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We evaluated the consequences of parameter errors for predictions of spatially explicit population models. We examined a simple model for organisms dispersing in a fragmented landscape and assessed how errors in three model input parameters propagate into errors in model predictions: (1) misclassification of habitat suitability (landscape error); (2) incorrect estimation of how far a disperser can travel (mobility error); and (3) incorrect estimation of the mortality rate of dispersers (dispersal‐mortality error). The two‐dimensional landscape through which organisms dispersed was filled with patches of various shapes (square, linear, and elbow) and sizes (4, 9, and 16 cells), and we allowed the overall proportion of suitable habitat in the landscape (2, 8, 16, and 24%) to vary among runs. A single run consisted of 400 individuals dispersing through the landscape until they found suitable habitat patches, and the output was a frequency distribution of the number of steps taken before a patch was found (n = 400 individuals). In the error‐free model, dispersal success increased with the percentage of the landscape that was composed of suitable habitat and was greater in landscapes filled with more small patches than in those with fewer large patches. Errors in dispersal‐mortality parameters resulted in the greatest prediction errors (25–90%), followed by mobility errors (2–60%) and landscape errors (<1–17%). In general, prediction errors were higher in landscapes with a lower percentage of suitable habitat, precisely the type of habitat characterizing most species of conservation concern. Our results point to the need for better empirical estimates of errors in dispersal parameters. In addition, our results suggest that less detailed models would improve the match between the complexity of the model and the quality of available data. Valoración de los Requerimientos de Datos para Modelos de Dispersión Espacialmente Explícita En este trabajo evaluamos las consecuencias de errores en los parámetros de las predicciones de modelos poblacionales con estructura espacial explícita. Construímos un modelo poblacional en el cual los organismos se dispersan en un ambiente fragmentado y evaluamos la propagación de errores en los parámetros usados en las predicciones que resultan del modelo. Nuestras simulaciones exploran las consecuencias de tres tipos de errores: (1) clasificación errónea del ambiente adecuado para la especie (error de “paisaje”); (2) estimación incorrecta de la distancia que puede viajar un organismo (error de movilidad); y (3) estimación incorrecta de la tasa de mortalidad de los organismos que se dispersan. El ambiente bi‐dimensional a través del cual los organismos se dispersan es ocupado por parches de distintas formas (cuadrados, lineales y acotados) y tamaños (4, 9 y 16 celdas); variamos además la proporción total de la superficie de ambiente adecuado (2, 8, 16, y 24%). Cada corrida del modelo consistió en 400 individuos dispersandose a través del ambiente hasta encontrar un parche adecuado. La distribución de frecuencias del número de pasos tomados antes de encontrar un parche apropiado (n = 400 individuos) fue evaluada para cada combinación de valores de los parámetros. El modelo libre de errores produjo simulaciones donde el éxito de dispersión aumentó con la proporción de ambiente adecuado y también en ambientes con muchos parches chicos comparado con aquellos con pocos parches grandes. Errores en la mortalidad de los individuos que se dispersan resultaron en los mayores errores de predicción (25–90%), seguido de errores en la movilidad de los individuos (2–60%) y errores en la calidad del hábitat (1–17%) en términos relativos. En general, los errores de predicción fueron mayores en ambientes con bajo porcentaje de hábitat adecuado, precisamente el tipo de ambiente característico de la mayoría de especies que concieren a los esfuerzos de conservación. Nuestros resultados señalan la importancia de la obtención de medidas empíricas de los errores asociados a los parámetros de dispersión y sugieren la necesidad de utilizar estrategias alternativas de modelado para predecir el destino de poblaciones que habian ambientes fragmentados.
Conservation Biology – Wiley
Published: Dec 2, 1997
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