occur in ecologically marginal habitats and have a lower per-capita reproductive rate. In this study, we investigated the long-term dynamics and population viability of a population at the margin of its range of Spiranthes spiralis in the Netherlands. At present, only 2 out of 40 previously known populations persist. Individual plants were monitored for 24 years and their life span, flowering frequency and vegetative growth were determined. Individual plants showed large temporal variation in sexual and vegetative growth among years. The proportion of flowering plants varied from 0 (no plants were flowering) to 100 (all plants were flowering). Vegetative growth, on the other hand, increased when the number of individuals decreased. Dormancy was present, but occurred only in a few individuals. Using a non-structured population viability model, future prospects of this species were assessed. Calculation of extinction probabilities and estimated times to extinction using the diffusion approximation model showed that the species had a relatively high probability (79%) of surviving the next 20 years, whereas the median time to extinction was forty years. However, because 95% confidence intervals of the population growth included 1, we suggest that continued monitoring and additional genetic research are needed to assess the long-term viability of this species.     

A Fuzzy-Set Approach for Addressing Uncertainties in Risk Assessment of Hydrocarbon-Contaminated Site

This study presented an integrated approach for evaluating environmental risks associated with hydrocarbon-contaminated sites through incorporation of a multiphase multi-component modeling system within a general risk assessment framework. The uncertainties associated with risk evaluation criteria were emphasized and effectively addressed through the development of a fuzzy-set approach. This development was based on (a) simulation of the fate of the contaminant in the subsurface to calculate contaminant concentrations in groundwater; (b) examination of the excess lifetime cancer risk (ELCR) distributions; (c) quantification of uncertainties in ELCR evaluation criteria using fuzzy membership functions; (d) categorization of the related risk levels into low, low-to-medium, medium, medium-to-high, and high, and (e) assessment of risk levels based on ELCR distribution and fuzzy evaluation criteria. The developed fuzzy risk assessment approach (FRA) was applied to a hydrocarbon-contaminated site in western Canada. Three remediation scenarios with different efficiencies (0, 60, and 90%) and planning periods (10, 20, 40, and 60 years later) were considered, and the spatial and temporal variations of risk values were examined. The temporal variation of membership values for different risk levels at locations of interest was also identified. The FRA is useful for the decision maker to gain insight into the environmental risks by considering uncertainties and to make more realistic remediation decisions.     

Landscape conditions predisposing grizzly bears to conflicts on private agricultural lands in the western USA

We used multiple logistic regression to model how different landscape conditions contributed to the probability of human-grizzly bear conflicts on private agricultural ranch lands. We used locations of livestock pastures, traditional livestock carcass disposal areas (boneyards), beehives, and wetland-riparian associated vegetation to model the locations of 178 reported human-grizzly bear conflicts along the Rocky Mountain East Front, Montana, USA during 1986-2001. We surveyed 61 livestock producers in the upper Teton watershed of north-central Montana, to collect spatial and temporal data on livestock pastures, boneyards, and beehives for the same period, accounting for changes in livestock and boneyard management and beehive location and protection, for each season. We used 2032 random points to represent the null hypothesis of random location relative to potential explanatory landscape features, and used Akaike’s Information Criteria (AIC/AIC_C) and Hosmer-Lemeshow goodness-of-fit statistics for model selection. We used a resulting “best” model to map contours of predicted probabilities of conflict, and used this map for verification with an independent dataset of conflicts to provide additional insights regarding the nature of conflicts. The presence of riparian vegetation and distances to spring, summer, and fall sheep or cattle pastures, calving and sheep lambing areas, unmanaged boneyards, and fenced and unfenced beehives were all associated with the likelihood of human-grizzly bear conflicts. Our model suggests that collections of attractants concentrated in high quality bear habitat largely explain broad patterns of human-grizzly bear conflicts on private agricultural land in our study area.     

Determining the infection status of a herd

This article presents hierarchical models for determining infection status and prevalence of infection within a herd given a hypergeometric or binomial sample of animals that have been screened with an imperfect test. Expert prior information on the infection status of the herd, diagnostic test accuracy, and herd prevalence is incorporated into the model. Posterior probabilities versus prior probabilities of infection are presented in the novel form of a curve, summarizing the probability of infection over a range of possible prior probability values. We demonstrate the model with serologic data for Mycobacterium paratuberculosis (Johne’s disease) in dairy herds.     

