Scaling range sizes to threats for robust predictions of risks to biodiversity
Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales...
Saved in:
| Published in | Conservation biology Vol. 32; no. 2; pp. 322 - 332 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Wiley Blackwell, Inc
01.04.2018
Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in redlist assessments for decades, appropriate spatial scales of AOO for predicting risks of species' extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scalesensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1-1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer-scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid-measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scalingprocedures for assessing risks posed by landscape-scale threats to species and ecosystems. La evaluación de los riesgos para la biodiversidad generalmente depende de la distribución espacial de las especies y los ecosistemas. Las medidas del rango de extensión, como el área de ocupación (ADO), que se utilizan ampliamente en estas valoraciones son sensibles a la escala de medición, lo que genera propuestas para medirlas a escalas más finas o a diferentes escalas con base en la forma de distribución o en las características ecológicas de la biota. A pesar de su papel dominante en las valoraciones de listas rojas durante décadas, las escalas espaciales apropiadas del ADO para predecir el riesgo de extinción de las especies o el colapso de un ecosistema sigue siendo polémico y sin ser probado. No hay evaluaciones cuantitativas de la sensibilidad de escala del ADO como pronóstico de los riesgos, la relación entre la escala óptima del ADO y la escala de la amenaza, o el efecto de incertidumbre de cuadrícula. Utilizamos modelos de simulación estocástica para explorar los riesgos para los ecosistemas y las especies con patrones de distribución agrupada, dispersa y linear sujetos a regímenes de eventos amenazantes con frecuencias y extensiones espaciales diferentes. El área de ocupación fue un pronosticador preciso del riesgo (0.81<|r|<0.98) y actuó óptimamente cuando se midió con celdas de cuadrícula de 0.1 -1.0 veces la mayor área plausible amenazada por un evento. Contrario a aseveraciones previas, los estimados del ADO a estas escalas relativamente burdasfueron mejore pronosticadores del riesgo que los estimados del ADO a escalas más finas (p. ej. cuando las celdas de medición son <1% del área de la mayor amenaza). La escala óptima dependió de las escalas espaciales de las amenazas más que de la forma o el tamaño de las distribuciones bióticas. Aunque encontramos un potencial apreciable para los errores de medida de celda, las pautas actuales de la UICNpara la estimación del ADO neutralizan la incertidumbre geométrica e incorporan procedimientos efectivos de modificación de escala para la valoración de los riesgos presentados por las amenazas a escala de paisaje para las especies y los ecosistemas. 对生物多祥性面临风险的评估通常依赖于物种和生态系统的空间分布。在这类评估中广泛使用的分布范围大 小的度量，例如占有面积 (AOO), 对测量尺度很敏感，这提示我们应基于分布区形状或生物区生态特性在更精细 的尺度或不同尺度上进行测量。尽管几十年来 AOO 在红色名录评估中占有关键地位，然而用于预测物种灭绝 风险或生态系统崩溃的AOO合适的空间尺度还未得到检验且尚存在争议。AOO 作为风险预测指标的尺度敏 感性、最适 AOO 尺度和胁迫尺度的关系，以及网格不确定性的影响，目前都没有进行定量评估。我们用随机模 拟模型来探究生态系统和聚群分布、分散分布和线性分布的物种面临的风险，同时考虑它们受到的不同频率和 空间尺度的胁迫事件。占有面积是准确的风险预测指标（0.81<|r|<0.98)，当在测量的网格单元大小为受胁迫 影响的最大可能面积的 0.1-1.0 倍时，预测效果最好。与之前的认识相反，AOO 估计值在相对粗糙的尺度上比在 精细尺度上(如测量单元大小<1%最大胁迫面积时) 可以更好地预测风险。最优尺度更多地依赖于胁迫的空间尺 度，而不是生物分布区的形状或大小。尽管我们发现网格测量有很大潜在的误差，但当前 IUCN 估计 AOO 的指 导原则抵消了几何不确定性，且整合了确定尺度的有效程序，来评估景观尺度的胁迫对物种和生态系统产生的风 险 |
|---|---|