Modeling spatial-temporal binary data using Markov random fields

An autologistic regression model consists of a logistic regression of a response variable on explanatory variables and an autoregression on responses at neighboring locations on a lattice. It is a Markov random field with pairwise spatial dependence and is a popular tool for modeling spatial binary responses. In this article, we add a temporal component to the autologistic regression model for spatial-temporal binary data. The spatial-temporal autologistic regression model captures the relationship between a binary response and potential explanatory variables, and adjusts for both spatial dependence and temporal dependence simultaneously by a space-time Markov random field. We estimate the model parameters by maximum pseudo-likelihood and obtain optimal prediction of future responses on the lattice by a Gibbs sampler. For illustration, the method is applied to study the outbreaks of southern pine bettle in North Carolina. We also discuss the generality of our approach for modeling other types of spatial-temporal lattice data.     

Monitoring multiple species: Estimating state variables and exploring the efficacy of a monitoring program

Monitoring programs have the potential to identify population declines and differentiate among the possible cause(s) of these declines. Recent criticisms regarding the design of monitoring programs have highlighted a failure to clearly state objectives and to address detectability and spatial sampling issues. Here, we incorporate these criticisms to design an efficient monitoring program whose goals are to determine environmental factors which influence the current distribution and measure change in distributions over time for a suite of amphibians. In designing the study we (1) specified a priori factors that may relate to occupancy, extinction, and colonization probabilities and (2) used the data collected (incorporating detectability) to address our scientific questions and adjust our sampling protocols. Our results highlight the role of wetland hydroperiod and other local covariates in the probability of amphibian occupancy. There was a change in overall occupancy probabilities for most species over the first three years of monitoring. Most colonization and extinction estimates were constant over time (years) and space (among wetlands), with one notable exception: local extinction probabilities for Rana clamitans were lower for wetlands with longer hydroperiods. We used information from the target system to generate scenarios of population change and gauge the ability of the current sampling to meet monitoring goals. Our results highlight the limitations of the current sampling design, emphasizing the need for long-term efforts, with periodic re-evaluation of the program in a framework that can inform management decisions.     

基于信息扩散理论分析华北平原夏玉米花期高温热害的风险概率

利用1980−2019年华北平原40个站点的气象数据,将夏玉米花期日最高气温≥35℃持续3d及以上作为高温指标,综合考虑频次和持续时间,制定轻度、中度、重度高温热害等级;利用灾害发生次数和站次比分析夏玉米花期高温热害的变化规律;基于信息扩散理论评估高温热害风险概率,为科学应对夏玉米花期高温热害,保障夏玉米的高产稳产提供依据。结果表明：（1）2010−2019年是华北平原夏玉米受花期高温热害影响加重的阶段,呈现连年发生、范围明显扩大的特征,河南省表现明显。（2）夏玉米花期高温热害高风险区主要为山东西部和河南省,山东西部以轻度热害为主,风险概率在10a一遇以上（≥10%）。河南省受灾范围广、频次高、程度重,重度高温热害的风险概率在10a一遇以上（≥10%）的面积占比为66%,5a一遇以上（≥20%）的面积占比为18%。     

渭河流域降雨侵蚀力时空分布特征

[目的]揭示渭河流域降雨侵蚀力的时空变化特征,为区域水土保持规划提供依据。[方法]根据渭河流域及其周边范围30个气象站点1957—2014年逐日降雨资料,采用章文波日降雨量侵蚀模型计算各站点的降雨侵蚀力,分析其空间分布规律和年内分布特征。[结果]渭河流域多年平均降雨侵蚀力值分布范围为806.25~3 510.81 MJ·mm/(hm2·h),平均值1 798.97 MJ·mm/(hm2·h),与多年平均侵蚀性降雨的空间分布基本一致,总体呈现西北低东南高的趋势。渭河流域降雨侵蚀力年内变化呈单峰型,主要集中在7—9月,占全年降雨侵蚀力的63.91%。北部黄土高原地区和关中平原发生水土流失的时期集中在7—9月,而秦岭北麓地区5—10月均有可能发生较大的水土流域,侵蚀风险由西北向东南递增。流域降雨侵蚀力年际波动较大,年际变率Cv值在34%~56%之间,整体而言,流域西北部地区的降雨侵蚀力年际变化幅度大于东南部地区。除洛川、长武、环县、平凉4个站点降雨侵蚀力在研究时段内有所增大外,其余地区降雨侵蚀侵蚀力呈不同速率的减小趋势。[结论]渭河流域降雨侵蚀力时空分布差异显著,尽管流域降雨侵蚀力呈减弱趋势,由于流域地处黄土高原,水土保持与水源涵养工作仍需高度重视。     

A Hierarchical Model for Serially-Dependent Extremes: A Study of Heat Waves in the Western US

Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979–2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online.     