| AbstractList | Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems. Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in redlist assessments for decades, appropriate spatial scales of AOO for predicting risks of species' extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scalesensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1-1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer-scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid-measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scalingprocedures for assessing risks posed by landscape-scale threats to species and ecosystems. La evaluación de los riesgos para la biodiversidad generalmente depende de la distribución espacial de las especies y los ecosistemas. Las medidas del rango de extensión, como el área de ocupación (ADO), que se utilizan ampliamente en estas valoraciones son sensibles a la escala de medición, lo que genera propuestas para medirlas a escalas más finas o a diferentes escalas con base en la forma de distribución o en las características ecológicas de la biota. A pesar de su papel dominante en las valoraciones de listas rojas durante décadas, las escalas espaciales apropiadas del ADO para predecir el riesgo de extinción de las especies o el colapso de un ecosistema sigue siendo polémico y sin ser probado. No hay evaluaciones cuantitativas de la sensibilidad de escala del ADO como pronóstico de los riesgos, la relación entre la escala óptima del ADO y la escala de la amenaza, o el efecto de incertidumbre de cuadrícula. Utilizamos modelos de simulación estocástica para explorar los riesgos para los ecosistemas y las especies con patrones de distribución agrupada, dispersa y linear sujetos a regímenes de eventos amenazantes con frecuencias y extensiones espaciales diferentes. El área de ocupación fue un pronosticador preciso del riesgo (0.81<|r|<0.98) y actuó óptimamente cuando se midió con celdas de cuadrícula de 0.1 -1.0 veces la mayor área plausible amenazada por un evento. Contrario a aseveraciones previas, los estimados del ADO a estas escalas relativamente burdasfueron mejore pronosticadores del riesgo que los estimados del ADO a escalas más finas (p. ej. cuando las celdas de medición son <1% del área de la mayor amenaza). La escala óptima dependió de las escalas espaciales de las amenazas más que de la forma o el tamaño de las distribuciones bióticas. Aunque encontramos un potencial apreciable para los errores de medida de celda, las pautas actuales de la UICNpara la estimación del ADO neutralizan la incertidumbre geométrica e incorporan procedimientos efectivos de modificación de escala para la valoración de los riesgos presentados por las amenazas a escala de paisaje para las especies y los ecosistemas. 对生物多祥性面临风险的评估通常依赖于物种和生态系统的空间分布。在这类评估中广泛使用的分布范围大 小的度量，例如占有面积 (AOO), 对测量尺度很敏感，这提示我们应基于分布区形状或生物区生态特性在更精细 的尺度或不同尺度上进行测量。尽管几十年来 AOO 在红色名录评估中占有关键地位，然而用于预测物种灭绝 风险或生态系统崩溃的AOO合适的空间尺度还未得到检验且尚存在争议。AOO 作为风险预测指标的尺度敏 感性、最适 AOO 尺度和胁迫尺度的关系，以及网格不确定性的影响，目前都没有进行定量评估。我们用随机模 拟模型来探究生态系统和聚群分布、分散分布和线性分布的物种面临的风险，同时考虑它们受到的不同频率和 空间尺度的胁迫事件。占有面积是准确的风险预测指标（0.81<|r|<0.98)，当在测量的网格单元大小为受胁迫 影响的最大可能面积的 0.1-1.0 倍时，预测效果最好。与之前的认识相反，AOO 估计值在相对粗糙的尺度上比在 精细尺度上(如测量单元大小<1%最大胁迫面积时) 可以更好地预测风险。最优尺度更多地依赖于胁迫的空间尺 度，而不是生物分布区的形状或大小。尽管我们发现网格测量有很大潜在的误差，但当前 IUCN 估计 AOO 的指 导原则抵消了几何不确定性，且整合了确定尺度的有效程序，来评估景观尺度的胁迫对物种和生态系统产生的风 险 Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red-list assessments for decades, appropriate spatial scales of AOO for predicting risks of species' extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale-sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1-1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer-scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid-measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape-scale threats to species and ecosystems.Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red-list assessments for decades, appropriate spatial scales of AOO for predicting risks of species' extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale-sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1-1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer-scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid-measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape-scale threats to species and ecosystems. Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<| r |<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems. 确定分布区大小受到胁迫的尺度以用于稳健的生物多样性风险预测 对生物多样性面临风险的评估通常依赖于物种和生态系统的空间分布。在这类评估中广泛使用的分布范围大小的度量, 例如占有面积 (AOO), 对测量尺度很敏感, 这提示我们应基于分布区形状或生物区生态特性在更精细的尺度或不同尺度上进行测量。尽管几十年来 AOO 在红色名录评估中占有关键地位, 然而用于预测物种灭绝风险或生态系统崩溃的 AOO 合适的空间尺度还未得到检验且尚存在争议。 AOO 作为风险预测指标的尺度敏感性、最适 AOO 尺度和胁迫尺度的关系, 以及网格不确定性的影响, 目前都没有进行定量评估。我们用随机模拟模型来探究生态系统和聚群分布、分散分布和线性分布的物种面临的风险, 同时考虑它们受到的不同频率和空间尺度的胁迫事件。