A risk analysis methodology for assessing natural resources degradation

Risk analysis has been employed, amongst other things, both to estimate the probability of future water demand emergencies in reservoir systems (using simulation modelling), and to estimate environmental and public health risks (using empirical data). We assert that this framework, when coupled with simulation modelling, can be applied to examine and compare the impacts of resource exploitation, land use and production strategies which may cause land degradation. Our representation of risk analysis relies on the assumption that each land use strategy is associated with a risk of the system (i.e., social system, production system, ecosystem) attaining a subjectively unacceptable environmental condition (e.g. poor human nutrition, crop failure, degradation of a natural resource) sometime during a management planning period. The research methodology entails: (1) the identification of critical variables in the social and biological environment that are affected by exploitation and management of resources; (2) the identification through interviews, surveys, and research of regions of unacceptability in these variables that determine the dynamics of local environmental degradation; (3) the translation of resource policy and practice into a computer model of impact on the resource system; (4) many iterations of simulation of the system to determine the ‘risk of failing’ in each of the critical variables. The presentation of risk probabilities to decision-makers represents a reduction of many simulations into an understandable estimate of environmental impact. More importantly, risk analysis is potentially a learning tool for human system studies, and an interface for applied social science and ecological research.     

河南省夏玉米花期高温热害风险分析

根据河南省110个气象站1970−2019年逐日最高气温观测资料和19个农业气象观测站夏玉米生育期数据,以32℃和35℃作为轻度和重度夏玉米花期高温热害发生阈值,选取花期日最高气温≥32℃和≥35℃发生频率和日最高气温≥32℃和≥35℃的积热量4个关键致灾气象因子,构建高温热害综合指数,开展河南省夏玉米花期高温热害风险区划,为夏玉米生产趋利避害和防灾减灾提供参考依据。结果表明：（1）1970−2019年河南省夏玉米花期高温日数呈先减小后增加的趋势,21世纪10年代（2010s）后高温日数和频率明显增加,与高温发生频次最低的1980s（20世纪80年代）相比,高温日数增加1.4d（≥32℃）和1.5d（≥35℃）,高温频率增加20.6个百分点（≥32℃）和20.5个百分点（≥35℃）。河南省夏玉米花期高温日数50a平均值在1.8～4.5d（≥32℃）和0.4～1.9d（≥35℃）,高温发生频率在24.3%～64.3%（≥32℃）和2.5%～31.1%（≥35℃）,豫东南为高温热害发生的高频区。（2）近50a轻度和重度高温积热均呈先减弱再增强的趋势,21世纪10年代（2010s）高温积热较1980s（20世纪80年代）增加52.8℃·d（≥32℃）和52.5℃·d（≥35℃）。空间分布上基本呈现出南高北低、东部平原高于西部山区的态势。（3）结合高温强度和发生频率的致灾高风险区主要集中在南阳南部、漯河、许昌东部、周口、驻马店,约占夏玉米主栽区面积的28.5%;研究区域大部地区为中风险区,约占夏玉米主栽区面积的56.2%;而三门峡、洛阳西部、济源西部、安阳等地夏玉米花期高温热害风险相对较低,约占夏玉米主栽区面积的15.3%。     

Patterns of diversity in pigeonpea (<Emphasis Type="Italic">Cajanus cajan</Emphasis> (L.) Millsp.) germplasm collected from different elevations in Kenya

Pigeonpea germplasm accessions collected from low (<500 m), medium (501–1000 m), high (1001–1500 m) and very high elevation zones (>1500 m) of Kenya were evaluated for 15 agronomic traits and seed protein content at ICRISAT, Patancheru, India. There were significant differences (P < 0.001) among elevation zones for the number of primary and secondary branches, days to 75% maturity, pod length, seeds per pod, 100-seed weight and seed yield. Mean values indicated that the accessions from low elevation zone were significantly different from those collected in higher elevation zones for early flowering and maturity, number of primary branches, pod length, number of pods per plant, seeds per pod, 100-seed weight, seed yield and harvest index. None of the accessions collected in Kenya belonged to extra early (<80 days to 50% flowering) and early (80–100 days to 50% flowering) maturity groups, as defined by time to flowering at Patancheru, India. Mean diversity index based on all characters indicated that accessions from the low elevation zone are more diverse than those from the higher elevation zones. Frequency distribution for trait extremes indicated that the accessions from the low elevation zone were early to flower and mature, short statured, produced more primary and secondary branches with high pod bearing length, long pods, more pods per plant, more seeds per pod, a high seed yield and harvest index. Accessions from the very high elevation zone were late flowering, with a large number of tertiary branches, large seeds and a high shelling percentage and could be a source for cold tolerance and the breeding of vegetable types. Results suggest that the elevation of collection sites is therefore a very important determinant of variation patterns of pigeonpea in Kenya.     