占有面积是准确的风险预测指标 (0.81<| r |<0.98), 当在测量的网格单元大小为受胁迫影响的最大可能面积的 0.1‐1.0 倍时, 预测效果最好。与之前的认识相反, AOO 估计值在相对粗糙的尺度上比在精细尺度上(如测量单元大小<1%最大胁迫面积时)可以更好地预测风险。最优尺度更多地依赖于胁迫的空间尺度, 而不是生物分布区的形状或大小。尽管我们发现网格测量有很大潜在的误差, 但当前 IUCN 估计 AOO 的指导原则抵消了几何不确定性, 且整合了确定尺度的有效程序, 来评估景观尺度的胁迫对物种和生态系统产生的风险。 【翻译: 胡怡思; 审校: 胡义波】 Article impact statement : Scaled area of occupancy is a good predictor of species extinction and ecosystem collapse, helping resolve misinterpretation of red‐list criteria. Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems. Ampliación de Rangos de Distribución ante Amenazas para Predicciones Robustas de los Riesgos para la Biodiversidad Resumen La evaluación de los riesgos para la biodiversidad generalmente depende de la distribución espacial de las especies y los ecosistemas. Las medidas del rango de extensión, como el área de ocupación (ADO), que se utilizan ampliamente en estas valoraciones son sensibles a la escala de medición, lo que genera propuestas para medirlas a escalas más finas o a diferentes escalas con base en la forma de distribución o en las características ecológicas de la biota. A pesar de su papel dominante en las valoraciones de listas rojas durante décadas, las escalas espaciales apropiadas del ADO para predecir el riesgo de extinción de las especies o el colapso de un ecosistema sigue siendo polémico y sin ser probado. No hay evaluaciones cuantitativas de la sensibilidad de escala del ADO como pronóstico de los riesgos, la relación entre la escala óptima del ADO y la escala de la amenaza, o el efecto de incertidumbre de cuadrícula. Utilizamos modelos de simulación estocástica para explorar los riesgos para los ecosistemas y las especies con patrones de distribución agrupada, dispersa y linear sujetos a regímenes de eventos amenazantes con frecuencias y extensiones espaciales diferentes. El área de ocupación fue un pronosticador preciso del riesgo (0.81<|r|<0.98) y actuó óptimamente cuando se midió con celdas de cuadrícula de 0.1 –1.0 veces la mayor área plausible amenazada por un evento. Contrario a aseveraciones previas, los estimados del ADO a estas escalas relativamente burdas fueron mejores pronosticadores del riesgo que los estimados del ADO a escalas más finas (p. ej. cuando las celdas de medición son <1% del área de la mayor amenaza). La escala óptima dependió de las escalas espaciales de las amenazas más que de la forma o el tamaño de las distribuciones bióticas. Aunque encontramos un potencial apreciable para los errores de medida de celda, las pautas actuales de la UICN para la estimación del ADO neutralizan la incertidumbre geométrica e incorporan procedimientos efectivos de modificación de escala para la valoración de los riesgos presentados por las amenazas a escala de paisaje para las especies y los ecosistemas. 摘要 确定分布区大小受到胁迫的尺度以用于稳健的生物多样性风险预测 对生物多样性面临风险的评估通常依赖于物种和生态系统的空间分布。在这类评估中广泛使用的分布范围大小的度量, 例如占有面积 (AOO), 对测量尺度很敏感, 这提示我们应基于分布区形状或生物区生态特性在更精细的尺度或不同尺度上进行测量。尽管几十年来 AOO 在红色名录评估中占有关键地位, 然而用于预测物种灭绝风险或生态系统崩溃的 AOO 合适的空间尺度还未得到检验且尚存在争议。 AOO 作为风险预测指标的尺度敏感性、最适 AOO 尺度和胁迫尺度的关系, 以及网格不确定性的影响, 目前都没有进行定量评估。我们用随机模拟模型来探究生态系统和聚群分布、分散分布和线性分布的物种面临的风险, 同时考虑它们受到的不同频率和空间尺度的胁迫事件。占有面积是准确的风险预测指标 (0.81<|r|<0.98), 当在测量的网格单元大小为受胁迫影响的最大可能面积的 0.1‐1.0 倍时, 预测效果最好。与之前的认识相反, AOO 估计值在相对粗糙的尺度上比在精细尺度上(如测量单元大小<1%最大胁迫面积时)可以更好地预测风险。最优尺度更多地依赖于胁迫的空间尺度, 而不是生物分布区的形状或大小。尽管我们发现网格测量有很大潜在的误差, 但当前 IUCN 估计 AOO 的指导原则抵消了几何不确定性, 且整合了确定尺度的有效程序, 来评估景观尺度的胁迫对物种和生态系统产生的风险。【翻译: 胡怡思; 审校: 胡义波】 Article impact statement: Scaled area of occupancy is a good predictor of species extinction and ecosystem collapse, helping resolve misinterpretation of red‐list criteria. |
| Author | Akçakaya, H. Resit Murray, Nicholas J. Keith, David A. |
| Author_xml | – sequence: 1 givenname: David A. surname: Keith fullname: Keith, David A. – sequence: 2 givenname: H. Resit surname: Akçakaya fullname: Akçakaya, H. Resit – sequence: 3 givenname: Nicholas J. surname: Murray fullname: Murray, Nicholas J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28703324$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkc1PGzEQxa0KVALtpXcqS72gSqH-Wq99pBFQpKgcmrvl9drBYbNObW-r8NfjkIQDQjCXufze08x7x-CgD70F4AtG57jMDxMaf46JFOIDGOGK0DGuqTwAIySEGAshyRE4TmmBEJIVZh_BERE1opSwEfj9x-jO93MYdT-3MPkHm2AOMN9Fq3OCLkQYQzOkDFfRtt5kH_oEg4PRp_sntPGh9f9sTD6vP4FDp7tkP-_2CZhdXc4mv8bT2-ubycV0bBgrN7mas4pXiFmNBLGMON0irjlpDXWy5qh1lODGMM2YsY0jGteYSclcYyrC6Ak429quYvg72JTV0idju073NgxJEVRRIYgs6z0USywEqxDfoN9eoIswxL78UQxxzbnkiBTq644amqVt1Sr6pY5rtc-0AGgLmBhSitYp47Pe5Jaj9p3CSG1qU5va1FNtRfL9hWTv-iqMt_B_39n1G6Sa3P682WtOt5pFyiE-axiTdQmqoo_887BF |
| CitedBy_id | crossref_primary_10_1007_s10531_019_01697_9 crossref_primary_10_1016_j_pecon_2023_02_002 crossref_primary_10_1590_1676_0611_bn_2024_1648 crossref_primary_10_1016_j_biocon_2022_109561 crossref_primary_10_1071_BT22009 crossref_primary_10_1111_jbi_14679 crossref_primary_10_1111_geb_13091 crossref_primary_10_1016_j_pecon_2019_11_002 crossref_primary_10_1111_ecog_04143 crossref_primary_10_1111_1365_2664_14601 crossref_primary_10_1007_s12224_024_09449_6 crossref_primary_10_1073_pnas_1919580117 crossref_primary_10_1016_j_tree_2019_06_009 crossref_primary_10_1016_j_ocecoaman_2020_105416 crossref_primary_10_1111_njb_04202 crossref_primary_10_1111_cobi_13197 crossref_primary_10_1111_cobi_14069 crossref_primary_10_1111_aec_13037 crossref_primary_10_1002_ajb2_16437 crossref_primary_10_1016_j_tree_2021_12_002 crossref_primary_10_1016_j_biocon_2023_109937 crossref_primary_10_1111_ecog_03865 crossref_primary_10_3390_biology10010063 crossref_primary_10_1016_j_jnc_2022_126286 crossref_primary_10_1016_j_gecco_2023_e02541 crossref_primary_10_3390_f14112234 crossref_primary_10_1111_cobi_13139 crossref_primary_10_1111_cobi_13854 crossref_primary_10_1007_s10531_018_1555_5 crossref_primary_10_1007_s10531_023_02543_9 crossref_primary_10_3389_fmars_2020_614852 crossref_primary_10_1016_j_ecolmodel_2022_110013 |
| Cites_doi | 10.1016/j.biocon.2009.10.012 10.1126/science.aaf3565 10.1073/pnas.2235465100 10.1525/bio.2011.61.4.8 10.1111/1365-2435.12528 10.1111/j.1523-1739.2008.01158.x 10.1038/nature09440 10.1111/j.1523-1739.2008.01044.x 10.1016/j.jnc.2016.03.007 10.1371/journal.pone.0062111 10.1111/ddi.12533 10.1111/j.1523-1739.2003.00015.x 10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2 10.1111/ddi.12452 10.1111/conl.12167 10.1126/sciadv.1601367 10.1016/j.biocon.2011.06.020 10.1073/pnas.0907321106 10.1890/1051-0761(2002)012[0618:ATATOU]2.0.CO;2 10.2305/IUCN.CH.2016.RLE.1.es 10.1126/science.1149345 10.1111/j.1523-1739.2008.00937.x 10.1111/j.1365-2664.2008.01596.x 10.1016/j.tpb.2006.11.001 10.1016/j.biocon.2011.02.004 10.1111/j.0014-3820.2004.tb01713.x 10.1111/j.1466-8238.2011.00716.x 10.1016/S0006-3207(99)00194-9 |
| ContentType | Journal Article |
| Copyright | 2018 Society for Conservation Biology 2017 The Authors. published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. 2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. 2018, Society for Conservation Biology |
| Copyright_xml | – notice: 2018 Society for Conservation Biology – notice: 2017 The Authors. published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. – notice: 2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. – notice: 2018, Society for Conservation Biology |
| DBID | 24P AAYXX CITATION NPM 7QG 7SN 7SS 7ST 7U6 8FD C1K F1W FR3 H95 L.G P64 RC3 SOI 7X8 7S9 L.6 |
| DOI | 10.1111/cobi.12988 |
| DatabaseName | Wiley Online Library Open Access CrossRef PubMed Animal Behavior Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Environment Abstracts Sustainability Science Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources Aquatic Science & Fisheries Abstracts (ASFA) Professional Biotechnology and BioEngineering Abstracts Genetics Abstracts Environment Abstracts MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef PubMed Aquatic Science & Fisheries Abstracts (ASFA) Professional Technology Research Database Ecology Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Entomology Abstracts Genetics Abstracts Sustainability Science Abstracts Animal Behavior Abstracts ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources Environment Abstracts MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA PubMed MEDLINE - Academic Aquatic Science & Fisheries Abstracts (ASFA) Professional CrossRef |
| Database_xml | – sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology Ecology |