Estimation of detection probability in aerial surveys of antarctic pack-ice seals

We use line transect detection functions together with generalized linear and additive models to estimate detection probability when detection on the line (“g(0)”) may not be certain. The methods provide a flexible way of modeling detection probability for independent observer surveys, and for investigating the effects of explanatory variables. Analysis of data from an aerial survey of pack-ice seals produced g(0) estimates substantially below 1 for some observers (it varied from 0.80 to 0.98), demonstrated a fairly complex dependence of detection probability on covariates, and showed negative correlation between observers’ search width and their g(0). In addition to illustrating the utility of generalized additive models for capturing the effect of covariates on detection probability, the analysis suggests that detection functions may be sufficiently variable that use of g(0) correction factors obtained from other surveys would be inadvisable. We recommend that estimation of g(0) be considered for all aerial surveys; if g(0) is found to be very close to 1, estimation from subsequent surveys under the assumption that it is 1 may be reasonable, but without any estimation of g(0), the assumption that it is 1 is a matter of faith.     

Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information

Bayesian Maximum Entropy was used to estimate the probabilities of occurrence of soil categories in the Netherlands, and to simulate realizations from the associated multi‐point pdf. Besides the hard observations (H) of the categories at 8369 locations, the soil map of the Netherlands 1:50 000 was used as soft information (S). The category with the maximum estimated probability was used as the predicted category. The quality of the resulting BME(HS)‐map was compared with that of the BME(H)‐map obtained by using only the hard data in BME‐estimation, and with the existing soil map. Validation with a probability sample showed that the use of the soft information in BME‐estimation leads to a considerable and significant increase of map purity by 15%. This increase of map purity was due to the high purity of the existing soil map (71.3%). The purity of the BME(HS) was only slightly larger than that of the existing soil map. This was due to the small correlation length of the soil categories. The theoretical purity of the BME‐maps overestimated the actual map purity, which can be partly explained by the biased estimates of the one‐point bivariate probabilities of hard and soft categories of the same label. Part of the hard data is collected to describe characteristic soil profiles of the map units which explains the bias. Therefore, care must be taken when using the purposively selected data in soil information systems for calibrating the probability model. It is concluded that BME is a valuable method for spatial prediction and simulation of soil categories when the number of categories is rather small (say < 10). For larger numbers of categories, the computational burden becomes prohibitive, and large samples are needed for calibration of the probability model.     

Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation

When data streams are observed without error and at regular time intervals, discrete-time hidden Markov models (HMMs) have become immensely popular for the analysis of animal location and auxiliary biotelemetry data. However, measurement error and temporally irregular data are often pervasive in telemetry studies, particularly in marine systems. While relatively small amounts of missing data that are missing-completely-at-random are not typically problematic in HMMs, temporal irregularity can result in few (if any) observations aligning with the regular time steps required by HMMs. Fitting HMMs that explicitly account for uncertainty attributable to location measurement error, temporally irregular observations, or other forms of missing data typically requires computationally demanding techniques, such as Markov chain Monte Carlo (MCMC). Using simulation and a real-world bearded seal (Erignathus barbatus) example, I investigate a practical alternative to incorporating measurement error and temporally irregular observations into HMMs based on multiple imputation of the position process drawn from a single-state continuous-time movement model. This two-stage approach is relatively simple, performed with existing software using efficient maximum likelihood methods, and completely parallelizable. I generally found the approach to perform well across a broad range of simulated measurement error and irregular sampling rates, with latent states and locations reliably recovered in nearly all simulated scenarios. However, high measurement error coupled with low sampling rates often induced bias in both the estimated probability distributions of data streams derived from the imputed position process and the estimated effects of spatial covariates on state transition probabilities. Results from the two-stage analysis of the bearded seal data were similar to a more computationally intensive single-stage MCMC analysis, but the two-stage analysis required much less computation time and no custom model-fitting algorithms. I thus found the two-stage multiple-imputation approach to be promising in terms of its ease of implementation, computation time, and performance. Code for implementing the approach using the R package “momentuHMM” is provided.Supplementary materials accompanying this paper appear online.     

Persistence in tillage decisions: Aggregate data analysis

The Emission Gap Report 2013 from the United Nations Environment Program showed that adopting conservation tillage such as no-till, as an alternative to conventional tillage, contributes significantly to climate change mitigation through carbon sequestration. However, substantial amounts of soil carbon are lost when farmers interrupt continuous use of conservation tillage with conventional tillage. Conservation tillage is spreading, but little is known about the behavioral persistence in tillage decisions. To address the gap in the literature, we estimate county-specific Markov models of tillage-crop choices, and use the predicted probabilities of alternative two- and three-year tillage rotations to evaluate spatial variation and temporal persistence in conservation tillage adoption for the state of Iowa (U.S). We find that the county-average probabilities of continuous conservation tillage range between 0.133 and 0.295, and vary significantly among crop rotations. We also find a statistically strong positive effect of the incidence of the highly erodible land on the county-average use of continuous conservation tillage. Our results underscore the importance of dynamic modeling for understanding behavioral persistence in tillage decisions, and the interdependence between farmers' crop and tillage rotations.