| EISSN | 1523-1739 |
| EndPage | 332 |
| ExternalDocumentID | 28703324 10_1111_cobi_12988 COBI12988 44973885 |
| Genre | article Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Australian Research Council funderid: LP130100435 – fundername: MAVA Foundation – fundername: National Science Foundation funderid: DEB‐1146198 |
| GroupedDBID | --- -DZ .3N .GA 05W 0R~ 10A 1OC 29F 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5HH 5LA 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHBH AAHKG AAHQN AAKGQ AAMMB AAMNL AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABBHK ABCQN ABCUV ABJNI ABLJU ABPLY ABPPZ ABPVW ABTLG ABXSQ ACAHQ ACCZN ACFBH ACGFO ACGFS ACNCT ACPOU ACPRK ACSTJ ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADUKH ADXAS ADZMN AEFGJ AEGXH AEIGN AEIMD AENEX AEUPB AEUYR AEYWJ AFAZZ AFBPY AFEBI AFFPM AFGKR AFRAH AFWVQ AFZJQ AGHNM AGXDD AGYGG AHBTC AIAGR AIDQK AIDYY AITYG AIURR AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ANHSF ATUGU AUFTA AZBYB AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 C45 CBGCD CS3 CUYZI D-E D-F D0L DCZOG DEVKO DPXWK DR2 DRFUL DRSTM DU5 EBS ECGQY EJD F00 F01 F04 F5P G-S G.N GODZA H.T H.X HGLYW HZI HZ~ IHE IPSME IX1 J0M JAAYA JBMMH JBS JEB JENOY JHFFW JKQEH JLS JLXEF JPM JST LATKE LC2 LC3 LEEKS LH4 LITHE LMP LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OES OIG OVD P2P P2W P2X P4D PQQKQ Q.N Q11 QB0 R.K ROL RSU RX1 SA0 SUPJJ TEORI TN5 UB1 UKR V8K W8V W99 WBKPD WIH WIK WNSPC WOHZO WQJ WXSBR WYISQ XG1 XSW YFH YUY YV5 YZZ ZCA ZO4 ZZTAW ~02 ~IA ~KM ~WT .-4 .Y3 1OB 24P 31~ 42X AAHHS AAISJ AANHP AAUTI ABEFU ABEML ACBWZ ACCFJ ACHIC ACPVT ACRPL ACSCC ACYXJ ADNMO ADULT ADZOD AEEZP AEQDE AEUQT AFPWT AHXOZ AI. AILXY AIWBW AJBDE AQVQM ASPBG AVWKF AZFZN CAG COF DOOOF ESX FEDTE GTFYD HF~ HGD HQ2 HTVGU HVGLF JSODD MVM NEJ QN7 SAMSI UQL VH1 VOH WHG WRC XIH ZCG AAYXX ABSQW ADXHL AGQPQ AGUYK CITATION NPM PKN 7QG 7SN 7SS 7ST 7U6 8FD C1K F1W FR3 H95 L.G P64 RC3 SOI 7X8 7S9 L.6 |
| ID | FETCH-LOGICAL-c4488-f76456504ea082e42fad06a62dc3f9760df321bc4a44cebf2a1714994fbc5243 |
| IEDL.DBID | DR2 |
| ISSN | 0888-8892 1523-1739 |
| IngestDate | Fri Jul 11 18:33:17 EDT 2025 Fri Jul 11 00:48:18 EDT 2025 Fri Jul 25 19:32:44 EDT 2025 Wed Feb 19 02:33:18 EST 2025 Tue Jul 01 02:25:28 EDT 2025 Thu Apr 24 23:04:18 EDT 2025 Wed Jan 22 16:28:59 EST 2025 Thu Jul 03 21:54:39 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | risk assessment species distribution threatening process landscape modeling IUCN Red List of Ecosystems Lista Roja de la UICN de Ecosistemas spatial scale valoración de riesgo IUCN Red List of Threatened Species proceso amenazante Lista Roja de la UICN de Especies Amenazadas distribución de especies 风险评估 ,IUCN 濒危物种红色名录, IUCN 生态系统红色名录,物种分布,胁迫过程,景观建模,空间尺度 escala espacial modelado de paisajes |
| Language | English |
| License | Attribution 2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4488-f76456504ea082e42fad06a62dc3f9760df321bc4a44cebf2a1714994fbc5243 |
| Notes | Scaled area of occupancy is a good predictor of species extinction and ecosystem collapse, helping resolve misinterpretation of red‐list criteria. Article impact statement ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-7627-4150 0000-0002-8679-5929 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcobi.12988 |
| PMID | 28703324 |
| PQID | 2017669602 |
| PQPubID | 36794 |
| PageCount | 11 |
| ParticipantIDs | proquest_miscellaneous_2053882905 proquest_miscellaneous_1918845065 proquest_journals_2017669602 pubmed_primary_28703324 crossref_citationtrail_10_1111_cobi_12988 crossref_primary_10_1111_cobi_12988 wiley_primary_10_1111_cobi_12988_COBI12988 jstor_primary_44973885 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20180401 April 2018 2018-04-00 |
| PublicationDateYYYYMMDD | 2018-04-01 |
| PublicationDate_xml | – month: 4 year: 2018 text: 20180401 day: 1 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Washington |
| PublicationTitle | Conservation biology |
| PublicationTitleAlternate | Conserv Biol |
| PublicationYear | 2018 |
| Publisher | Wiley Blackwell, Inc Blackwell Publishing Ltd |
| Publisher_xml | – name: Wiley Blackwell, Inc – name: Blackwell Publishing Ltd |
| References | 2009; 23 2009; 46 2002; 52 2002; 12 2010; 467 2011; 61 2017; 23 2016; 32 2000; 94 2010; 143 2007 2016; 30 1994 2003; 17 1993 2007; 72 2003 2013; 8 2015; 8 2016; 2 2001 2008; 319 2016; 352 2011; 21 2016 2008; 22 2003; 100 2011; 144 2016; 22 2009; 106 e_1_2_6_32_1 e_1_2_6_10_1 e_1_2_6_31_1 e_1_2_6_30_1 IUCN (International Union for Conservation of Nature) (e_1_2_6_15_1) 2016 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_35_1 e_1_2_6_11_1 e_1_2_6_34_1 e_1_2_6_12_1 e_1_2_6_33_1 e_1_2_6_18_1 IUCN (International Union for Conservation of Nature) Standards and Petitions Subcommittee (e_1_2_6_16_1) 2003 e_1_2_6_21_1 Burgman MA (e_1_2_6_5_1) 1993 e_1_2_6_20_1 IUCN (International Union for Conservation of Nature) Standards and Petitions Subcommittee (e_1_2_6_17_1) 2016 e_1_2_6_9_1 e_1_2_6_8_1 e_1_2_6_4_1 e_1_2_6_7_1 e_1_2_6_6_1 e_1_2_6_25_1 e_1_2_6_24_1 e_1_2_6_3_1 IUCN (International Union for Conservation of Nature) (e_1_2_6_14_1) 2001 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_22_1 e_1_2_6_29_1 e_1_2_6_28_1 e_1_2_6_27_1 e_1_2_6_26_1 |
| References_xml | – volume: 352 start-page: 416 year: 2016 end-page: 418 article-title: Filling in biodiversity threat gaps publication-title: Science – volume: 144 start-page: 2432 year: 2011 end-page: 2440 article-title: Adapting the IUCN Red List criteria for invertebrates publication-title: Biological Conservation – volume: 32 start-page: 1 year: 2016 end-page: 9 article-title: Habitats on the grid: the spatial dimension does matter for red‐listing publication-title: Journal for Nature Conservation – volume: 143 start-page: 311 year: 2010 end-page: 320 article-title: Large‐scale estimators of threatened freshwater catchment species relative to practical conservation management publication-title: Biological Conservation – volume: 17 start-page: 1559 year: 2003 end-page: 1570 article-title: Scale dependency of rarity, extinction risk, and conservation priority publication-title: Conservation Biology – year: 2007 – year: 2001 – volume: 8 start-page: e62111 year: 2013 article-title: Scientific foundations for an IUCN Red List of ecosystems publication-title: PLOS ONE – volume: 21 start-page: 732 year: 2011 end-page: 742 article-title: Understanding bias in geographic range size estimates publication-title: Global Ecology and Biogeography – volume: 52 start-page: 891 year: 2002 end-page: 904 article-title: The human footprint and the last of the wild publication-title: BioScience – year: 2003 – volume: 22 start-page: 881 year: 2016 end-page: 892 article-title: Putting susceptibility on the map to improve conservation planning, an example with terrestrial mammals publication-title: Diversity and Distributions – volume: 30 start-page: 70 year: 2016 end-page: 78 article-title: Facilitation and the niche: implications for coexistence, range shifts and ecosystem functioning publication-title: Functional Ecology – year: 2016 – volume: 8 start-page: 214 year: 2015 end-page: 226 article-title: The IUCN red list of ecosystems: motivations, challenges, and applications publication-title: Conservation Letters – year: 1994 – volume: 106 start-page: 19666 year: 2009 end-page: 19672 article-title: Size, shape, and the thermal niche of endotherms publication-title: Proceedings of the National Academy of Sciences – volume: 467 start-page: 555 year: 2010 end-page: 561 article-title: Global threats to human water security and river biodiversity publication-title: Nature – volume: 12 start-page: 618 year: 2002 end-page: 628 article-title: A taxonomy and treatment of uncertainty for ecology and conservation biology publication-title: Ecological Applications – volume: 61 start-page: 281 year: 2011 end-page: 289 article-title: The spatial distribution of threats to species in Australia publication-title: BioScience – volume: 72 start-page: 77 year: 2007 end-page: 85 article-title: How patch configuration affects the impact of disturbances on metapopulation persistence publication-title: Theoretical Population Biology – volume: 100 start-page: 12765 year: 2003 end-page: 12770 article-title: Biodiversity as spatial insurance in heterogeneous landscapes publication-title: Proceedings of the National Academy of Sciences – volume: 319 start-page: 948 year: 2008 end-page: 952 article-title: A global map of human impact on marine ecosystems publication-title: Science – volume: 22 start-page: 1424 year: 2008 end-page: 1442 article-title: Quantification of extinction risk: IUCN's system for classifying threatened species publication-title: Conservation Biology – volume: 2 start-page: e1601367 issue: 11 year: 2016 article-title: Incorporating explicit geospatial data shows more species at risk of extinction than the current red list publication-title: Science Advances – volume: 22 start-page: 897 year: 2008 end-page: 911 article-title: A standard lexicon for biodiversity conservation: Unified classifications of threats and actions publication-title: Conservation Biology – volume: 94 start-page: 311 year: 2000 end-page: 319 article-title: Sensitivity analyses of decision rules in world conservation union (IUCN) Red List criteria using Australian plants publication-title: Biological Conservation – volume: 23 start-page: 474 year: 2017 end-page: 483 article-title: The use of range size to assess risks to biodiversity from stochastic threats publication-title: Diversity and Distributions – year: 1993 – volume: 23 start-page: 259 year: 2009 end-page: 274 article-title: Assessing the threat status of ecological communities publication-title: Conservation Biology – volume: 144 start-page: 1585 year: 2011 end-page: 1594 article-title: Incorporating temporality and biophysical vulnerability to quantify the human spatial footprint on ecosystems publication-title: Biological Conservation – volume: 46 start-page: 1 year: 2009 end-page: 9 article-title: The sizes of species’ geographic ranges publication-title: Journal of Applied Ecology – ident: e_1_2_6_33_1 doi: 10.1016/j.biocon.2009.10.012 – volume-title: IUCN red list categories and criteria year: 2001 ident: e_1_2_6_14_1 – ident: e_1_2_6_18_1 doi: 10.1126/science.aaf3565 – ident: e_1_2_6_22_1 doi: 10.1073/pnas.2235465100 – ident: e_1_2_6_8_1 doi: 10.1525/bio.2011.61.4.8 – volume-title: An introduction to the IUCN Red List of ecosystems: the categories and criteria for assessing risks to ecosystems year: 2016 ident: e_1_2_6_15_1 – ident: e_1_2_6_4_1 doi: 10.1111/1365-2435.12528 – ident: e_1_2_6_25_1 doi: 10.1111/j.1523-1739.2008.01158.x – ident: e_1_2_6_34_1 doi: 10.1038/nature09440 – ident: e_1_2_6_23_1 doi: 10.1111/j.1523-1739.2008.01044.x – ident: e_1_2_6_11_1 doi: 10.1016/j.jnc.2016.03.007 – ident: e_1_2_6_21_1 doi: 10.1371/journal.pone.0062111 – ident: e_1_2_6_24_1 doi: 10.1111/ddi.12533 – volume-title: Risk assessment in conservation biology year: 1993 ident: e_1_2_6_5_1 – ident: e_1_2_6_13_1 doi: 10.1111/j.1523-1739.2003.00015.x – ident: e_1_2_6_31_1 doi: 10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2 – ident: e_1_2_6_27_1 doi: 10.1111/ddi.12452 – ident: e_1_2_6_20_1 doi: 10.1111/conl.12167 – ident: e_1_2_6_26_1 doi: 10.1126/sciadv.1601367 – ident: e_1_2_6_2_1 – ident: e_1_2_6_6_1 doi: 10.1016/j.biocon.2011.06.020 – volume-title: Guidelines for using the IUCN red list categories and criteria year: 2016 ident: e_1_2_6_17_1 – ident: e_1_2_6_28_1 doi: 10.1073/pnas.0907321106 – ident: e_1_2_6_29_1 doi: 10.1890/1051-0761(2002)012[0618:ATATOU]2.0.CO;2 – ident: e_1_2_6_3_1 doi: 10.2305/IUCN.CH.2016.RLE.1.es – ident: e_1_2_6_12_1 doi: 10.1126/science.1149345 – volume-title: Guidelines for using the IUCN red list categories and criteria year: 2003 ident: e_1_2_6_16_1 – ident: e_1_2_6_30_1 doi: 10.1111/j.1523-1739.2008.00937.x – ident: e_1_2_6_10_1 doi: 10.1111/j.1365-2664.2008.01596.x – ident: e_1_2_6_35_1 doi: 10.1016/j.tpb.2006.11.001 – ident: e_1_2_6_7_1 doi: 10.1016/j.biocon.2011.02.004 – ident: e_1_2_6_9_1 doi: 10.1111/j.0014-3820.2004.tb01713.x – ident: e_1_2_6_32_1 doi: 10.1111/j.1466-8238.2011.00716.x – ident: e_1_2_6_19_1 doi: 10.1016/S0006-3207(99)00194-9 |
| SSID | ssj0009514 |
| Score | 2.427661 |
| Snippet | Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments,... Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments,... |
| SourceID | proquest pubmed crossref wiley jstor |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 322 |
| SubjectTerms | Area Assessments Balances (scales) Biodiversity Biota Cells Computer simulation Dispersion distribución de especies Distribution Distribution patterns Ecosystems Environment models escala espacial extinction Frequency dependence geometry guidelines IUCN Red List of Ecosystems IUCN Red List of Threatened Species landscape modeling Lista Roja de la UICN de Ecosistemas Lista Roja de la UICN de Especies Amenazadas Mathematical models Measurement modelado de paisajes neutralization Optimization prediction Predictions Procedures proceso amenazante quantitative analysis Risk Risk assessment Scaling Sensitivity analysis Shape simulation models Spatial analysis Spatial distribution spatial scale species distribution Species extinction Threatened species threatening process Threats Uncertainty valoración de riesgo 风险评估 ,IUCN 濒危物种红色名录, IUCN 生态系统红色名录,物种分布,胁迫过程,景观建模,空间尺度 |
| Title | Scaling range sizes to threats for robust predictions of risks to biodiversity |
| URI | https://www.jstor.org/stable/44973885 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcobi.12988 https://www.ncbi.nlm.nih.gov/pubmed/28703324 https://www.proquest.com/docview/2017669602 https://www.proquest.com/docview/1918845065 https://www.proquest.com/docview/2053882905 |
| Volume | 32 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS9xAEB9UKPTFaq2a-sEW-9JCjrvdTbIHfWlFsQW1tBZ8KSG72cVDucgl96B_fWd2k1SLCvUtkAnM7szs_CYzOwPwXuO5qEs7jGnmdiy1KGPFiyS26D0t4ltt_Dif45P06Jf8dp6cL8Cn7i5M6A_R_3Ajy_DnNRl4oes7Rm4qPRmgt1J005eKtQgR_eB3Ou6Gxt4Y4sVKjXnbm5TKeP5-es8bhYLEh6DmfeTqXc_hK_jdMR0qTi4H80YPzO0__Ryfu6oVWG4xKfsclGgVFuz0NbwIUypv8OnAd7a-WYOTnyhR9HVsRlcSWD25tTVrKtZcEPSsGQJgNqv0vG7Y9YwyQF6pWeUYVbB7Uj2pyq4U5A2cHR6c7R_F7UCG2GAUp2KXpQQAh9IWiBys5K4oh2mR8tIIh7hmWDrBR9rIQkpjteMFjVcfj6XTJuFSrMPStJraTWC4PqesyVypMMIUXJca3WKmjEisGOk0gg-dXHLTNiunmRlXeRe00EblfqMi2Otpr0OLjgep1r14exIpx5lQKolgu5N33tpvnSMsytIUozsewbv-NVoepVOKqa3mdY6RLjKfIIZ7nIbjGacoV400G0GXegYoxSwQz0bw0WvEE8zn-6dfvvqnt_9DvAUvcSEqFBptw1Izm9sdxFCN3oVFLr_veov5AwsWFjk |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VIkQvPAql6QNcwQWkrLa2k3iP0Ie2r0WCReotih1brFptqk320P56ZuxsaFGpBDdLmUi2x-P5xh5_A_BB476oS9uPqeZ2LLUoY8WLJLboPS3iW218OZ-zUTr8IY_Pk_M2N4fewgR-iO7AjSzD79dk4HQgfcvKTaUnPXRXSj2Cx1TSm6jz97_xW5y7gdobg7xYqQFv2Ukpkef3v3f8UUhJvA9s3sWu3vkcPg8VVmvPWUg5Jxe9eaN75uYPRsf_HtcLeNbCUvY5rKOXsGSnq_AkFKq8xtaBJ7e-fgWj76hUdHdsRq8SWD25sTVrKtb8JPRZM8TAbFbped2wqxldAvl1zSrHKIndi-pJVS6yQV7D-PBgvDeM25oMscFATsUuSwkD9qUtEDxYyV1R9tMi5aURDqFNv3SC72ojCymN1Y4XVGF9MJBOm4RLsQbL02pq14Hh-JyyJnOlwiBTcF1q9IyZMiKxYlenEXxcKCY3LV85lc24zBdxC01U7icqgved7FVg6bhXas3rtxORcpAJpZIIthYKz1sTrnNERlmaYoDHI9jpPqPx0Y1KMbXVvM4x2MXOJwjj_i7DcZtTdF2NMm_CYuo6QLfMAiFtBJ_8knig8_ne1y9HvrXxL8Lv4OlwfHaanx6NTjZhBQelQt7RFiw3s7ndRkjV6LfecH4B0zMZfg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9RAEB_aSosvorbV6Kkr9aVCyt3uJtkDX_Ts0fpxFlqhbyH7hQdyOS65h_rXO7P5aAut4NtCJrCZ2Zn5TWZ2BuCdRruorRvGNHM7llrYWPEiiR16T4f4Vpswzuf7LD35Kb9cJpcb8KG7C9P0h-h_uJFmBHtNCr60_oaSm1LPj9BbKbUJDyjbRwVdXJ7daLnbdPbGGC9Waszb5qRUx3P97i131FQk3oU1b0PX4Humj-FRCxrZx0bKT2DDLZ7CdjNG8gpXx6H19NUuzM6R5eiM2IruDLBq_sdVrC5Z_YuwYcUQobJVqddVzZYrStGEU8dKz6jEPJDqeWm7Wo09uJgeX0xO4nZiQmwwzFKxz1JCaEPpCnTtTnJf2GFapNwa4RF4DK0XfKSNLKQ0Tnte0Pzz8Vh6bRIuxT5sLcqFew4MWeOVM5m3CkNAwbXV6LcyZUTixEinERx2fMtN202chlr8zruognicBx5HcNDTLpseGndS7Qf29yRSjjOhVBLBoJNH3ipYlSNuydIUwy8ewdv-MaoG5TuKhSvXVY6hKG4-QZB1Pw1HI6QomYw0zxpZ9xugHLBAwBnB-yD8f2w-n_z4dBpWL_6H-A3snH2e5t9OZ19fwkP8JtUUBQ1gq16t3SvEO7V-HY71Xxg194I |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Scaling+range+sizes+to+threats+for+robust+predictions+of+risks+to+biodiversity&rft.jtitle=Conservation+biology&rft.au=Keith%2C+David+A.&rft.au=Ak%C3%A7akaya%2C+H.+Resit&rft.au=Murray%2C+Nicholas+J.&rft.date=2018-04-01&rft.issn=0888-8892&rft.eissn=1523-1739&rft.volume=32&rft.issue=2&rft.spage=322&rft.epage=332&rft_id=info:doi/10.1111%2Fcobi.12988&rft.externalDBID=10.1111%252Fcobi.12988&rft.externalDocID=COBI12988 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0888-8892&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0888-8892&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0888-8892&client=summon |